Galaxies

galaxies

This paper is available on arxiv under CC 4.0 license. Authors: (1) Igor Yu. Potemine, Institut de Math´ematiques, Universit´e Paul Sabatier. Table of Links Abstract and Introduction 5-dimensional Einstein Equations Nested Gogberashvili Models Harmony of Transcosmic Spheres Sequence of Event Horizons Time-amalgamated Multiverses Dark Energy and Multiversal Nurseries Black Holes as Multiverses Multi-solitonic and Gyratonic Gravitational Waves Elliptic and Spiral Galaxies Cosmological Coupling Conclusion and References 10 Elliptic and spiral galaxies It seems that it partially explains the classification of galaxies, harboring supermassive black holes at their cores. Indeed, on the one hand, supermassive black holes of multi-solitonic type could generate elliptic galaxies. On the other hand, multi-gyratonic black holes could generate spiral galaxies. The number of spirals and their configuration is related in this case to the number of multiversal layers and the harmony of the spheres (cf. sections 4 and 9). Relativistic jets, emitting by supermassive black holes at galaxy cores, also suggest their gyratonic nature.

Hyperverse, 5-dimensional Gravity and Multiverses: Elliptic and Spiral Galaxies

Space Research Meets The Metaverse Who isn’t aware of the Martian phenomenon of Life in today's high-tech world? Life on Mars is no longer a fantasy but a distant reality. Humans are all set to colonize other planets through their futuristic technological advances. And with technical wonders like the Metaverse, IoT, Artificial Intelligence, and machine learning, the research scale will only broaden. The Metaverse can dwell with almost all industries and has many benefits for each one. However, the Metaverse has outlawed most technologies in the field of research. With the Metaverse, the education and research sector has upscaled its business by creating simulations that assist experiments. The Metaverse also has given outstanding results in Space and Medical research. One can easily conduct a with the right tech stack. Metaverse Virtual Planet Trip Through immersive experiences, the researchers can conduct experiments with the naked eye and also be a part of them. According to CNBC, this industry will reach around a trillion dollars in 2040, with the launch cost estimations dropping by 95%. Defining The Metaverse The Metaverse is a virtual world created with an advanced tech stack that offers an overall immersive experience to the users. The Metaverse can provide not just an immersive experience through virtual gears but can also be a medium for marketing brands in an innovative way. And with the Metaverse, the Web3 space is leading the race of decentralization. Decentralization, as the word suggests, means removing the control of a third-party monitoring the transactions. The Metaverse falls under the umbrella of the Web3 space and facilitates its cutting-edge technologies. by users in the form of cryptocurrency that can later be converted into real money via exchange wallets. Metaverse platforms accept payments With a lot of technical craze in the current advanced world, the Metaverse is always fresh in the talks. How is Metaverse Opening New Frontiers For Space Researchers? Many of us are already aware of the experiments carried out with VR headsets, where individuals spent days living in mixed reality. This VR headset that serves as a medium to enter the Metaverse can also be a tool for carrying out various space experiments. Not just that, game engines can also help create virtual worlds that will fuel scientific studies by creating digital twins of situations the astronauts face in Space. Now let us understand the scope of Space research education In Metaverse by the following: Creating A Digital Twin A replicates a physical concept using highly technological solutions. A digital twin is created by the data collected by the IoT, where sensors are fit on physical objects and data is collected. For example, when creating a digital twin of a particular aircraft or satellite, the sensors on them record accurate data to make the digital twin as real as possible. digital twin Focusing on Physical Fitness The Metaverse can also effectively monitor the astronauts' mental well-being and physical fitness. The Houston-based Metaverse Development Company, Z3VR, is already in the run to develop a technology that will optimize the astronauts' physical fitness. This will be executed by developing a VR-based game that can portray the long-term biological results of a human body in space. Metaverse runs on the power of Blockchain Technology, with it, the astronauts' biometric data can be analyzed at a larger scale. This Houston-based company will work on making exercises an engaging activity for the astronauts with mobile sensors and other fresh VR Tech. A Medium To Monitor The Mental Well-Being Of The Astronauts During long space missions, it is quite easy for Astronauts to feel detached from the physical world. To deal with this, health professionals are finding ways for how the Metaverse can help. They have come up with a mixed reality experience to create virtual areas that will support the mental well-being of the astronauts. Luckily, many institutions like Simon Fraser University's iSpace Lab, are already using the Metaverse to do so. Many studies, like the SIRIUS International Isolation in 2021, were conducted to simulate an eight-month mission in an isolated space. The Metaverse will surely facilitate the Space Industry to be fully prepared for a Space mission before it launches. Virtual Space Experiments in Metaverse Thousands of experiments are conducted in the International Space Station, ranging from testing drugs to analyzing plant growth. With the correct use of Metaverse in the picture, these experiments can be performed virtually. The virtual tours of planets conducted both in and inside our solar system can easily facilitate in-depth analysis of the wonders of Space. Virtual Tours and Missions: Creating your Own Space-Themed Virtual World The International Space University has recently decided to enter the Metaverse to conduct virtual experiments in Space. ISU will partner with Metavisionaries to create a “Metaship.” This will be used for delivering lectures on interesting topics like the effects of unique space environments on the human body. Space researchers can leverage the Metaverse to create virtual tours of planets to train astronauts under the extreme conditions prevalent in space. With a virtual experience developed by a team of Metaverse developers, research can also be carried out easily, giving people a glimpse of the galaxies beyond our planet Earth. Are you a Space Enthusiast? Launch Your Own Metaverse Space Tour through the right Metaverse Development Company and explore Space from the comfort of your couch. relies on listing private vendors to build spacecraft and other types of equipment. Along with that, NASA also provides Exoplanet Excursions to explore Star Systems like the TRAPPIST-1 Star System. The immersive 7-minute virtual tour will give a 360-degree tour on Youtube. Another virtual tour is the International Space Station, which takes users on an immersive tour of the Space Crew's sleeping quarters and Hygiene Systems. NASA's Commercial Crew program https://youtu.be/SUYuxZSaeH0?list=PLTUZypZ67cdumL1V4yFWlfoxwjb3rDCzb?embedable=true Tech Stack Used In Creating A Space Themed Virtual World Technologies Ruling The Metaverse Let us go through some of the unique technologies that are a part of the Metaverse: Blockchain Technology Blockchain technology is a recent cutting-edge technology that keeps data safe and prevents third-party intervention at all costs. This technology works on storing data on distributed ledges with a backup of a thousand computer systems. This technology keeps user data secured and facilitates peer-to-peer payments in seconds. Cryptocurrency As The Mode of Exchange The Metaverse outlaws fiat modes of payments and works with tokens that hold a certain value in terms of real money and cryptocurrencies. Cryptocurrencies through exchange wallets can revert the payment in fiat money to the user. Cryptocurrencies are a robust solution to ease seamless payments without third-party interference. AI With AI (Artificial Intelligence), it is easier to picture the functioning of the Metaverse. Artificial Intelligence can perceive, synthesize and process information that normally is done by the human mind. With artificial Intelligence, computer systems can easily process a great deal of information in a fraction of a second with great accuracy. VR Virtual reality means creating virtual experiences through digital twins and simulations through VR Headsets. This is where the immersive experience of one actually being completely engulfed in the Metaverse occurs. Through virtual reality, humans have strongly created multiple Metaverses that have the ability to merge the digital and physical worlds and create a mixed world. In this space, the physical world will run parallel with the digital world, enabling users to live multiple lives simultaneously. IoT The Internet of Things will assist users in building, testing, and deploying any sort of customizable IoT application. IoT also involves interconnecting physical things with the environment and living things for ideal and effective data sharing. Simulations Through The Unreal Engine: Using Epic Games to Learn Virtual Reality Software As explained earlier, the concepts of digital twins and simulations replicate real-life situations digitally. The actual usage of the Metaverse in the space industry comes with simulations that are actively used for testing products, processes, and systems. Simulations are made on software applications with advanced computer designs. The models can both be created in a 3D or 2D space with mathematical concepts. The simulations will work by introducing a variety of variables inside the digital space that will assess suitable outcomes. The simulations to conduct virtual experiments in the Metaverse can be deployed by the Unreal Engine to various platforms with the smallest details accurately covered. The Pixel Streaming available in the can easily assist in delivering high-performance activities. The display system inherited by the Unreal Engine can render real-time information at a large scale while displaying that information on big LED screens. Epic Games Unreal Engine The Ideal Martian Metaverse: Benefits Of Metaverse For Space Researchers Reduced costs The Metaverse is a technological gift that offers a variety of benefits, like reduced costs to train personnel in virtual spaces rather than at a physical place. The cost is limited to creating a virtual world and equipment rather than traveling to a place to carry out those operations manually. For training astronauts in a digital zero-gravity vacuum before an actual space mission, costs can be highly reduced through the aid of the Metaverse. Data-Driven Decision Making The decision-making can yield successful results with the application of Metaverse tool sets that can analyze and detect problems before it occurs. Data-driven decision-making will always give better results than manual ones and decrease accidents. Especially when talking about the Space Industry, one slight error can fail the whole mission. This will result in not just the mission failing but also the loss of lives. Optimized Outcomes The key benefit of data-driven decision-making is that it can help researchers optimize the outcomes. Integrating metaverse in the training of astronauts or analyzing a Space Mission helps in a better understanding of the results before anything is done in the physical world. The accuracy of these results is perfect enough to train and alter any required information and optimize the outcomes. Wrapping Up The Metaverse welcomes every possible industry in its realms. With the space industry, the Metaverse can easily open new doors in research that will be efficient enough to yield cost-effective results. The astronauts rely on AR/VR for a variety of tasks already. As we know, even the strongest humans sometimes need a helping hand. And the astronauts are not any different. For them, the helping hands come in the form of other astronauts and, to a large extent, on the Metaverse. With AR/VR, several tasks are carried out in the International Space Station. The current use of  AR by the astronauts through the VR headset, “sidekick,” consists of HD holograms instructing a variety of tasks and showcasing them as 3D objects. Thus the Metaverse has already touched the Space Industry, and with it, the researchers are leveraging data like never before. More information on this project here.

