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Italian Investment bank Mediobanca and the United Kingdom’s (UK) Founders Factory joined forces to a new venture studio and an accelerator program in late July. Starting with €12 million, the partnering companies intend to invest in 35 fintech businesses over the next five years. launch financial technology Founders Factory is known for building and funding startups, having established a relationship with UK financial services operator, Aviva, while supporting nearly 50 fintech ventures, including Acre, Qumata, and ClearGlass. The newly formed venture studio also plans to build, finance, and launch new fintech ventures that utilize advanced technologies, such as blockchain and (AI). Artificial Intelligence Mediobanca’s Group Chief Operating Officer Marco Pozzi said, “The systematic collaboration with some of the most interesting startups in the international market will allow us, on the one hand, to contribute to the growth of an extremely high-potential market such as fintech, and, on the other, to transform existing systems by developing distinctive and highly innovative solutions.” Meanwhile, Henry Lane Fox, CEO of Founders Factory commented, “Combining our venture-building capabilities and fintech experience with Mediobanca’s heritage, financial prowess, and global ambition creates a powerful platform to back fintech founders.” Europe’s Fintech Scene Lately, European fintech companies have been attracting investment in the millions. In fact, CNBC and market research firm Statista published a of the world’s top 200 fintech companies on 2 August 2023, which includes European firms like Adyen, Ecolytiq, BancaAideXa, Bondora, Bricksave, Conda, Crowdium and more, in the top five positions under defined categories. report But it hasn’t been all sunshine and roses. A published in October 2022 found that the deteriorating macroeconomic environment had hit European fintechs hard, “with valuations declining and access to financing becoming more difficult.” McKinsey report But the report also suggested that many European countries have flourishing fintech ecosystems, specifically in the UK and Sweden. This article was originally published by on . Akriti Seth 150sec

Italy's Mediobanca Teams up with UK's Founders Factory to Bolster Fintech Startups

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. M., & Schaye, J. 2009, MNRAS, 398, 53, doi: 10.1111/j.1365-2966.2009.15043.x Chatterjee, K., & Narayan, R. 2022, ApJ, 941, 30, doi: 10.3847/1538-4357/ac9d97 Chatterjee, K., Chael, A., Tiede, P., et al. 2023, Galaxies, 11, 38, doi: 10.3390/galaxies11020038 Dav´e, R., Angl´es-Alc´azar, D., Narayanan, D., et al. 2019, MNRAS, 486, 2827, doi: 10.1093/mnras/stz937 Event Horizon Telescope Collaboration, Akiyama, K., Algaba, J. C., et al. 2021, ApJL, 910, L13, doi: 10.3847/2041-8213/abe4de Ferrarese, L., & Merritt, D. 2000, ApJL, 539, L9, doi: 10.1086/312838 Ferri`ere, K. 2020, Plasma Physics and Controlled Fusion, 62, 014014, doi: 10.1088/13616587/ab49eb Fiacconi, D., Sijacki, D., & Pringle, J. E. 2018, MNRAS, 477, 3807, doi: 10.1093/mnras/sty893 Gammie, C. F., McKinney, J. C., & T´oth, G. 2003, ApJ, 589, 444, doi: 10.1086/374594 Gebhardt, K., Bender, R., Bower, G., et al. 2000, ApJL, 539, L13, doi: 10.1086/312840 Grete, P., Dolence, J. C., Miller, J. M., et al. 2023, 37, 465, doi: 10.1177/10943420221143775 Guerra, J. A., Lopez-Rodriguez, E., Chuss, D. T., Butterfield, N. O., & Schmelz, J. T. 2023, AJ, 166, 37, doi: 10.3847/1538-3881/acdacd Guo, M., Stone, J. M., Kim, C.-G., & Quataert, E. 2023, ApJ, 946, 26, doi: 10.3847/1538-4357/acb81e Hopkins, P. F., & Quataert, E. 2010, MNRAS, 407, 1529, doi: 10.1111/j.1365-2966.2010.17064.x Hopkins, P. F., Grudic, M. Y., Su, K.