Merging The Galaxies With The Metaverse

There’s something about the cosmic perspective, which for some people is enlightening and for other people is terrifying. For those who are terrified by it, they’re here on Earth and they have a certain self-identity. And then they learn that the Earth is tiny and we’re in this void of interplanetary space. There’s a star we call the sun and there are a hundred billion other stars in our galaxy. And our galaxy ‘The Milky Way’ is one of the the 50-100 billion other galaxies in the universe. And with every step, with every window modern astrophysics has opened to our mind, the person that wants to feel like they’re the center of everything, ends up shrinking. Each dot you see is a Galaxy. This is the clearest and the widest picture ever taken by the Hubble space telescope to date. For some people they actually might find it depressing. I assert, that if you were depressed after learning and being exposed to the cosmic perspective, you started your day with an unjustifiably large ego. You thought more highly of yourself than in fact what you deserved. So here’s what you do. You say to yourself… "I have no ego at all." No ego. No cause to puff myself up. Now let’s learn about the cosmic perspective. We’re on a planet that’s orbiting a star. And the star is an energy source and it’s giving us this energy. We’re feeling this energy and life is enabled by this energy in this star. And by the way there are 100 billion other stars that have other planets. There might be other life out there. Life there could be like us. Probably not like us. Whatever it is, it would be fascinating to find out who it is. Can we talk to them? Can we not? Are they more advanced? Are they less advanced? By the way the atoms of your body are comparable to what stars do. And all you can do is bask in the relevance of the cosmos. So those who view cosmic perspective as a depressing outlook, they really need to re-assess how they think about the world. Because when I look up in the Universe i know I'm small but I’m also big. I’m big because I’m connected to the universe, and the universe is connected to me”. Nobody Knows what lies beyond our observable universe. But it is Estimated that the Entire Un-observable universe is larger than our observable universe. Let that sink in for a second. 150 sextillion times Our Observable universe might also be a tiny dot in the more bigger universe that we are never ever going to explore. Finally, we are unimaginably small. We have so much more to learn, explore and discover. The only question is where to even start ...

The Cosmos and Your Ego

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Hyerin Cho (조혜린), Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA;
(2) Ben S. Prather, CCS-2, Los Alamos National Laboratory, PO Box 1663, Los Alamos, NM 87545, USA;
(3) Ramesh Narayan, enter for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA;
(4) Priyamvada Natarajan, Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA, Department of Astronomy, Yale University, Kline Tower, 266 Whitney Avenue, New Haven, CT 06511, USA and Department of Physics, Yale University, P.O. Box 208121, New Haven, CT 06520, USA;
(5) Kung-Yi Su, Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA;
(6) Angelo Ricarte, Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA;
(7) Koushik Chatterjee, Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA. Table of Links Abstract and Introduction Numerical Methods Hydrodynamic Bondi Accretion Magnetized Bondi Accretion Feedback Via Reconnection-Driven Convection Summary and Conclusions Acknowledgements Appendix A. GRMHD Primer and Definitions B. Numerical Set-up C. Resolution and Initial Condition Study References REFERENCES Angl´es-Alc´azar, D., Quataert, E., Hopkins, P. F., et al. 2021, ApJ, 917, 53, doi: 10.3847/1538-4357/ac09e8 Anile, A. M. 1989, Relativistic fluids and magneto-fluids: With applications in astrophysics and plasma physics Bisnovatyi-Kogan, G. S., & Ruzmaikin, A. A. 1974, Ap&SS, 28, 45, doi: 10.1007/BF00642237 Bondi, H. 1952, MNRAS, 112, 195, doi: 10.1093/mnras/112.2.195 Booth, C. 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Prather, CCS-2, Los Alamos National Laboratory, PO Box 1663, Los Alamos, NM 87545, USA; (3) Ramesh Narayan, enter for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA; (4) Priyamvada Natarajan, Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA, Department of Astronomy, Yale University, Kline Tower, 266 Whitney Avenue, New Haven, CT 06511, USA and Department of Physics, Yale University, P.O. Box 208121, New Haven, CT 06520, USA; (5) Kung-Yi Su, Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA; (6) Angelo Ricarte, Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA; (7) Koushik Chatterjee, Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA. This paper is available on arxiv under CC 4.0 license. Authors : Authors (1) Hyerin Cho (조혜린), Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA; (2) Ben S. Prather, CCS-2, Los Alamos National Laboratory, PO Box 1663, Los Alamos, NM 87545, USA; (3) Ramesh Narayan, enter for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA; (4) Priyamvada Natarajan, Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA, Department of Astronomy, Yale University, Kline Tower, 266 Whitney Avenue, New Haven, CT 06511, USA and Department of Physics, Yale University, P.O. Box 208121, New Haven, CT 06520, USA; (5) Kung-Yi Su, Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA; (6) Angelo Ricarte, Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA; (7) Koushik Chatterjee, Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA. Table of Links Abstract and Introduction Abstract and Introduction Numerical Methods Numerical Methods Hydrodynamic Bondi Accretion Hydrodynamic Bondi Accretion Magnetized Bondi Accretion Magnetized Bondi Accretion Feedback Via Reconnection-Driven Convection Feedback Via Reconnection-Driven Convection Summary and Conclusions Summary and Conclusions Acknowledgements Acknowledgements Appendix Appendix A. GRMHD Primer and Definitions A. GRMHD Primer and Definitions B. Numerical Set-up B. Numerical Set-up C. Resolution and Initial Condition Study C. Resolution and Initial Condition Study References References REFERENCES Angl´es-Alc´azar, D., Quataert, E., Hopkins, P. F., et al. 2021, ApJ, 917, 53, doi: 10.3847/1538-4357/ac09e8 Anile, A. M. 1989, Relativistic fluids and magneto-fluids: With applications in astrophysics and plasma physics Bisnovatyi-Kogan, G. S., & Ruzmaikin, A. 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All the References That We Used in Our Black Hole Study So You Can Continue to Learn

Identifying Axion Conversion in Compact Star Magnetospheres With Radio-Wave Polarization Signatures

Adaptive Graph Neural Networks for Cosmological Data Generalization: Acknowledgements and Disclosure

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Andrea Roncoli, Department of Computer, Science (University of Pisa);
(2) Aleksandra Ciprijanovi´c´, Computational Science and AI Directorate (Fermi National Accelerator Laboratory) and Department of Astronomy and Astrophysics (University of Chicago);
(3) Maggie Voetberg, Computational Science and AI Directorate, (Fermi National Accelerator Laboratory);
(4) Francisco Villaescusa-Navarro, Center for Computational Astrophysics (Flatiron Institute);
(5) Brian Nord, Computational Science and AI Directorate, Fermi National Accelerator Laboratory, Department of Astronomy and Astrophysics (University of Chicago) and Kavli Institute for Cosmological Physics (University of Chicago). Table of Links Abstract and Intro Data and Methods Results Conclusions Acknowledgments and Disclosure of Funding, and References Additional Plots Abstract 1 Introduction Accurate determination of cosmological parameters using big data from astronomical surveys is a task of paramount importance in modern science. Historically, the extraction of valuable cosmological information has relied on computing summary statistics [31, 16, 15]. More recently, deep learning methods, such as 2D and 3D Convolutional Neural Networks (CNNs), showed great promise in extracting rich non-linear information that summary statistics struggle to capture [32, 39, 29]. However, CNNs lack scale-invariance, as their analysis is firmly anchored to the grid size of the convolutional kernels, while any information on scales below that is lost. Choosing a superfine grid to avoid information loss, though, would simply yield almost entirely zeros in case of sparse and irregular data, such as galaxy clusterings. Thus, CNNs result in an inadequate method for structured sparse data. In contrast, Graph Neural Networks (GNNs) [23, 4, 49, 46] can handle structured cosmic web data in a scale-free manner [41, 14]. As with any other model, the typical procedure is to train GNNs on labeled data (like simulations) and then infer cosmological parameters from unlabeled data (like observations). However, there is a significant risk of these models not generalizing in the presence of the domain shift between simulations and observations. Systematic biases have been demonstrated even in experiments that train and test on simulations with different subgrid physics [41]. Domain adaptation (DA) techniques [11, 43, 18, 27] can be used to increase model robustness to this type of domain shift. Here we propose the use of Domain Adaptive Graph Neural Networks (DA-GNNs) and investigate the utility of distance-based DA losses i.e., Maximum Mean Discrepancy (MMD) [6]. MMD is an unsupervised DA technique because it does not require labeled data, which is paramount for future applications on observations. We show that our domain-adaptive models achieve stronger generalization across datasets than regular GNN models. Our work is a significant step towards building future models trained on simulations, yet robust enough to work on observational data. Related Work GNNs have shown great potential for extracting information from large sparse datasets, such as the distribution of galaxies, galaxy clusters, and cosmic large-scale structures [25, 28, 41, 33, 42, 14]. Unfortunately, due to the complexity of most deep learning models, they often learn dataset-specific features, which renders them useless when testing on a different dataset (different simulations or astronomical observations). In astronomy, it has been shown that DA techniques applied to different types of CNNs can substantially improve model performance in cross-dataset applications [8, 10, 9, 37, 21, 2]. Recently, it has been shown that DA can be used on other types of deep learning algorithms such as GNNs [12, 24, 45, 47, 7, 44, 17]. However, DA on GNNs has not been used for any astrophysics or cosmology applications. This paper is available on arxiv under CC 4.0 license. Authors: (1) Andrea Roncoli, Department of Computer, Science (University of Pisa); (2) Aleksandra Ciprijanovi´c´, Computational Science and AI Directorate (Fermi National Accelerator Laboratory) and Department of Astronomy and Astrophysics (University of Chicago); (3) Maggie Voetberg, Computational Science and AI Directorate, (Fermi National Accelerator Laboratory); (4) Francisco Villaescusa-Navarro, Center for Computational Astrophysics (Flatiron Institute); (5) Brian Nord, Computational Science and AI Directorate, Fermi National Accelerator Laboratory, Department of Astronomy and Astrophysics (University of Chicago) and Kavli Institute for Cosmological Physics (University of Chicago). This paper is available on arxiv under CC 4.0 license. available on arxiv Authors: Authors: (1) Andrea Roncoli, Department of Computer, Science (University of Pisa); (2) Aleksandra Ciprijanovi´c´, Computational Science and AI Directorate (Fermi National Accelerator Laboratory) and Department of Astronomy and Astrophysics (University of Chicago); (3) Maggie Voetberg, Computational Science and AI Directorate, (Fermi National Accelerator Laboratory); (4) Francisco Villaescusa-Navarro, Center for Computational Astrophysics (Flatiron Institute); (5) Brian Nord, Computational Science and AI Directorate, Fermi National Accelerator Laboratory, Department of Astronomy and Astrophysics (University of Chicago) and Kavli Institute for Cosmological Physics (University of Chicago). Table of Links Abstract and Intro Abstract and Intro Data and Methods Data and Methods Results Results Conclusions Conclusions Acknowledgments and Disclosure of Funding, and References Acknowledgments and Disclosure of Funding, and References Additional Plots Additional Plots Abstract 1 Introduction Accurate determination of cosmological parameters using big data from astronomical surveys is a task of paramount importance in modern science. Historically, the extraction of valuable cosmological information has relied on computing summary statistics [31, 16, 15]. More recently, deep learning methods, such as 2D and 3D Convolutional Neural Networks (CNNs), showed great promise in extracting rich non-linear information that summary statistics struggle to capture [32, 39, 29]. However, CNNs lack scale-invariance, as their analysis is firmly anchored to the grid size of the convolutional kernels, while any information on scales below that is lost. Choosing a superfine grid to avoid information loss, though, would simply yield almost entirely zeros in case of sparse and irregular data, such as galaxy clusterings. Thus, CNNs result in an inadequate method for structured sparse data. In contrast, Graph Neural Networks (GNNs) [23, 4, 49, 46] can handle structured cosmic web data in a scale-free manner [41, 14]. As with any other model, the typical procedure is to train GNNs on labeled data (like simulations) and then infer cosmological parameters from unlabeled data (like observations). However, there is a significant risk of these models not generalizing in the presence of the domain shift between simulations and observations. Systematic biases have been demonstrated even in experiments that train and test on simulations with different subgrid physics [41]. Domain adaptation (DA) techniques [11, 43, 18, 27] can be used to increase model robustness to this type of domain shift. Here we propose the use of Domain Adaptive Graph Neural Networks (DA-GNNs) and investigate the utility of distance-based DA losses i.e., Maximum Mean Discrepancy (MMD) [6]. MMD is an unsupervised DA technique because it does not require labeled data, which is paramount for future applications on observations. We show that our domain-adaptive models achieve stronger generalization across datasets than regular GNN models. Our work is a significant step towards building future models trained on simulations, yet robust enough to work on observational data. Related Work GNNs have shown great potential for extracting information from large sparse datasets, such as the distribution of galaxies, galaxy clusters, and cosmic large-scale structures [25, 28, 41, 33, 42, 14]. Unfortunately, due to the complexity of most deep learning models, they often learn dataset-specific features, which renders them useless when testing on a different dataset (different simulations or astronomical observations). In astronomy, it has been shown that DA techniques applied to different types of CNNs can substantially improve model performance in cross-dataset applications [8, 10, 9, 37, 21, 2]. Recently, it has been shown that DA can be used on other types of deep learning algorithms such as GNNs [12, 24, 45, 47, 7, 44, 17]. However, DA on GNNs has not been used for any astrophysics or cosmology applications. Related Work

Adaptive Graph Neural Networks for Cosmological Data Generalization: Abstract and Intro

Author:
(1) Mohammad Akhlaghi, Centro de Estudios de F´ısica del Cosmos de Aragon (CEFCA), Plaza San Juan 1, 44001, Teruel, Spain {mohammad@akhlaghi.org}. Table of Links Abstract and Intro Simulating the Exposure Map Acknowledgment and References Appendix ABSTRACT The pointing pattern is an integral part of designing one’s observation strategy for a certain scientific goal. But accounting for the particular science case or instrument artifacts (like distortion, vignetting or large areas of bad pixels) can make it hard to predict how the exposure map of the final stack will be. To help address this problem, Gnuastro 0.21 includes a new executable program called astscript-pointing-simulate that is fully described in the Gnuastro manual and comes with a complete tutorial. The figures of this research note are reproducible with Maneage, on the Git commit 4176d29. Keywords: Astronomical methods (1043), Field of view (534), Direct imaging (387), Astronomical techniques (1684), Astronomy software (1855) 1. INTRODUCTION Astronomical images are often composed of many single exposures. After completing the observations, when the science topic does not depend on the time of observation, we stack those single exposures into one deep stacked image (also known as coadd). Designing the strategy to take those single exposures is therefore a very important aspect of planning an astronomical observation. Each exposure has a pointing (the location on the sky that it was targeting). Subsequent exposures are usually taken with different pointings. When the next pointing is ver near (a small fraction of the field of view) to the previous one, it is known as a dither (literally/generally meaning trembling or vibration). But when the distance between subsequent pointings is large (and issues like re-focusing become necessary), the pointing is known as an offset. These terms are sometimes used interchangably by some. When we only have dithers, most of the central part of the final stack has a fixed depth. Only a thin border becomes shallower (conveying a sense of vibration!). For example see Figures 3 and 4 of Illingworth et al. (2013) which show the exposures that went into the XDF survey. These types of images (where each pixel contains the number of exposures, or time, that were used in it) are known as exposure maps. However, it can happen that only offsets are used. For example see Figure 1 of Trujillo et al. (2021), which shows the exposure map for the LIGHTS survey. The dithering pattern therefore is strongly defined by the science case (high-level purpose of the observation) and your telescope’s field of view. For example in the HUDF, CANDELS and other proposals (that constitute the XDF), the scientific target was high redshift (distant) galaxies. When counting the pixels that they cover in relation to the number of pixels in each exposure, these targets are very small objects. Such that within that small footprint (of just 1 arcminute!) we have thousands of such small objects (which may also be stars or nearby galaxies of similar observed size to the high redshift galaxies). Therefore for the scientific goal of the XDF, the very small dithers were sufficient to avoid instrument artifacts. However, the LIGHTS survey is focused on the halos of large nearby galaxies (that can be more than 10 arcminutes wide); thus covering a large fraction of the field of view of the Large Binocular Telescope that was used. In order to do robust calibration of the images (for example to estimate the flat field pattern, or the sky background level) it was necessary to have offsets. This enabled the galaxy and its halo to cover very different pixels from one exposure to the next. In other cases, the pointings include both offsets and dithers (around each offset). To find the ideal dither pattern for the particular scientific goal, it therefore helps to be able to simulate various strategies. In the next section, an installed script is introduced for this purpose (to simulate the exposure map of a series of pointings) in GNU Astronomy Utilities (Gnuastro Akhlaghi & Ichikawa 2015) version[1] 0.21 and later. This paper is available on arxiv under CC 4.0 DEED license. [1] This paper is published shortly before the release of Gnuastro 0.21. If Gnuastro 0.21 is not yet available, please use the latest test (alpha) release. Author: (1) Mohammad Akhlaghi, Centro de Estudios de F´ısica del Cosmos de Aragon (CEFCA), Plaza San Juan 1, 44001, Teruel, Spain {mohammad@akhlaghi.org}. Author: Author: (1) Mohammad Akhlaghi, Centro de Estudios de F´ısica del Cosmos de Aragon (CEFCA), Plaza San Juan 1, 44001, Teruel, Spain {mohammad@akhlaghi.org}. Table of Links Abstract and Intro Abstract and Intro Simulating the Exposure Map Simulating the Exposure Map Acknowledgment and References Acknowledgment and References Appendix Appendix ABSTRACT The pointing pattern is an integral part of designing one’s observation strategy for a certain scientific goal. But accounting for the particular science case or instrument artifacts (like distortion, vignetting or large areas of bad pixels) can make it hard to predict how the exposure map of the final stack will be. To help address this problem, Gnuastro 0.21 includes a new executable program called astscript-pointing-simulate that is fully described in the Gnuastro manual and comes with a complete tutorial. The figures of this research note are reproducible with Maneage, on the Git commit 4176d29. Keywords : Astronomical methods (1043), Field of view (534), Direct imaging (387), Astronomical techniques (1684), Astronomy software (1855) Keywords 1. INTRODUCTION Astronomical images are often composed of many single exposures. After completing the observations, when the science topic does not depend on the time of observation, we stack those single exposures into one deep stacked image (also known as coadd). Designing the strategy to take those single exposures is therefore a very important aspect of planning an astronomical observation. Each exposure has a pointing (the location on the sky that it was targeting). Subsequent exposures are usually taken with different pointings. When the next pointing is ver near (a small fraction of the field of view) to the previous one, it is known as a dither (literally/generally meaning trembling or vibration). But when the distance between subsequent pointings is large (and issues like re-focusing become necessary), the pointing is known as an offset. These terms are sometimes used interchangably by some. When we only have dithers, most of the central part of the final stack has a fixed depth. Only a thin border becomes shallower (conveying a sense of vibration!). For example see Figures 3 and 4 of Illingworth et al. (2013) which show the exposures that went into the XDF survey. These types of images (where each pixel contains the number of exposures, or time, that were used in it) are known as exposure maps. However, it can happen that only offsets are used. For example see Figure 1 of Trujillo et al. (2021), which shows the exposure map for the LIGHTS survey. The dithering pattern therefore is strongly defined by the science case (high-level purpose of the observation) and your telescope’s field of view. For example in the HUDF, CANDELS and other proposals (that constitute the XDF), the scientific target was high redshift (distant) galaxies. When counting the pixels that they cover in relation to the number of pixels in each exposure, these targets are very small objects. Such that within that small footprint (of just 1 arcminute!) we have thousands of such small objects (which may also be stars or nearby galaxies of similar observed size to the high redshift galaxies). Therefore for the scientific goal of the XDF, the very small dithers were sufficient to avoid instrument artifacts. However, the LIGHTS survey is focused on the halos of large nearby galaxies (that can be more than 10 arcminutes wide); thus covering a large fraction of the field of view of the Large Binocular Telescope that was used. In order to do robust calibration of the images (for example to estimate the flat field pattern, or the sky background level) it was necessary to have offsets. This enabled the galaxy and its halo to cover very different pixels from one exposure to the next. In other cases, the pointings include both offsets and dithers (around each offset). To find the ideal dither pattern for the particular scientific goal, it therefore helps to be able to simulate various strategies. In the next section, an installed script is introduced for this purpose (to simulate the exposure map of a series of pointings) in GNU Astronomy Utilities (Gnuastro Akhlaghi & Ichikawa 2015) version[1] 0.21 and later. This paper is available on arxiv under CC 4.0 DEED license. This paper is available on arxiv under CC 4.0 DEED license. available on arxiv [1] This paper is published shortly before the release of Gnuastro 0.21. If Gnuastro 0.21 is not yet available, please use the latest test (alpha) release.