-Y., et al. 2023, arXiv e-prints, arXiv:2309.13115. https://arxiv.org/abs/2309.13115 Igumenshchev, I. V., & Narayan, R. 2002, ApJ, 566, 137, doi: 10.1086/338077 Igumenshchev, I. V., Narayan, R., & Abramowicz, M. A. 2003, ApJ, 592, 1042, doi: 10.1086/375769 Kaaz, N., Murguia-Berthier, A., Chatterjee, K., Liska, M. T. P., & Tchekhovskoy, A. 2023, ApJ, 950, 31, doi: 10.3847/1538-4357/acc7a1 Komissarov, S. S. 1999, MNRAS, 303, 343, doi: 10.1046/j.1365-8711.1999.02244.x Kormendy, J., & Ho, L. C. 2013, ARA&A, 51, 511, doi: 10.1146/annurev-astro-082708-101811 Lalakos, A., Gottlieb, O., Kaaz, N., et al. 2022, ApJL, 936, L5, doi: 10.3847/2041-8213/ac7bed Li, Y., & Bryan, G. L. 2014, ApJ, 789, 153, doi: 10.1088/0004-637X/789/2/153 Magorrian, J., Tremaine, S., Richstone, D., et al. 1998, AJ, 115, 2285, doi: 10.1086/300353 Meszaros, P. 1975, A&A, 44, 59 Michel, F. C. 1972, Ap&SS, 15, 153, doi: 10.1007/BF00649949 Narayan, R., Chael, A., Chatterjee, K., Ricarte, A., & Curd, B. 2022, MNRAS, 511, 3795, doi: 10.1093/mnras/stac285 Narayan, R., Igumenshchev, I. V., & Abramowicz, M. A. 2000, ApJ, 539, 798, doi: 10.1086/309268—. 2003, PASJ, 55, L69, doi: 10.1093/pasj/55.6.L69 Ni, Y., Di Matteo, T., Bird, S., et al. 2022, MNRAS, 513, 670, doi: 10.1093/mnras/stac351 Pen, U.-L., Matzner, C. D., & Wong, S. 2003, ApJL, 596, L207, doi: 10.1086/379339 Penna, R. F., Kulkarni, A., & Narayan, R. 2013, A&A, 559, A116, doi: 10.1051/0004-6361/201219666 Porth, O., Chatterjee, K., Narayan, R., et al. 2019, ApJS, 243, 26, doi: 10.3847/1538-4365/ab29fd Prather, B. S., Wong, G. N., Dhruv, V., et al. 2021, 6, 3336, doi: 10.21105/joss.03336 Quataert, E., & Gruzinov, A. 2000, ApJ, 539, 809, doi: 10.1086/309267 Ressler, S. M., Quataert, E., White, C. J., & Blaes, O. 2021, MNRAS, 504, 6076, doi: 10.1093/mnras/stab311 Ressler, S. M., White, C. J., Quataert, E., & Stone, J. M. 2020, ApJL, 896, L6, doi: 10.3847/2041-8213/ab9532 Ricarte, A., Tremmel, M., Natarajan, P., & Quinn, T. 2019, MNRAS, 489, 802, doi: 10.1093/mnras/stz2161 Ripperda, B., Liska, M., Chatterjee, K., et al. 2022, ApJL, 924, L32, doi: 10.3847/2041-8213/ac46a1 Rosas-Guevara, Y., Bower, R. G., Schaye, J., et al. 2016, MNRAS, 462, 190, doi: 10.1093/mnras/stw1679 Russell, H. R., Fabian, A. C., McNamara, B. R., & Broderick, A. E. 2015, MNRAS, 451, 588, doi: 10.1093/mnras/stv954 Shapiro, S. L., & Teukolsky, S. A. 1983, Black holes, white dwarfs, and neutron stars : the physics of compact objects Shvartsman, V. F. 1971, Soviet Ast., 15, 377 Sijacki, D., Vogelsberger, M., Genel, S., et al. 2015, MNRAS, 452, 575, doi: 10.1093/mnras/stv1340 Talbot, R. Y., Bourne, M. A., & Sijacki, D. 2021, MNRAS, 504, 3619, doi: 10.1093/mnras/stab804 Tchekhovskoy, A., McKinney, J. C., & Narayan, R. 2012, in Journal of Physics Conference Series, Vol. 372, Journal of Physics Conference Series, 012040, doi: 10.1088/1742-6596/372/1/012040 Tchekhovskoy, A., Narayan, R., & McKinney, J. C. 2011, MNRAS, 418, L79, doi: 10.1111/j.1745-3933.2011.01147.x Trott, C. R., Lebrun-Grandi, D., Arndt, D., et al. 2022, 33, 805, doi: 10.1109/TPDS.2021.3097283 Wang, Q. D., Nowak, M. A., Markoff, S. B., et al. 2013, Science, 341, 981, doi: 10.1126/science.1240755 Weinberger, R., Springel, V., Pakmor, R., et al. 2018, MNRAS, 479, 4056, doi: 10.1093/mnras/sty1733 Weinberger, R., Su, K.-Y., Ehlert, K., et al. 2023, MNRAS, 523, 1104, doi: 10.1093/mnras/stad1396 Wellons, S., Faucher-Gigu`ere, C.-A., Hopkins, P. F., et al. 2023, MNRAS, 520, 5394, doi: 10.1093/mnras/stad511 Wong, G. N., Du, Y., Prather, B. S., & Gammie, C. F. 2021, ApJ, 914, 55, doi: 10.3847/1538-4357/abf8b8 Yuan, F., Gan, Z., Narayan, R., et al. 2015, ApJ, 804, 101, doi: 10.1088/0004-637X/804/2/101 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 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. M., & Schaye, J. 2009, MNRAS, 398, 53, doi: 10.1111/j.1365-2966.2009.15043.x Chatterjee, K., & Narayan, R. 2022, ApJ, 941, 30, doi: 10.3847/1538-4357/ac9d97 Chatterjee, K., Chael, A., Tiede, P., et al. 2023, Galaxies, 11, 38, doi: 10.3390/galaxies11020038 Dav´e, R., Angl´es-Alc´azar, D., Narayanan, D., et al. 2019, MNRAS, 486, 2827, doi: 10.1093/mnras/stz937 Event Horizon Telescope Collaboration, Akiyama, K., Algaba, J. C., et al. 2021, ApJL, 910, L13, doi: 10.3847/2041-8213/abe4de Ferrarese, L., & Merritt, D. 2000, ApJL, 539, L9, doi: 10.1086/312838 Ferri`ere, K. 2020, Plasma Physics and Controlled Fusion, 62, 014014, doi: 10.1088/13616587/ab49eb Fiacconi, D., Sijacki, D., & Pringle, J. E. 2018, MNRAS, 477, 3807, doi: 10.1093/mnras/sty893 Gammie, C. F., McKinney, J. C., & T´oth, G. 2003, ApJ, 589, 444, doi: 10.1086/374594 Gebhardt, K., Bender, R., Bower, G., et al. 2000, ApJL, 539, L13, doi: 10.1086/312840 Grete, P., Dolence, J. C., Miller, J. M., et al. 2023, 37, 465, doi: 10.1177/10943420221143775 Guerra, J. A., Lopez-Rodriguez, E., Chuss, D. T., Butterfield, N. O., & Schmelz, J. T. 2023, AJ, 166, 37, doi: 10.3847/1538-3881/acdacd Guo, M., Stone, J. M., Kim, C.