Gnuastro: Simulating The Exposure Map of a Pointing Pattern - Abstract and Intro

This paper is available on Arxiv under CC 4.0 license.
Authors:
(1) Hyerin Cho (조혜린), Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA;
(2) Ben S. Prather, CCS-2, Los Alamos National Laboratory, PO Box 1663, Los Alamos, NM 87545, USA;
(3) Ramesh Narayan, enter for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA;
(4) Priyamvada Natarajan, Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA, Department of Astronomy, Yale University, Kline Tower, 266 Whitney Avenue, New Haven, CT 06511, USA and Department of Physics, Yale University, P.O. Box 208121, New Haven, CT 06520, USA;
(5) Kung-Yi Su, Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA;
(6) Angelo Ricarte, Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA;
(7) Koushik Chatterjee, Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA. Table of Links Abstract and Introduction Numerical Methods Hydrodynamic Bondi Accretion Magnetized Bondi Accretion Feedback Via Reconnection-Driven Convection Summary and Conclusions Acknowledgements Appendix A. GRMHD Primer and Definitions B. Numerical Set-up C. Resolution and Initial Condition Study References ABSTRACT Keywords: Accretion (14), Active galactic nuclei (16), Bondi accretion (174), Schwarzschild black holes (1433), Supermassive black holes (1663), Magnetohydrodynamical simulations (1966) 1. INTRODUCTION Most nearby galaxies are found to harbor central supermassive black holes (SMBHs) whose masses are correlated with properties of the stellar components of their hosts (e.g., Magorrian et al. 1998; Ferrarese & Merritt 2000; Gebhardt et al. 2000; Kormendy & Ho 2013). However, the details of how gas flows into the galactic nucleus from large cosmic distances and how the SMBH in turn imparts feedback into the galaxy remain coupled unresolved problems. Recently, some attempts to connect these disparate scales have been made using nested simulations where each smaller-scale simulation is initialized from the next larger-scale simulation (e.g., Hopkins & Quataert 2010; Ressler et al. 2020; Guo et al. 2023); or by adopting Lagrangian hyper-refinement methods (e.g., Angl´esAlc´azar et al. 2021; Hopkins et al. 2023); or by pushing out the simulation regime to larger scales (e.g., Lalakos et al. 2022; Kaaz et al. 2023). While successful in studying the process of accretion, feedback from the SMBH has not been followed out to galaxy scales in prior work because either the communication between scales is only directed inwards or because of the inability to include the entire galaxy and the black hole. We employ here a multi-zone computational method which represents a first attempt to not only follow accretion flows but also trace feedback across 7 orders of magnitude from the event horizon to galactic scales. With this method, we study purely hydrodynamic Bondi accretion as well as the magnetized Bondi problem. The outline of this Letter is as follows: In Section 2 we give an overview of the numerical scheme that captures a wide dynamic range simultaneously. In Section 3, we present a purely hydrodynamic simulation and test our numerical scheme by comparing against the general relativistic (GR) analytical solution. In Section 4 we include magnetic fields for a more realistic representation of the environment of SMBHs and study how Bondi accretion is modified. We discuss our findings of feedback via convection in Section 5 and summarize conclusions of our study in Section 6. Additional details are presented in a set of short appendices: various GRMHD quantities are defined in Appendix A, the details of the simulation set-up are outlined in Appendix B, and a resolution and initial condition study is presented in Appendix C. This paper is available on Arxiv under CC 4.0 license. This paper is available on Arxiv under CC 4.0 license. Authors: (1) Hyerin Cho (조혜린), Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA; (2) Ben S. Prather, CCS-2, Los Alamos National Laboratory, PO Box 1663, Los Alamos, NM 87545, USA; (3) Ramesh Narayan, enter for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA; (4) Priyamvada Natarajan, Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA, Department of Astronomy, Yale University, Kline Tower, 266 Whitney Avenue, New Haven, CT 06511, USA and Department of Physics, Yale University, P.O. Box 208121, New Haven, CT 06520, USA; (5) Kung-Yi Su, Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA; (6) Angelo Ricarte, Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA; (7) Koushik Chatterjee, Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA. This paper is available on Arxiv under CC 4.0 license. Authors: Authors: (1) Hyerin Cho (조혜린), Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA; (2) Ben S. Prather, CCS-2, Los Alamos National Laboratory, PO Box 1663, Los Alamos, NM 87545, USA; (3) Ramesh Narayan, enter for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA; (4) Priyamvada Natarajan, Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA, Department of Astronomy, Yale University, Kline Tower, 266 Whitney Avenue, New Haven, CT 06511, USA and Department of Physics, Yale University, P.O. Box 208121, New Haven, CT 06520, USA; (5) Kung-Yi Su, Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA; (6) Angelo Ricarte, Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA; (7) Koushik Chatterjee, Center for Astrophysics | Harvard & Smithsonian, 60 Garden Street, Cambridge, MA 02138, USA and Black Hole Initiative at Harvard University, 20 Garden Street, Cambridge, MA 02138, USA. Table of Links Abstract and Introduction Abstract and Introduction Numerical Methods Numerical Methods Hydrodynamic Bondi Accretion Hydrodynamic Bondi Accretion Magnetized Bondi Accretion Magnetized Bondi Accretion Feedback Via Reconnection-Driven Convection Feedback Via Reconnection-Driven Convection Summary and Conclusions Summary and Conclusions Acknowledgements Acknowledgements Appendix Appendix A. GRMHD Primer and Definitions A. GRMHD Primer and Definitions B. Numerical Set-up B. Numerical Set-up C. Resolution and Initial Condition Study C. Resolution and Initial Condition Study References References ABSTRACT Keywords : Accretion (14), Active galactic nuclei (16), Bondi accretion (174), Schwarzschild black holes (1433), Supermassive black holes (1663), Magnetohydrodynamical simulations (1966) Keywords 1. INTRODUCTION Most nearby galaxies are found to harbor central supermassive black holes (SMBHs) whose masses are correlated with properties of the stellar components of their hosts (e.g., Magorrian et al. 1998; Ferrarese & Merritt 2000; Gebhardt et al. 2000; Kormendy & Ho 2013). However, the details of how gas flows into the galactic nucleus from large cosmic distances and how the SMBH in turn imparts feedback into the galaxy remain coupled unresolved problems. Recently, some attempts to connect these disparate scales have been made using nested simulations where each smaller-scale simulation is initialized from the next larger-scale simulation (e.g., Hopkins & Quataert 2010; Ressler et al. 2020; Guo et al. 2023); or by adopting Lagrangian hyper-refinement methods (e.g., Angl´esAlc´azar et al. 2021; Hopkins et al. 2023); or by pushing out the simulation regime to larger scales (e.g., Lalakos et al. 2022; Kaaz et al. 2023). While successful in studying the process of accretion, feedback from the SMBH has not been followed out to galaxy scales in prior work because either the communication between scales is only directed inwards or because of the inability to include the entire galaxy and the black hole. We employ here a multi-zone computational method which represents a first attempt to not only follow accretion flows but also trace feedback across 7 orders of magnitude from the event horizon to galactic scales. With this method, we study purely hydrodynamic Bondi accretion as well as the magnetized Bondi problem. The outline of this Letter is as follows: In Section 2 we give an overview of the numerical scheme that captures a wide dynamic range simultaneously. In Section 3, we present a purely hydrodynamic simulation and test our numerical scheme by comparing against the general relativistic (GR) analytical solution. In Section 4 we include magnetic fields for a more realistic representation of the environment of SMBHs and study how Bondi accretion is modified. We discuss our findings of feedback via convection in Section 5 and summarize conclusions of our study in Section 6. Additional details are presented in a set of short appendices: various GRMHD quantities are defined in Appendix A, the details of the simulation set-up are outlined in Appendix B, and a resolution and initial condition study is presented in Appendix C. This paper is available on Arxiv under CC 4.0 license. This paper is available on Arxiv under CC 4.0 license. Arxiv

Bridging Scales in Black Hole Accretion and Feedback: Studying Supermassive Black Holes

DUBAI, UAE, April 9th, 2024/Chainwire/--Landrocker, an upcoming play-to-earn (P2E) game that combines the thrill of space exploration and multiplayer battles with the opportunity to earn rewards, has successfully raised $2.5 million through its seed and presale funding rounds. With only four rounds remaining until the completion of its presale, Landrocker is on a mission to breathe fresh life into the P2E gaming space. The capital will be strategically utilized to continuously enhance Landrocker’s gameplay by adding fresh features and modes. Set to release in Q2, 2024, Landrocker Pro promises an enthralling experience and a focus on quality gameplay – a factor often overlooked by P2E developers. “We are extremely grateful for the overwhelming support we’ve received in our funding rounds,” said Landrocker’s CEO Hamid Fathalian. “Interestingly, a recent survey found that gameplay quality is a key factor for P2E gamers when choosing a game – even more important than economic potential. With Landrocker, we have striven to combine exceptional game elements and the opportunity for bountiful play-to-earn and play-to-win rewards. We aren’t just interested in preaching to the converted, so to speak, but enticing traditional gamers too, pure gamers. We cannot wait for them to experience what we’ve built!” Inviting players to embark on an epic journey of discovery and space exploration, Landrocker’s gameplay sees gamers commander Rover vehicles and roam various planets in search of valuable resources such as NFTs, crypto tokens, crafting materials, and more. Materials unearthed on these mining missions can be crafted into valuable NFTs like Rovers and Planets to boost earnings, or sold on the integrated peer-to-peer marketplace. The game contains an infinite number of planets and galaxies, each with its own unique design. As well as familiar play-to-earn and play-to-win dynamics, Landrocker introduces a concept known as play-to-liberate (P2L), wherein players use special fighter Rovers to ‘liberate’ a planet from all dangers and outsiders. After a planet is liberated, it is made available in P2E mode where players can mine it for resources and a percentage of revenue generated is dedicated to the liberators. Other players, meanwhile, get a share of revenue based on how many kills they recorded in P2L mode. The action-packed game leverages blockchain to ensure transparent and hidden rewards, providing all players with an equal chance of discovery. It also integrates AI algorithms to balance its play-to-earn and play-to-win resources, an approach intended to prevent inflation and ensure a balanced gaming economy, another common challenge for P2E games. Landrocker will be available on multiple platforms, including Windows, iOS, Android, and web browsers, and will feature a variety of gaming experiences including multiplayer battles, racing challenges, strategic standoffs, city conquests, and more. In a strategic move, Landrocker (LRT) is scheduled to list on the Uniswap DEX in May. The listing is timed to coincide with the launch of Landrocker Pro, aligning the game’s entry into the market with the availability of its native token. About Landrocker: Landrocker is an innovative play-to-earn game set in a universe of space exploration  and discovery. It offers players a unique blend of adventure, strategy, multiplayer battles, and the opportunity to earn valuable rewards, including NFTs. With a focus on quality gameplay and a balanced gaming economy, Landrocker is set to become a leading title in the popular P2E gaming space. Website | Telegram | Instagram | X | Discord Contact Homa Has homa@landrocker.io This story was distributed as a release by Chainwire under HackerNoon’s Business Blogging Program. Learn more about the program . here

Landrocker's $2.5M Funding Unleashes a New Era Of Hardcore P2E Gaming And Pro Players To Blockchain

Landrocker এর $2.5M তহবিল ব্লকচেইনে হার্ডকোর P2E গেমিং এবং প্রো প্লেয়ারদের একটি নতুন যুগের সূচনা করে

Landrockers Finanzierung in Höhe von 2,5 Millionen US-Dollar läutet eine neue Ära des Hardcore-P2E-Gamings und der Blockchain-Profispieler ein

La financiación de 2,5 millones de dólares de Landrocker desata una nueva era de juegos P2E incondicionales y jugadores profesionales en Blockchain

Le financement de 2,5 millions de dollars de Landrocker ouvre une nouvelle ère de jeux P2E hardcore et de joueurs professionnels vers la blockchain

लैंडरॉकर की $2.5M फंडिंग ने हार्डकोर P2E गेमिंग और प्रो प्लेयर्स के ब्लॉकचेन के लिए एक नए युग की शुरुआत की

ランドロッカーの250万ドルの資金調達により、ハードコアP2Eゲームとプロプレイヤーのブロックチェーン時代が到来

Landrocker의 250만 달러 자금 조달로 하드코어 P2E 게임 및 프로 플레이어의 블록체인 시대가 열렸습니다.

O financiamento de US$ 2,5 milhões da Landrocker desencadeia uma nova era de jogos P2E hardcore e jogadores profissionais para Blockchain

Финансирование Landrocker в размере 2,5 миллиона долларов открывает новую эру хардкорных P2E-игр и профессиональных игроков в блокчейне

Landrocker'ın 2,5 Milyon Dolarlık Fonu, Blockchain'de Zorlu P2E Oyun ve Profesyonel Oyuncuların Yeni Bir Dönemini Başlatıyor

Khoản tài trợ 2,5 triệu đô la của Landrocker mở ra kỷ nguyên mới của trò chơi P2E hạng nặng và người chơi chuyên nghiệp trên chuỗi khối

Landrocker 获 250 万美元融资，开启硬核 P2E 游戏和职业玩家进入区块链的新时代

In nearly all of my recent explorations of Generative AI, I've come to realize how important prompts are. That hasn't necessarily translated me into writing better prompts all the time, but being aware of the problem is at least one step towards correcting it, right? One of the things that I thought would help me personally would be to have a tool to quickly compare and contrast different prompts. Obviously, I could just open up multiple tabs in a browser, but I really wanted something more direct and simplified for my purposes. With that in mind, let me share what I've built. Prompt Tester Prompt Tester is my incredibly imaginatively named tool to... um... test prompts. It begins with one prompt: The idea is that you enter your initial prompt, and then click one or more times to add more prompts. When ready, you can then just click "Generate Results": Before showing some example results, let me talk a bit about how it was built. The Code The backend is an simple Node.js server that handles throwing up the static HTML and responding to the front-end API requests. Once again, I thank Todd Sharp for sharing his initial code with me. Here's the boring simple handling portion: incredibly async function handler(req, res) {
	console.log('Entered handler.', req.method, req.url);

	if(req.method === 'GET' && req.url.indexOf('favicon.ico') === -1) {
		res.writeHead(200, { 'Content-Type':'text/html' });
		res.write(fs.readFileSync('./demo.html'));
		res.end();
	} else if(req.method === 'POST' && req.url === '/api') {

		let body = '';
		req.on('data', chunk => {
			body += chunk.toString();
		});

		req.on('end', async () => {
			body = JSON.parse(body);
			console.log('BODY:\n', JSON.stringify(body, null, '\t'));

			let result = await callGemini(body.prompt);
			res.writeHead(200, { 'Content-Type':'application/json' });
			res.write(JSON.stringify(result));
			res.end();

		});

	}

}

const server = http.createServer(handler);
server.listen(3000);
console.log('Listening on port 3000'); And the part which just passes the prompt off to Google's . In theory, this could be changed to ChatGPT or any other service. callGemini Gemini API const MODEL_NAME = "gemini-pro";
const API_KEY = process.env.GOOGLE_AI_KEY;

const genAI = new GoogleGenerativeAI(API_KEY);
const model = genAI.getGenerativeModel({ model: MODEL_NAME });

async function callGemini(text) {

	const generationConfig = {
		temperature: 0.9,
		topK: 1,
		topP: 1,
		maxOutputTokens: 2048,
	};

	const safetySettings = [
		{ category: HarmCategory.HARM_CATEGORY_HARASSMENT, threshold: HarmBlockThreshold.BLOCK_MEDIUM_AND_ABOVE, },
		{ category: HarmCategory.HARM_CATEGORY_HATE_SPEECH,	threshold: HarmBlockThreshold.BLOCK_MEDIUM_AND_ABOVE, },
		{ category: HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT, threshold: HarmBlockThreshold.BLOCK_MEDIUM_AND_ABOVE, },
		{ category: HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT, threshold: HarmBlockThreshold.BLOCK_MEDIUM_AND_ABOVE, },
	];

	const parts = [
    	{text},
  	];

	const result = await model.generateContent({
		contents: [{ role: "user", parts }],
		generationConfig,
		safetySettings,
	});

	try {

		if(result.response.promptFeedback && result.response.promptFeedback.blockReason) {

			return { error: `Blocked for ${result.response.promptFeedback.blockReason}` };
		}
		const response = result.response.candidates[0].content.parts[0].text;
		return { response };
	} catch(e) {
		// better handling
		return {
			error:e.message
		}
	}
	