-G., & Quataert, E. 2023, ApJ, 946, 26, doi: 10.3847/1538-4357/acb81e Hopkins, P. F., & Quataert, E. 2010, MNRAS, 407, 1529, doi: 10.1111/j.1365-2966.2010.17064.x Hopkins, P. F., Grudic, M. Y., Su, K.-Y., et al. 2023, arXiv e-prints, arXiv:2309.13115. https://arxiv.org/abs/2309.13115 Igumenshchev, I. V., & Narayan, R. 2002, ApJ, 566, 137, doi: 10.1086/338077 Igumenshchev, I. V., Narayan, R., & Abramowicz, M. A. 2003, ApJ, 592, 1042, doi: 10.1086/375769 Kaaz, N., Murguia-Berthier, A., Chatterjee, K., Liska, M. T. P., & Tchekhovskoy, A. 2023, ApJ, 950, 31, doi: 10.3847/1538-4357/acc7a1 Komissarov, S. S. 1999, MNRAS, 303, 343, doi: 10.1046/j.1365-8711.1999.02244.x Kormendy, J., & Ho, L. C. 2013, ARA&A, 51, 511, doi: 10.1146/annurev-astro-082708-101811 Lalakos, A., Gottlieb, O., Kaaz, N., et al. 2022, ApJL, 936, L5, doi: 10.3847/2041-8213/ac7bed Li, Y., & Bryan, G. L. 2014, ApJ, 789, 153, doi: 10.1088/0004-637X/789/2/153 Magorrian, J., Tremaine, S., Richstone, D., et al. 1998, AJ, 115, 2285, doi: 10.1086/300353 Meszaros, P. 1975, A&A, 44, 59 Michel, F. C. 1972, Ap&SS, 15, 153, doi: 10.1007/BF00649949 Narayan, R., Chael, A., Chatterjee, K., Ricarte, A., & Curd, B. 2022, MNRAS, 511, 3795, doi: 10.1093/mnras/stac285 Narayan, R., Igumenshchev, I. V., & Abramowicz, M. A. 2000, ApJ, 539, 798, doi: 10.1086/309268—. 2003, PASJ, 55, L69, doi: 10.1093/pasj/55.6.L69 Ni, Y., Di Matteo, T., Bird, S., et al. 2022, MNRAS, 513, 670, doi: 10.1093/mnras/stac351 Pen, U.-L., Matzner, C. D., & Wong, S. 2003, ApJL, 596, L207, doi: 10.1086/379339 Penna, R. F., Kulkarni, A., & Narayan, R. 2013, A&A, 559, A116, doi: 10.1051/0004-6361/201219666 Porth, O., Chatterjee, K., Narayan, R., et al. 2019, ApJS, 243, 26, doi: 10.3847/1538-4365/ab29fd Prather, B. S., Wong, G. N., Dhruv, V., et al. 2021, 6, 3336, doi: 10.21105/joss.03336 Quataert, E., & Gruzinov, A. 2000, ApJ, 539, 809, doi: 10.1086/309267 Ressler, S. M., Quataert, E., White, C. J., & Blaes, O. 2021, MNRAS, 504, 6076, doi: 10.1093/mnras/stab311 Ressler, S. M., White, C. J., Quataert, E., & Stone, J. M. 2020, ApJL, 896, L6, doi: 10.3847/2041-8213/ab9532 Ricarte, A., Tremmel, M., Natarajan, P., & Quinn, T. 2019, MNRAS, 489, 802, doi: 10.1093/mnras/stz2161 Ripperda, B., Liska, M., Chatterjee, K., et al. 2022, ApJL, 924, L32, doi: 10.3847/2041-8213/ac46a1 Rosas-Guevara, Y., Bower, R. G., Schaye, J., et al. 2016, MNRAS, 462, 190, doi: 10.1093/mnras/stw1679 Russell, H. R., Fabian, A. C., McNamara, B. R., & Broderick, A. E. 2015, MNRAS, 451, 588, doi: 10.1093/mnras/stv954 Shapiro, S. L., & Teukolsky, S. A. 1983, Black holes, white dwarfs, and neutron stars : the physics of compact objects Shvartsman, V. F. 1971, Soviet Ast., 15, 377 Sijacki, D., Vogelsberger, M., Genel, S., et al. 2015, MNRAS, 452, 575, doi: 10.1093/mnras/stv1340 Talbot, R. Y., Bourne, M. A., & Sijacki, D. 2021, MNRAS, 504, 3619, doi: 10.1093/mnras/stab804 Tchekhovskoy, A., McKinney, J. C., & Narayan, R. 2012, in Journal of Physics Conference Series, Vol. 372, Journal of Physics Conference Series, 012040, doi: 10.1088/1742-6596/372/1/012040 Tchekhovskoy, A., Narayan, R., & McKinney, J. C. 2011, MNRAS, 418, L79, doi: 10.1111/j.1745-3933.2011.01147.x Trott, C. R., Lebrun-Grandi, D., Arndt, D., et al. 2022, 33, 805, doi: 10.1109/TPDS.2021.3097283 Wang, Q. D., Nowak, M. A., Markoff, S. B., et al. 2013, Science, 341, 981, doi: 10.1126/science.1240755 Weinberger, R., Springel, V., Pakmor, R., et al. 2018, MNRAS, 479, 4056, doi: 10.1093/mnras/sty1733 Weinberger, R., Su, K.-Y., Ehlert, K., et al. 2023, MNRAS, 523, 1104, doi: 10.1093/mnras/stad1396 Wellons, S., Faucher-Gigu`ere, C.-A., Hopkins, P. F., et al. 2023, MNRAS, 520, 5394, doi: 10.1093/mnras/stad511 Wong, G. N., Du, Y., Prather, B. S., & Gammie, C. F. 2021, ApJ, 914, 55, doi: 10.3847/1538-4357/abf8b8 Yuan, F., Gan, Z., Narayan, R., et al. 2015, ApJ, 804, 101, doi: 10.1088/0004-637X/804/2/101 This paper is available on Arxiv under CC 4.0 license. This paper is available on Arxiv under CC 4.0 license. Arxiv

All the References That We Used in Our Black Hole Study So You Can Continue to Learn

This paper is available on arxiv under CC 4.0 license.