} I'm proud to say that I'm now, finally, doing some basic error handling and trying to expose to the user (me) why a prompt was blocked. The front end is relatively simple. I used for the core CSS (again, thank you Todd) and some vanilla JavaScript to handle adding new prompts and making the calls. Here's the HTML (minus the inline CSS): Milligram <h1>Prompt Tester</h1>

<p>
This tool helps you see the result of multiple prompts at the same time. The idea is to use similar prompts with different phrasing 
to see how it impacts the result.
</p>

<div id="prompts">
</div>

<template id="promptRow">
<div class="row">
	<div class="column column-33"><textarea placeholder="Enter your prompt."></textarea></div>
	<div class="column column-67 result"></div>
</div>
<hr>
</template>


<div class="float-right">
<button id="addPrompt">Add Prompt</button> <button id="generateResults">Generate Results</button>
</div> And then the JavaScript. Notice I used above to handle the prompt layout. This is the first time I've used that tag, and it was relatively easy. <template> let $template, $prompts, $addPromptsBtn, $generateResultsBtn;
let promptCount = 0;

document.addEventListener('DOMContentLoaded', init, false);
async function init() {
	
	$addPromptsBtn = document.querySelector('#addPrompt');
	$addPromptsBtn.addEventListener('click',  addPrompt, false);
	$generateResultsBtn = document.querySelector('#generateResults')
	$generateResultsBtn.addEventListener('click',  generateResults, false);
	$template = document.querySelector('#promptRow');
	$prompts = document.querySelector('#prompts');

	addPrompt();
}

function addPrompt() {
	promptCount++;
	const newPrompt = $template.content.cloneNode(true);
	$prompts.append(newPrompt);
}

async function generateResults() {
	// disable add prompt and generate
	$addPromptsBtn.setAttribute('disabled', 'disabled');
	$generateResultsBtn.setAttribute('disabled', 'disabled');

	let $results = document.querySelectorAll('div.result');
	// clear any previous results
	$results.forEach(r => r.innerHTML = '');

	/*
	gather prompts from the inputs
	*/
	let prompts = [];
	let $fields = document.querySelectorAll('#prompts textarea');
	for(f of $fields) prompts.push(f.value);

	let promises = [];
	for(p of prompts) promises.push(getResult(p));
	let results = await Promise.all(promises);

	// assign results to result area for each, handle errors - later
	// there's a one to one correspondance between our array of results and the result div
	for(let i=0;i<results.length;i++) {
		if(results[i].response) $results[i].innerHTML = marked.parse(results[i].response);
		else if(results[i].error) $results[i].innerHTML = `<strong>Error with Prompt: </strong> ${results[i].error}`;
	}

	$addPromptsBtn.removeAttribute('disabled');
	$generateResultsBtn.removeAttribute('disabled');

}

async function getResult(p) {
	console.log('get result for',p);
	// quick return on empty prompt
	if(p.trim() === '') return { response:'<strong>Enter a prompt.</strong>' };
	let req = await fetch('/api', { method:'POST', body: JSON.stringify({prompt:p.trim()})});
	let result = await req.json();
	return result;
} I that's all pretty simple DOM manipulation and API calling, but definitely let me know in a comment below if something doesn't make sense. Notice I fire off all my API calls at once and wait for them to finish. think Promise.all Also, note I use the library. The results from Google typically have Markdown, and this helps render them nicely in my little web app. marked All of this isn't terribly interesting I think, but let's look at some testing. As I said, I the style of prompt was important. Having it presented directly to me in a tool went a of a long way to reinforcing that. knew heck Sample Result - Gravity Let's start with a simple idea - explaining gravity but for different age groups. Because of the large size of the results, I'm going to use a bit of CSS here to help keep things sane. Prompt: explain gravity to me but make it appropriate for a child in 1st grade Gravity is a superpower that our planet has! It's like an invisible force that makes everything on Earth stay on the ground. Imagine you have a ball in your hand. When you drop it, what happens? It falls down, right? That's because of gravity. Gravity pulls the ball towards the center of the Earth, and so it falls to the ground. Gravity is also what keeps us on the ground. It's what keeps our feet planted firmly on the Earth and prevents us from floating away! Gravity is a very important force. It keeps everything in place and helps things work the way they do. Without gravity, we would all be floating around in space, and things would be very chaotic! Here's a fun way to think about gravity: imagine that the Earth is a giant magnet, and everything on Earth is made of metal. The magnet pulls the metal objects towards it, and that's what keeps them on the ground. Gravity is a very important and interesting force. It's one of the things that makes our planet such a unique and special place to live! Prompt: explain gravity to me but make it appropriate for a child in 7th grade Imagine you're playing with a ball. You toss it up in the air, and it comes back down to you. This is because of gravity. Gravity is a force that pulls things toward each other. It's what keeps you on the ground and what makes things fall down. The amount of gravity between two objects depends on their mass. The more mass an object has, the stronger its gravity is. That's why things like planets and stars have more gravity than things like people and cars. Gravity is also what keeps the planets in orbit around the sun. The sun's gravity is much stronger than the Earth's, so it pulls the Earth towards it. This keeps the Earth moving in a circle around the sun. Gravity is a very important force in our universe. It keeps things in place and makes things move. Without gravity, the planets would fly off into space, and we would all float away. Here are some fun facts about gravity: Gravity is responsible for the tides. The moon's gravity pulls on Earth's oceans, causing them to rise and fall. Gravity is the reason why we have an atmosphere. Without gravity, the air would just float away into space. Gravity is what makes it possible for us to walk. When we walk, our feet push against the ground, and the ground pushes back. This is what propels us forward. Gravity is the reason why we don't float away into space. Earth's gravity pulls us towards its center, keeping us on the ground. Note the typo in the prompt below. Gemini handled it just fine. Prompt: explain gravity to me but make it appropriate for a college-aged student Gravity: The Force of Attraction Gravity is a fundamental force in the universe responsible for keeping celestial bodies, such as planets, moons, and stars, in motion. It is also what keeps us firmly planted to the ground and allows objects to fall when dropped. Newton's Law of Universal Gravitation: The law of universal gravitation states that every object in the universe attracts every other object with a force proportional to the product of their masses and inversely proportional to the square of the distance between them. Mathematically, it can be expressed as: F = Gm1m2/r^2 Where: F is the gravitational force between two objects. G is the gravitational constant (6.674 × 10^-11 N m^2/kg^2). m1 and m2 are the masses of the two objects. r is the distance between the centers of the two objects. Significance of Gravity: The gravitational pull of larger celestial bodies keeps smaller ones revolving around them. For example, Earth's gravity keeps the moon in orbit, while Sun's gravity keeps Earth and other planets in their respective orbits. Keeps Celestial Bodies in Orbit: Gravity between the Earth and the moon causes ocean tides. As the moon orbits Earth, its gravitational pull creates a bulge in the ocean's water on the side facing the moon. Another bulge forms on the opposite side of Earth due to the inertial forces. As the Earth rotates, these bulges move across the planet's surface, resulting in high and low tides. Responsible for Tides: Gravity pulls objects towards Earth's center, preventing them from floating away. This phenomenon allows us to walk, stand, and interact with our environment. Keeps Objects on Earth: Gravity plays a crucial role in the formation of stars and galaxies. The gravitational collapse of massive clouds of gas and dust leads to the creation of new stars. Similarly, the gravitational pull of galaxies keeps them bound together, preventing them from dispersing. Formation of Stars and Galaxies: Albert Einstein's general relativity theory provides a more comprehensive understanding of gravity. It explains that gravity is not a force but rather a curvature of spacetime caused by the presence of mass or energy. This curvature affects the motion of objects, causing them to follow curved paths rather than straight lines. Relativity Theory: In conclusion, gravity is a fundamental force responsible for keeping celestial bodies in motion, tides, and the behavior of objects on Earth. It is a complex phenomenon that has been studied extensively over the centuries, and it continues to fascinate scientists and researchers to this day. Sample Result - Pets For my second test, I started with a basic prompt and then a better version that asked for specific types of details: (Quick editorial note - the tabular data below is a bit messy, but was messy in the original output.) not Prompt: why are cats so much better than dogs? Cats and dogs are both popular pets, and there is no definitive answer to the question of which one is better. Both cats and dogs have their own unique set of advantages and disadvantages, and the best pet for you will depend on your individual needs and preferences. Some people prefer cats because they are generally more independent than dogs. Cats are typically happy to entertain themselves, and they don't need to be walked or taken outside for exercise. This can be a big advantage for people who live in apartments or who have busy schedules. Cats are also known for being clean and tidy animals. They groom themselves regularly, and they typically use the litter box without any problems. This can be a big advantage for people who don't want to deal with the mess and smell of a dog. Of course, there are also some disadvantages to having a cat. Cats can be more expensive than dogs, and they can be more difficult to train. Cats are also more likely to scratch furniture and other household items. Ultimately, the decision of whether to get a cat or a dog is a personal one. There is no right or wrong answer, and the best pet for you will depend on your individual needs and preferences. Here is a table summarizing the key differences between cats and dogs: Characteristic Cat Dog Independence More independent Less independent Exercise needs Does not need to be walked or taken outside Needs to be walked or taken outside Cleanliness Cleans itself regularly Needs to be bathed regularly Training Can be difficult to train Easier to train Scratching May scratch furniture and other household items Does not typically scratch furniture Cost More expensive Less expensive Prompt: why are cats so much better than dogs? explain with a bulleted list of reasons that cover emotional well being and financial aspects Emotional well-being: Independence: Cats are more independent than dogs, which can be a good thing for people who want a pet that doesn't require a lot of attention. Low maintenance: Cats are generally lower maintenance than dogs. They don't need to be walked or groomed as often, and they can be left alone for longer periods of time. Quiet: Cats are generally quieter than dogs, which can be ideal for people who live in apartments or have noise-sensitive neighbors. Therapeutic: Studies have shown that cats can have a calming effect on humans, and they can help to reduce stress and anxiety. Financial aspects: Lower cost: Cats are generally less expensive to own than dogs. They eat less food, they need fewer vet visits, and they don't require as much grooming. Easier to travel with: Cats are easier to travel with than dogs. They can be transported in a carrier, and they don't need to be walked or exercised while you're away. Longer lifespan: Cats have a longer lifespan than dogs, on average. This means that you can enjoy your cat's companionship for many years to come. Sample Result - Chain of Thought Prompting The following test came from an presentation from AWS re:Invent: . I found a lot of great advice from this session, including a good overview of Chain of Thought prompting, where the idea is you provide information about the steps used to solve a problem to help the GenAI engine better solve a prompt. excellent AWS re:Invent 2023 - Prompt engineering best practices for LLMs on Amazon Bedrock (AIM377) Now, the example given in that presentation went like so: Q: Roger has 5 tennis balls. he buys 2 more cans of tennis balls. Each 
can has 3 tennis balls. How many tennis balls does he have now?

A: The answer is 11.

Q. The cafeteria had 23 apples. If they used 20 to make lunch and 
bought 6 more, how many apples do they have? In the presentation, the model in use incorrectly returned 27. In my test, it worked correctly, but I absolutely saw issues with math back when I built my last November. generative AI blackjack advice demo As I said, Gemini handled it fine: But in Prompt Tester, I tried the suggested CoT prompt: A: The answer is 9. Q: Roger has 5 tennis balls. he buys 2 more cans of tennis balls. Each 
can has 3 tennis balls. How many tennis balls does he have now?

A: Roger started with 5 balls. 2 cans of 3 tennis balls each is 6 
tennis balls. 5 + 6 = 11. The answer is 11.

Q. The cafeteria had 23 apples. If they used 20 to make lunch and 
bought 6 more, how many apples do they have? And the result was improved, showing how it got to the answer: A: The cafeteria had 23 apples. They used 20 apples for lunch, which 
means they have 3 apples left. They then bought 6 more apples, so 
they now have a total of 3 + 6 = 9 apples.

The answer is 9. The difference is pretty significant. The Code If this seems useful to you at all, feel free to grab the code from here: . As I mentioned, it shouldn't be that difficult to add or some such and use a different service. If you do, let me know! https://github.com/cfjedimaster/ai-testingzone/tree/main/prompt_tester callChatGPT

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Cách kiểm tra nhiều biến thể của lời nhắc AI sáng tạo

如何测试生成式 AI 提示的多种变体

The open-source developer community is full of volunteers, so, it was to be expected that their creations were also made for fun. That’s how we can enjoy a lot of free and ad-free games on mobile and desktop, all of them designed with passion. The teams behind these titles cover all potential expenses (domains, hardware, distribution, etc.) by themselves —and that’s why they’d appreciate a tip if you had a nice time with their software. Developers on GitHub usually share their PayPal or Patreon accounts, but these platforms always charge high fees, aren’t available for everyone, and aren’t censorship-resistant. If you want to donate to an open-source project available on GitHub, This Obyte-based application only asks for the GitHub profile to send some cryptocurrencies, even if the recipient doesn’t have a wallet yet. you can use as a decentralized, globally available, unstoppable, as well as faster and cheaper option. Kivach Besides, Kivach has a unique feature: recipients can distribute their received funds automatically among other repositories, considering their contribution to their projects —creating ‘cascading donations.’ This way, you could be helping several projects with just one donation. In this episode, we’ll explore five interesting games that you can try for free and, maybe, donate some coins via Kivach. Let’s go! SuperTux If you enjoyed the classic Super Mario Bros., you could enjoy SuperTux as well. This is a 2D jump'n'run sidescroller game inspired by that series, created and released by Bill Kendrick in 2004. Players control Tux, the Linux penguin, as he navigates through various levels, overcoming obstacles and enemies to rescue his kidnapped friend, Penny. https://www.youtube.com/watch?v=Wcqat_b8ft8&embedable=true Key features include colorful and engaging graphics, diverse levels, power-ups, and boss battles. SuperTux is currently developed by a community of contributors worldwide, who are improving the software and adding new features and levels continually. Besides the main story, there are community-contributed levels available as add-ons or in the forums, and an integrated level editor for further contributions. Funding for the project primarily comes . They help to pay for domains, servers, and salaries. In exchange, donors can enjoy exclusive content. The team accepts funds via PayPal and cryptocurrencies via Coinbase. In addition, you can send them coins via Kivach, where they appear as . from donations supertux/supertux OpenTTD Not an adventurer but a businessman? OpenTTD, based on the original Transport Tycoon Deluxe, allows players to build and manage transportation networks. It was initially released in 2004 by Ludvig Strigeus --also the creator of uTorrent. The game has been praised for the improvements applied to the original version, including multiplayer support, modding capabilities, and a customizable interface. https://www.youtube.com/watch?v=7tF73VBVSNQ&embedable=true They must efficiently transport passengers and cargo, navigate complex terrain, and compete with AI or other players in a sandbox environment. With its deep simulation and strategic challenges, the game offers an immersive and rewarding experience. In OpenTTD, players build railways, roads, airports, and waterways to connect cities and industries. The current team behind this software willingly offers their free time to keep it running, but additional costs like server infrastructure and hosting come from donations. They accept bank transfers, LiberaPay, PayPal, and cheque. However, all these methods have their own (often high) fees. You could send them some coins via Kivach for much less, just by clicking on “Donate” here. FreeOrion Inspired by the commercial series Masters of Orion, this is a space empire and galactic conquest game set in a vast universe filled with diverse alien species, ancient ruins, and strategic opportunities. It was initially in 2003 by a team led by Zach Laine. Players control their own customizable empire, managing resources, researching technologies, and engaging in diplomacy and warfare with rival factions. released https://www.youtube.com/watch?v=gF3_RFsXeU8&embedable=true It has an engaging gameplay, procedurally generated galaxies, customizable ship designs, and a rich lore-driven universe. Players have a multitude of options and opportunities at their disposal. They can design and customize their ships to fit specific roles and strategies, engage in epic space battles against AI-controlled factions or other players in multiplayer mode, explore the galaxy to discover ancient relics and artifacts that provide unique bonuses, and terraform planets to make them more habitable for their population. The development team is formed by volunteers, but they accept donations to cover costs derived from hosting, servers, hardware, artists, and others. They shared their PayPal and Flattr accounts for this purpose, but we have to note that the last one closed its operations in late 2023. You can still send them crypto via Kivach, where they’re available as . freeorion/freeorion Cataclysm: Dark Days Ahead (CDDA) This one could be a great option for fans of the post-apocalyptic genre. with a unique set of traits and dedicate to anything to survive or help others to do so: craft objects like clothes, weapons, or chemicals, build houses or shelters, farm the land and grow food, or directly fight against a plethora of creatures. In a world devastated by zombies, robots, and interdimensional creatures, players can create their own avatar https://www.youtube.com/watch?v=y_bt7-ZxfYM&embedable=true The game features a procedurally generated world filled with diverse locations, including towns, forests, and underground bunkers, each offering unique challenges and opportunities. There's no specific goal to win the game, instead, what matters is the experience, and to survive one more day. The software features ASCII graphics, creating a retro aesthetic reminiscent of classic roguelike games, with detailed environmental descriptions enhancing immersion and gameplay depth. CDDA was originally launched by “Whales” in 2010 and subsequently forked by the community in early 2013. They accept donations via Patreon, or you can sponsor an individual developer via GitHub Sponsors. Of course, it’s also possible to send them funds via Kivach, where they appear as . cleverraven/cataclysm-dda Enigma We have something simpler but equally challenging on this list as well: Enigma. This is a game inspired by Oxyd (Atari, 1990), Players control marbles through landscapes, overcoming obstacles, navigating hazards, and aiming for quick completion to beat personal and world records. which offers over 1,000 diverse levels blending logic puzzles with dexterity challenges. Released in 2002 by Daniel Heck and its team to keep the spirit of Oxyd alive after its publisher's departure, Enigma features Lua-generated landscapes, a level editor, and music by Andrew 'Necros' Sega (from Iris). Praised as a faithful Oxyd clone, it reached a stable version in 2007 and boasts high-resolution 2-D graphics, realistic physics, 500 in-game objects, and availability across multiple platforms. While free to play, Enigma welcomes donations to support ongoing development, ensuring its longevity and continued improvement. As they mention on the : “Just donating a fraction of the amount you would be willing to spend for a commercial 'Enigma' in a store would help us to improve Enigma.” The project is available on Kivach as . official website Enigma-Game/Enigma Contribute to more projects! There are all kinds of open-source software available on GitHub for you to explore and use for free. Most developers there aren’t paid for their work though, and a lot of projects are orphaned and often lose their functionality. Every software requires maintenance, after all. If you want your favorite projects to keep running, please consider donating! And don’t forget to check our previous chapters to discover more awesome tools. Kivach Donations: 5 GitHub projects it has helped so far How You Can Support Key Open-Source Initiatives With Kivach 5 Open-source Projects You Can Support With Kivach — and Cryptocurrencies 5 Open-source Projects You Can Support with Kivach and Cryptos, Episode III 5 Open-Source Projects You Can Donate to via Kivach, Episode IV: Privacy Tools 5 Open-source Blogging and Writing Tools to Donate to via Kivach (Ep V) 5 Open-Source Tools You Can Donate to via Kivach, Episode VI: Decentralized Services Featured Vector Image by pikisuperstar / Freepik

5 Open-Source Projects To Donate via Kivach, Episode VII: Games To Play for Free!

কিভাচের মাধ্যমে দান করার জন্য 5টি ওপেন-সোর্স প্রকল্প, পর্ব VII: বিনামূল্যে খেলার জন্য গেম!