Authors:
(1) Yici Zhong, Department of Physics, Graduate School of Science, University of Tokyo, Bunkyo-ku, Tokyo 113-0033, Japan;
(2) Kazumi Kashiyama, Research Center for the Early Universe, Graduate School of Science, University of Tokyo, Bunkyo-ku, Tokyo 113-0033, Japan and Kavli Institute for the Physics and Mathematics of the Universe (Kavli IPMU,WPI), The University of Tokyo, Chiba 277-8582, Japan;
(3) Shinsuke Takasao, Department of Earth and Space Science, Graduate School of Science, Osaka University, Toyonaka, Osaka 560-0043, Japan;
(4) Toshikazu Shigeyama, Research Center for the Early Universe (RESCEU), School of Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan and Department of Astronomy, School of Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan;
(5) Kotaro Fujisawa, Research Center for the Early Universe (RESCEU), School of Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan and Department of Liberal Arts, Tokyo University of Technology, Ota-ku, Tokyo 144-0051, Japan. Table of Links Abstract and Intro Setup Result Summary and Discussion Appendix A. Dual Energy Formalism B. Convergence of Results C. Change of the Mass Loss Rate in MHD Regime References REFERENCES Briggs, G. P., Ferrario, L., Tout, C. A., Wickramasinghe, D. T., & Hurley, J. R. 2015, MNRAS, 447, 1713, doi: 10.1093/mnras/stu2539 Bryan, G. L., Norman, M. L., Stone, J. M., Cen, R., & Ostriker, J. P. 1995, Computer Physics Communications, 89, 149, doi: 10.1016/0010-4655(94)00191-4 Bucciantini, N., Thompson, T. A., Arons, J., Quataert, E., & Del Zanna, L. 2006, MNRAS, 368, 1717, doi: 10.1111/j.1365-2966.2006.10217.x Caiazzo, I., Burdge, K. B., Fuller, J., et al. 2021, Nature, 595, 39, doi: 10.1038/s41586-021-03615-y Cheng, S., Cummings, J. D., M´enard, B., & Toonen, S. 2020, ApJ, 891, 160, doi: 10.3847/1538-4357/ab733c Contopoulos, I., & Spitkovsky, A. 2006, ApJ, 643, 1139, doi: 10.1086/501161 Ferreira, J. M. 2000, MNRAS, 316, 647, doi: 10.1046/j.1365-8711.2000.03540.x Garc´ıa-Berro, E., Lor´en-Aguilar, P., Aznar-Sigu´an, G., et al. 2012, ApJ, 749, 25, doi: 10.1088/0004-637X/749/1/25 Gvaramadze, V. V., Gr¨afener, G., Langer, N., et al. 2019, Nature, 569, 684, doi: 10.1038/s41586-019-1216-1 Iben, I., J., & Tutukov, A. V. 1984, ApJS, 54, 335, doi: 10.1086/190932 Iijima, H. 2016, PhD thesis, University of Tokyo, Department of Earth and Planetary Environmental Science Jardine, M., & Collier Cameron, A. 2019, MNRAS, 482, 2853, doi: 10.1093/mnras/sty2872 Kashiyama, K., Fujisawa, K., & Shigeyama, T. 2019, ApJ, 887, 39, doi: 10.3847/1538-4357/ab4e97 Kashiyama, K., Ioka, K., & Kawanaka, N. 2011, PhRvD, 83, 023002, doi: 10.1103/PhysRevD.83.023002 Kashiyama, K., & Murase, K. 2017, ApJL, 839, L3, doi: 10.3847/2041-8213/aa68e1 Kilic, M., Kosakowski, A., Moss, A. G., Bergeron, P., & Conly, A. A. 2021, ApJL, 923, L6, doi: 10.3847/2041-8213/ac3b60 Kirsten, F., Marcote, B., Nimmo, K., et al. 2022, Nature, 602, 585, doi: 10.1038/s41586-021-04354-w Ko, T., Suzuki, H., Kashiyama, K., et al. 2023, arXiv e-prints, arXiv:2304.14669, doi: 10.48550/arXiv.2304.14669 Kremer, K., Piro, A. L., & Li, D. 2021, ApJL, 917, L11, doi: 10.3847/2041-8213/ac13a0 Lu, W., Beniamini, P., & Kumar, P. 2022, MNRAS, 510, 1867, doi: 10.1093/mnras/stab3500 Lykou, F., Parker, Q. A., Ritter, A., et al. 2022, arXiv e-prints, arXiv:2208.03946, doi: 10.48550/arXiv.2208.03946 Matt, S. P., MacGregor, K. B., Pinsonneault, M. H., & Greene, T. P. 2012, ApJL, 754, L26, doi: 10.1088/2041-8205/754/2/L26 Michel, F. C. 1969, ApJ, 158, 727, doi: 10.1086/150233 Miyoshi, T., & Kusano, K. 2005, Journal of Computational Physics, 208, 315, doi: 10.1016/j.jcp.2005.02.017 Nomoto, K., & Iben, I., J. 1985, ApJ, 297, 531, doi: 10.1086/163547 Oskinova, L. M., Gvaramadze, V. V., Gr¨afener, G., Langer, N., & Todt, H. 2020, A&A, 644, L8, doi: 10.1051/0004-6361/202039232 Parker, E. N. 1965, SSRv, 4, 666, doi: 10.1007/BF00216273 Raives, M. J., Coleman, M. S. B., & Thompson, T. A. 2023, arXiv e-prints, arXiv:2302.05462, doi: 10.48550/arXiv.2302.05462 Ritter, A., Parker, Q. A., Lykou, F., et al. 2021, ApJL, 918, L33, doi: 10.3847/2041-8213/ac2253 Saio, H., & Nomoto, K. 2004, ApJ, 615, 444, doi: 10.1086/423976 Schwab, J. 2021, ApJ, 906, 53, doi: 10.3847/1538-4357/abc87e Schwab, J., Quataert, E., & Kasen, D. 2016, MNRAS, 463, 3461, doi: 10.1093/mnras/stw2249 Stone, J. M., Tomida, K., White, C. J., & Felker, K. G. 2020, The Astrophysical Journal Supplement Series, 249, 4, doi: 10.3847/1538-4365/ab929b Takasao, S., Tomida, K., Iwasaki, K., & Suzuki, T. K. 2019, ApJL, 878, L10, doi: 10.3847/2041-8213/ab22bb Tout, C. A., Wickramasinghe, D. T., Liebert, J., Ferrario, L., & Pringle, J. E. 2008, MNRAS, 387, 897, doi: 10.1111/j.1365-2966.2008.13291.x Townsend, R. H. D., & Owocki, S. P. 2005, MNRAS, 357, 251, doi: 10.1111/j.1365-2966.2005.08642.x Ud-Doula, A., Owocki, S. P., & Townsend, R. H. D. 2009, MNRAS, 392, 1022, doi: 10.1111/j.1365-2966.2008.14134.x Webbink, R. F. 1984, ApJ, 277, 355, doi: 10.1086/161701 Weber, E. J., & Davis, Leverett, J. 1967, ApJ, 148, 217, doi: 10.1086/149138 Wu, C., Xiong, H., Lin, J., et al. 2023, ApJL, 944, L54, doi: 10.3847/2041-8213/acb6f3 Yao, P. Z., Quataert, E., & Goulding, A. 2023, MNRAS, doi: 10.1093/mnras/stad1934 This paper is available on arxiv under CC 4.0 license. Authors: (1) Yici Zhong, Department of Physics, Graduate School of Science, University of Tokyo, Bunkyo-ku, Tokyo 113-0033, Japan; (2) Kazumi Kashiyama, Research Center for the Early Universe, Graduate School of Science, University of Tokyo, Bunkyo-ku, Tokyo 113-0033, Japan