5 Open-Source-Projekte zum Spenden über Kivach, Episode VII: Spiele zum kostenlosen Spielen!

5 proyectos de código abierto para donar a través de Kivach, Episodio VII: ¡Juegos para jugar gratis!

5 projets open source à donner via Kivach, épisode VII : jeux auxquels jouer gratuitement !

किवाच के माध्यम से दान करने के लिए 5 ओपन-सोर्स प्रोजेक्ट, एपिसोड VII: मुफ्त में खेलने के लिए गेम!

Kivach 経由で寄付できる 5 つのオープンソース プロジェクト、エピソード VII: 無料でプレイできるゲーム!

Kivach를 통해 기부할 5가지 오픈 소스 프로젝트, 에피소드 VII: 무료로 플레이할 수 있는 게임!

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5 проектов с открытым исходным кодом, которые можно пожертвовать через «Кивач», Эпизод VII: Игры, в которые можно играть бесплатно!

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5 dự án nguồn mở quyên góp qua Kivach, Tập VII: Trò chơi để chơi miễn phí!

通过 Kivach 捐赠的 5 个开源项目，第七集：免费玩游戏！

This paper is available on arxiv under CC 4.0 license. Authors: (1) Igor Yu. Potemine, Institut de Math´ematiques, Universit´e Paul Sabatier. Table of Links Abstract and Introduction 5-dimensional Einstein Equations Nested Gogberashvili Models Harmony of Transcosmic Spheres Sequence of Event Horizons Time-amalgamated Multiverses Dark Energy and Multiversal Nurseries Black Holes as Multiverses Multi-solitonic and Gyratonic Gravitational Waves Elliptic and Spiral Galaxies Cosmological Coupling Conclusion and References 9 Multi-solitonic and gyratonic gravitational waves So, we are interested in Robinson-Trautman metrics describing expanding spacetimes with spherical gravitational waves. Multi-soliton solutions of this type, associated with matter trapping, correspond to our Gogberashvili multiverses. If we admit that the null matter field has some internal spin / angular momentum, there are also more general (multi-)gyratonic solutions. They give rise to rotating Gogberashvili models/multiverses. This intriguing topic, including a thorough comparison ot two models, should be studied in more details. In particular, the harmony of transcosmic spheres should acquire more complete meaning.

Hyperverse, 5-dimensional Gravity and Multiverses: Multi-solitonic and Gyratonic Gravitational Waves

This paper is available on arxiv under CC 4.0 license. Authors: (1) Igor Yu. Potemine, Institut de Math´ematiques, Universit´e Paul Sabatier. Table of Links Abstract and Introduction 5-dimensional Einstein Equations Nested Gogberashvili Models Harmony of Transcosmic Spheres Sequence of Event Horizons Time-amalgamated Multiverses Dark Energy and Multiversal Nurseries Black Holes as Multiverses Multi-solitonic and Gyratonic Gravitational Waves Elliptic and Spiral Galaxies Cosmological Coupling Conclusion and References 12 Conclusion It turns out that nested Gogberashvili shells represent good models for multiverses inside the Hyperverse. In this paper we have restrained the Hyperverse to its local (4 + 1)- dimensional stratum. However, similar solutions exist in all dimensions d + 1 with d > 2. In fact, we can construct an infinite tower of embedded Gogberashvili multiverses of various dimensions. An alternative approach is given by Robinson-Trautman metrics describing expanding spacetimes and spherical gravitational waves. It provides us with an intriguing idea to consider supermassive astronomical black holes as expanding (2 + 1)-dimensional multiverses (with possible higher strata too). It might give new angles of view on the black hole growth, the cosmological coupling, relativistic jets and even on the classification of galaxies. References [1] V. Rubakov and M. Shaposhnikov. Extra space-time dimensions: Towards a solution to the cosmological constant problem. Physics Letters B, 125(2-3):139–143, 1983. [2] M. Gogberashvili. Our world as an expanding shell. Europhysics Letters, 7(3):396–399, 2000. [3] I. Potemine. The amalgamated structure of the Local Multiverse. Journal of High Energy Physics, Gravitation and Cosmology, 7(4):1213–1218, 2021. [4] K. Alvi. Approximate binary-black-hole metric. Physical Review D, 61(12-15), 2000. [5] J. Podolsky, R. Svarc, and H. Maeda. All solutions of Einstein’s equations in 2+1 dimensions: Λ-vacuum, pure radiation, or gyratons. Classical and Quantum Gravity, 36, 2019. [6] I. Robinson and A. Trautman. Spherical gravitational waves. Physical Review Letters, 8(4):431–432, 1960. 7] J. Podolsky and R. Svarc. Gyratons in the Robinson–Trautman and Kundt classes. Physical Review D, 99, 2019. [8] T.X. Zhang. The principles and laws of black hole universe. Journal of Modern Physics, 9(9):1838–1865, 2018. [9] Y. Bisabr. Gravitational coupling and the cosmological constant. International Journal of Modern Physics D, 27(8), 2018. [10] K. Croker, M. Zevin, D. Farrah, K. Nishimura, and G. Tarl´e. Cosmologically coupled compact objects: A single-parameter model for LIGO–Virgo mass and redshift distributions. The Astrophysical Journal Letters, 921(2), 2021.

Hyperverse, 5-dimensional Gravity and Multiverses:  Conclusion & References 

This paper is available on arxiv under CC 4.0 license. Authors: (1) Igor Yu. Potemine, Institut de Math´ematiques, Universit´e Paul Sabatier. Table of Links Abstract and Introduction 5-dimensional Einstein Equations Nested Gogberashvili Models Harmony of Transcosmic Spheres Sequence of Event Horizons Time-amalgamated Multiverses Dark Energy and Multiversal Nurseries Black Holes as Multiverses Multi-solitonic and Gyratonic Gravitational Waves Elliptic and Spiral Galaxies Cosmological Coupling Conclusion and References 11 Cosmological coupling Our constructions are consistent with the modern theory of cosmological coupling [9]. Recent astronomical observations show the abundance of black holes with unexpectedly high masses. Croker et al. proposed a mechanism, explaining the growth of black holes through anomalous coupling of matter with gravity [10]. It implies that the expansion of the universe and a large (but hidden) cosmological constant contribute to the growth of black hole masses.

Hyperverse, 5-dimensional Gravity and Multiverses: Cosmological Coupling

This paper is available on arxiv under CC 4.0 license. Authors: (1) Igor Yu. Potemine, Institut de Math´ematiques, Universit´e Paul Sabatier. Table of Links Abstract and Introduction 5-dimensional Einstein Equations Nested Gogberashvili Models Harmony of Transcosmic Spheres Sequence of Event Horizons Time-amalgamated Multiverses Dark Energy and Multiversal Nurseries Black Holes as Multiverses Multi-solitonic and Gyratonic Gravitational Waves Elliptic and Spiral Galaxies Cosmological Coupling Conclusion and References 8 Black holes as multiverses

Hyperverse, 5-dimensional Gravity and Multiverses: Black Holes as Multiverses

This paper is available on arxiv under CC 4.0 license. Authors: (1) Igor Yu. Potemine, Institut de Math´ematiques, Universit´e Paul Sabatier. Table of Links Abstract and Introduction 5-dimensional Einstein Equations Nested Gogberashvili Models Harmony of Transcosmic Spheres Sequence of Event Horizons Time-amalgamated Multiverses Dark Energy and Multiversal Nurseries Black Holes as Multiverses Multi-solitonic and Gyratonic Gravitational Waves Elliptic and Spiral Galaxies Cosmological Coupling Conclusion and References 7 Dark energy and multiversal nurseries It is possible to endow the Hyperverse with a metric approximately describing several widely separated Gogberashvili multiverses. For example, one can use the work of Kashif Alvi on widely separated binary black holes [4]. In our picture, the Hyperverse has numerous powerful sources of dark energy with large local cosmological constants. Those sources play role of the nurseries of multiverses. The situation is somewhat similar to the star formation and stellar nurseries in galaxies. In the latter case, it is related to the collapsing matter in giant molecular clouds. Notice that, in our constructions, the dark energy is a higher-dimensional phenomenon of the Hyperverse.

Hyperverse, 5-dimensional Gravity and Multiverses: Dark Energy and Multiversal Nurseries

This paper is available on arxiv under CC 4.0 license. Authors: (1) Igor Yu. Potemine, Institut de Math´ematiques, Universit´e Paul Sabatier. Table of Links Abstract and Introduction 5-dimensional Einstein Equations Nested Gogberashvili Models Harmony of Transcosmic Spheres Sequence of Event Horizons Time-amalgamated Multiverses Dark Energy and Multiversal Nurseries Black Holes as Multiverses Multi-solitonic and Gyratonic Gravitational Waves Elliptic and Spiral Galaxies Cosmological Coupling Conclusion and References 6 Time-amalgamated multiverses

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