and Kavli Institute for the Physics and Mathematics of the Universe (Kavli IPMU,WPI), The University of Tokyo, Chiba 277-8582, Japan; (3) Shinsuke Takasao, Department of Earth and Space Science, Graduate School of Science, Osaka University, Toyonaka, Osaka 560-0043, Japan; (4) Toshikazu Shigeyama, Research Center for the Early Universe (RESCEU), School of Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan and Department of Astronomy, School of Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan; (5) Kotaro Fujisawa, Research Center for the Early Universe (RESCEU), School of Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan and Department of Liberal Arts, Tokyo University of Technology, Ota-ku, Tokyo 144-0051, Japan. This paper is available on arxiv under CC 4.0 license. Authors: Authors: (1) Yici Zhong, Department of Physics, Graduate School of Science, University of Tokyo, Bunkyo-ku, Tokyo 113-0033, Japan; (2) Kazumi Kashiyama, Research Center for the Early Universe, Graduate School of Science, University of Tokyo, Bunkyo-ku, Tokyo 113-0033, Japan and Kavli Institute for the Physics and Mathematics of the Universe (Kavli IPMU,WPI), The University of Tokyo, Chiba 277-8582, Japan; (3) Shinsuke Takasao, Department of Earth and Space Science, Graduate School of Science, Osaka University, Toyonaka, Osaka 560-0043, Japan; (4) Toshikazu Shigeyama, Research Center for the Early Universe (RESCEU), School of Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan and Department of Astronomy, School of Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan; (5) Kotaro Fujisawa, Research Center for the Early Universe (RESCEU), School of Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan and Department of Liberal Arts, Tokyo University of Technology, Ota-ku, Tokyo 144-0051, Japan. Table of Links Abstract and Intro Abstract and Intro Setup Setup Result Result Summary and Discussion Summary and Discussion Appendix Appendix A. Dual Energy Formalism A. Dual Energy Formalism B. Convergence of Results B. Convergence of Results C. Change of the Mass Loss Rate in MHD Regime C. Change of the Mass Loss Rate in MHD Regime References References REFERENCES Briggs, G. P., Ferrario, L., Tout, C. A., Wickramasinghe, D. T., & Hurley, J. R. 2015, MNRAS, 447, 1713, doi: 10.1093/mnras/stu2539 Bryan, G. L., Norman, M. L., Stone, J. M., Cen, R., & Ostriker, J. P. 1995, Computer Physics Communications, 89, 149, doi: 10.1016/0010-4655(94)00191-4 Bucciantini, N., Thompson, T. A., Arons, J., Quataert, E., & Del Zanna, L. 2006, MNRAS, 368, 1717, doi: 10.1111/j.1365-2966.2006.10217.x Caiazzo, I., Burdge, K. B., Fuller, J., et al. 2021, Nature, 595, 39, doi: 10.1038/s41586-021-03615-y Cheng, S., Cummings, J. D., M´enard, B., & Toonen, S. 2020, ApJ, 891, 160, doi: 10.3847/1538-4357/ab733c Contopoulos, I., & Spitkovsky, A. 2006, ApJ, 643, 1139, doi: 10.1086/501161 Ferreira, J. M. 2000, MNRAS, 316, 647, doi: 10.1046/j.1365-8711.2000.03540.x Garc´ıa-Berro, E., Lor´en-Aguilar, P., Aznar-Sigu´an, G., et al. 2012, ApJ, 749, 25, doi: 10.1088/0004-637X/749/1/25 Gvaramadze, V. V., Gr¨afener, G., Langer, N., et al. 2019, Nature, 569, 684, doi: 10.1038/s41586-019-1216-1 Iben, I., J., & Tutukov, A. V. 1984, ApJS, 54, 335, doi: 10.1086/190932 Iijima, H. 2016, PhD thesis, University of Tokyo, Department of Earth and Planetary Environmental Science Jardine, M., & Collier Cameron, A. 2019, MNRAS, 482, 2853, doi: 10.1093/mnras/sty2872 Kashiyama, K., Fujisawa, K., & Shigeyama, T. 2019, ApJ, 887, 39, doi: 10.3847/1538-4357/ab4e97 Kashiyama, K., Ioka, K., & Kawanaka, N. 2011, PhRvD, 83, 023002, doi: 10.1103/PhysRevD.83.023002 Kashiyama, K., & Murase, K. 2017, ApJL, 839, L3, doi: 10.3847/2041-8213/aa68e1 Kilic, M., Kosakowski, A., Moss, A. G., Bergeron, P., & Conly, A. A. 2021, ApJL, 923, L6, doi: 10.3847/2041-8213/ac3b60 Kirsten, F., Marcote, B., Nimmo, K., et al. 2022, Nature, 602, 585, doi: 10.1038/s41586-021-04354-w Ko, T., Suzuki, H., Kashiyama, K., et al. 2023, arXiv e-prints, arXiv:2304.14669, doi: 10.48550/arXiv.2304.14669 Kremer, K., Piro, A. L., & Li, D. 2021, ApJL, 917, L11, doi: 10.3847/2041-8213/ac13a0 Lu, W., Beniamini, P., & Kumar, P. 2022, MNRAS, 510, 1867, doi: 10.1093/mnras/stab3500 Lykou, F., Parker, Q. A., Ritter, A., et al. 2022, arXiv e-prints, arXiv:2208.03946, doi: 10.48550/arXiv.2208.03946 Matt, S. P., MacGregor, K. B., Pinsonneault, M. H., & Greene, T. P. 2012, ApJL, 754, L26, doi: 10.1088/2041-8205/754/2/L26 Michel, F. C. 1969, ApJ, 158, 727, doi: 10.1086/150233 Miyoshi, T., & Kusano, K. 2005, Journal of Computational Physics, 208, 315, doi: 10.1016/j.jcp.2005.02.017 Nomoto, K., & Iben, I., J. 1985, ApJ, 297, 531, doi: 10.1086/163547 Oskinova, L. M., Gvaramadze, V. V., Gr¨afener, G., Langer, N., & Todt, H. 2020, A&A, 644, L8, doi: 10.1051/0004-6361/202039232 Parker, E. N. 1965, SSRv, 4, 666, doi: 10.1007/BF00216273 Raives, M. J., Coleman, M. S. B., & Thompson, T. A. 2023, arXiv e-prints, arXiv:2302.05462, doi: 10.48550/arXiv.2302.05462 Ritter, A., Parker, Q. A., Lykou, F., et al. 2021, ApJL, 918, L33, doi: 10.3847/2041-8213/ac2253 Saio, H., & Nomoto, K. 2004, ApJ, 615, 444, doi: 10.1086/423976 Schwab, J. 2021, ApJ, 906, 53, doi: 10.3847/1538-4357/abc87e Schwab, J., Quataert, E., & Kasen, D. 2016, MNRAS, 463, 3461, doi: 10.1093/mnras/stw2249 Stone, J. M., Tomida, K., White, C. J., & Felker, K. G. 2020, The Astrophysical Journal Supplement Series, 249, 4, doi: 10.3847/1538-4365/ab929b Takasao, S., Tomida, K., Iwasaki, K., & Suzuki, T. K. 2019, ApJL, 878, L10, doi: 10.3847/2041-8213/ab22bb Tout, C. A., Wickramasinghe, D. T., Liebert, J., Ferrario, L., & Pringle, J. E. 2008, MNRAS, 387, 897, doi: 10.1111/j.1365-2966.2008.13291.x Townsend, R. H. D., & Owocki, S. P. 2005, MNRAS, 357, 251, doi: 10.1111/j.1365-2966.2005.08642.x Ud-Doula, A., Owocki, S. P., & Townsend, R. H. D. 2009, MNRAS, 392, 1022, doi: 10.1111/j.1365-2966.2008.14134.x Webbink, R. F. 1984, ApJ, 277, 355, doi: 10.1086/161701 Weber, E. J., & Davis, Leverett, J. 1967, ApJ, 148, 217, doi: 10.1086/149138 Wu, C., Xiong, H., Lin, J., et al. 2023, ApJL, 944, L54, doi: 10.3847/2041-8213/acb6f3 Yao, P. Z., Quataert, E., & Goulding, A. 2023, MNRAS, doi: 10.1093/mnras/stad1934

Magnetic Wind From a Massive White Dwarf Merger: References

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

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The TechBeat: Mastering the Art of Load Testing for Web Applications (12/5/2023)

Speed Up Your Process With Artificial Intelligence As a writer, I know all too well the struggles of writer’s block and the tedious task of editing. That’s why I’ve scoured the internet and put together a list of the top 10 AI tools for content creation that can expedite your writing process. Here are my top picks, each with a short descriptor, price points, and key features: Articles,Blogs,Copy Articoolo: AI-Powered Content Creation Tool **Pricing:**Starts at $20 per month Generates high-quality articles in seconds, allows customization, integrates with popular content management systems (CMS) Key features: Chat GPT- OpenAI: State-of-the-art Language Generation Model **Pricing:** Not available for individual purchase, requires integration with other platforms. Can generate articles, summaries, conversation, and more, highly customizable, large knowledge base. Key features: Chat GPT has to be my favourite so far as I find it the most customizable and versatile. In fact, if you know how to prompt it correctly, it can deliver a lot of what these other applications can. Copy.ai: AI Content Generation Tool Starts at $50 per month. Pricing: Generates headlines, product descriptions, blog posts, and more, integrates with popular CMS and marketing tools Key features: Wordsmith: Natural Language Generation Tool Pricing not publicly available, requires contact for quote. Pricing: Generates reports, summaries, and more, highly customizable, can integrate with existing workflows. Key features: Infill: AI Writing Tool Free with limited features, premium plans starting at $15 per month. Pricing: : Offers suggestions for words and phrases, saves writing time, integrates with popular CMS and note-taking apps. Key features Rasa NLU: Open-source AI Tool for Natural Language Processing : Free, open source. Pricing Can be used for chatbots, language modeling, and more, highly customizable, integrates with existing workflows Key features: Graphics Craiyon: free AI art generator Price: Free Key features: Straightforward interface, unlimited prompts. Midjourney: AI Art Generator and queries (e.g., variations, upscales). Pricing: free for the first 25 prompts Basic membership — $10 USD per month Limited use (~200 images/month) General commercial terms** Incremental billing after limits (optional) Access to the member gallery Standard membership — $30 USD per month Unlimited personal use* General commercial terms** Access to the member gallery Video Content Video content is a must-have for businesses and content creators wanting to compete in this highly visual environment. Reports have shown that more than 80% of online traffic is video traffic, and an increasing amount of people prefer it over other forms of online content like text and images. Pictory: AI Video Generator Helps you easily create and edit high-quality videos. Price point: Free trial, starts at $19/month. Key features: Video based on articles or scripts. Edit videos using text. Create shareable video highlight reels. Automatically caption and summarize video Synthesys: Text-to-Voiceover and Videos for Commercial Use Creators and companies can use Synthesys to create videos with lip-syncing AI video technology. There is no need for cameras or film crews. All you have to do is choose an avatar and type your script in one of 66+ available languages, and the tool will produce a high-quality video. $39 per month. Pricing: Key features: 74 real Humatars. 66+ languages and 254 unique styles. Excellent tool for explain. Final Thoughts These AI tools for content creation can help you save time and enhance the quality of your writing. But, I still think that ChatGPT can do the most for the least amount of effort and will thus continue to use ChatGPT. These tools are probably pretty good too, but because of the upfront capital, and well, ChatGPT completely ruining the market, we’re spoilt. Great news for ChatGPT, bad news for other people. ChatGPT’s pricing and marketing strategy was simple: make a product so good that people have no choice but to pay for it. It might be fair to consider that they had lots of budget- not all MVPs can do this, and it is fair that users pay for a product that they use to make them money. I’m not sure how AI will disrupt the world, but I do know that people who take a little more effort to put things together, learn how to use things, and adapt with technology tend to fare a little better in the changing of times. For more ChatGPT Tools, read . For a guide to prompting ChatGPT as a content creator, read . this this Till next time! First published here

9 Non-ChatGPT AI Tools for Content Creation

There has been a lot of talk about chatbots these days. You come across them quite often in Facebook Messenger. In fact, right now, there are more than on Facebook Messenger. For a technology that has gained momentum just a few years ago, this number is huge. 300,000 active chatbots Not only on Facebook Messenger, chatbots are also showing up on a lot of business websites. That’s because the barriers that prevented people from using chatbots are breaking down. According to a recent customers are becoming increasingly comfortable interacting with chatbots. Only 38% of people said that they prefer to talk to humans when engaging with a brand in 2019, compared to 43% of people who said the same last year. survey by Drift, The remaining 62% of consumers are happy and satisfied with chatbot support. Why are Chatbots So Popular? One of the primary reasons behind this surge in the popularity of chatbots is the development of AI-powered chatbots. AI-powered chatbots are easy to build and can interact with humans with astonishing accuracy. Chatbots are bringing in significant structural changes in many organizations. They are proving to be an integral part of sales and customer support. They empower businesses to provide excellent customer service around the clock without the need to hire additional employees. In fact, chatbots are now being deployed in almost all aspects of customer support. People like to use chatbots for a variety of purposes, including: Image via Drift Benefits of Using Chatbots for Your Business Let’s take a look at some of the and how they can contribute to the growth of your business. benefits of using chatbots 1. Using Chatbots is Better and Cheaper Than Building a New App A users actively use social media apps such as Facebook, Instagram, and Whatsapp. They like the comfort of using these apps and are very less likely to replace them with any new apps. majority of smartphone Therefore, integrating chatbots into one of the platforms that your consumers already use is better and cheaper than building a new app. For example, Lyft, a ride-sharing app, allows you to request a ride via a chatbot integrated into Facebook Messenger. The chatbot also updates you about the vehicle number and the current location of the driver. Image source: Social Media Today 2. Chatbots Can Provide Extensive Customer Support Shopping online may seem easy but it isn’t always. Many online shoppers need assistance while shopping. From dealing with navigation issues to seeking help during registration, payment, and checkout processes, your customers may need support. In such situations, chatbots can assist them just like a salesperson would if they were in a retail store. Along with solving basic customer issues, chatbots can also ask questions to understand the buying preferences of your customers and recommend relevant products to them accordingly. The best part about using chatbots is that they can provide round-the-clock customer support. You don’t need to hire more executives as chatbots can handle multiple requests at one time. This ensures that your customers don’t have to wait very long. customer support Also, these bots can be programmed to give predefined answers to common questions. They can also forward requests to a real human in case a customer asks any complicated question. Starbucks’ chatbot is an amazing example. The bot makes it extremely easy for customers to place an order using either voice command or text messaging. The chatbot also tells you the amount of time required for your order to be ready and the total bill amount. And if you want to repeat your past order, all you have to do is to just ask the bot. Image source: GeekWire 3. Chatbots are Cost-Effective Integrating a chatbot is cheaper and easier than creating a cross platform app or hiring people to perform each task manually. Since chatbots are automated programs, they can handle multiple customer requests at once. This means that you can save on repetitive employee costs by investing in a chatbot. Using chatbots can also enable you to prevent issues caused due to human errors. 4 Steps to Make a Chatbot for Your Business If you are wondering how to make a chatbot with minimal or no coding knowledge, you shouldn’t worry. There are various chatbot platforms such as , , and that let you create a customized chatbot without any coding knowledge. Aivo ManyChat Botsify You can follow this guide to learn the step-by-step process to make a chatbot as a beginner. Step 1: Understand Your Goal Behind Making the Chatbot Before you start to make a chatbot, it is important to understand what you want your chatbot to do. Determining this can help you select the right chatbot platform for your business. This information will also guide you to create conversations that can address your end goals. For example, if your end goal is to engage your customers until a real human takes over, you can design a simple chatbot with some predefined text. You don’t need to leverage artificial intelligence for this. However, if your end goal is to pitch products and engage customers to increase sales, then you need to make an AI-powered chatbot. Your chatbot should have the ability to deliver the right solutions to a variety of questions that your customers may ask. A brilliant example of this is Dom, Domino’s interactive chatbot integrated with Facebook Messenger. Dom allows you to place orders and repeat previous orders without you having to create an account. You can place an order from the entire restaurant menu without having to leave the comfort of your favorite app. Image source – Digital Marketing Institute Step 2: Examine the Communication Habits of Your Customers They way your customers interact with your business serves as a primary factor for deciding how you should design and develop your chatbot conversations. To understand how your customers interact with your business, you should answer the questions below: Which communication channels do your customers use the most? Do they contact you via chat, phone calls, emails, or any social media channels?What kind of questions do they usually ask? How often do your customers contact you? These customer interaction habits will put you in a better place to set up effective conversations for your chatbot through integration with the out there, which can lead to more automatic conversations. best VoIPs Step 3: Design a Chatbot Conversation Chatbot conversations need to be accurate, interesting, and engaging. The way your chatbot communicates is the differentiating factor that decides the quality of customer engagement it can deliver. Due to advancements in natural language processing (NLP), chatbots are now able to understand user intent. They are emerging as intelligent, conversation-driven, virtual assistants for businesses. Image Source – IMPACT Broadly, chatbot conversations can be divided into two segments: Structured Interaction Structured interaction is a logical flow of information that works similar to the FAQs section on a website. It takes menus, choices, and forms into account and can be designed easily based on your analysis. In this case, you collect commonly-asked questions from your customers and then set their answers in your channel. Unstructured Interaction This involves freestyle conversation such as one that may happen with family, colleagues, and friends. Developing a script for unstructured interactions mainly depends on AI. AI uses natural language processing (NLP) to find the intent of the communication and then provide suitable responses. Designing chatbot conversations is not only about providing correct responses but also about creating a conversational user interface. It is important to keep the conversation topics close to your goals. To meet your end goals, designers need to develop both close-ended and open-ended chatbot conversations. Close-ended conversations are for customers who want specific answers whereas open-ended conversations are for customers who are looking for assistance and natural conversations. Step 4: Choose Between Non-Coding and Code-Based Framework If your product is not extremely complicated and you are clear about what your visitors expect, you can create your chatbot using a non-coding framework. There are many chatbot applications which allow you to make a chatbot using a simple drag-and-drop builder. You can design simple conversations and upload them to a cloud-based platform and you are done. However, if your product is extremely complicated and you want your chatbot to handle unstructured conversations, you need to use a code-based framework. This kind of conversation is usually not addressed by default templates. You may need advanced knowledge of programming languages, machine learning, and AI. Also, if you want to store and analyze customer data, making a code-based chatbot will be your best bet. Design Elements to Use in Chatbots In order to ensure that the chatbot offers a smooth and interactive user experience, it is important to include the right design elements. Of course, the design elements will depend on the platform you choose to build your bot on. Let’s take a look at the top design elements you can use when you make your chatbot. 1. Buttons Buttons enable users to take action. When a user clicks any button, it prompts an action. It is a good idea to include interactive buttons to help users make decisions faster. You can add buttons to ask users to choose one of the multiple options you have to offer. Image source: Chatbots Magazine You can also add a “Get Started” button to encourage users to start a conversation. 2. Cards If there is any additional information (apart from the conversation) that you want to serve users, you can leverage cards. You can enter text, links, images, and buttons into cards which the user can view as a side column during their conversation. Users can click the cards relevant to them or they can choose to hide them. Image source: Zumata 3. Smart Reply Smart reply is a feature that allows users to respond to questions asked by the chatbot without actually typing anything. You can use this design element when you can give a list of available options to the user. This can make it easier for users to interact with your chatbot. Gmail’s smart reply is a brilliant example of this. In addition to offering three responses to choose from, Gmail provides you an option to auto-complete your sentences without typing anything. The GIF below shows how this smart reply feature simplifies the lives of Gmail users. GIF source: Google Blog 4. Persistent Menu Users can opt for this feature when they want to browse another feature of your chatbot that is not otherwise readily available. Tips to Make a Chatbot That Has an Engaging Personality If you want to make a chatbot that users would love to interact with, you should keep a few points in mind. 1. The Chatbot Should Be Able to Direct Users To give continuity and direction to any conversation, a chatbot should ask questions that are relevant to the conversation. They should also offer options that are easy to select with a “tap.” If a user does not know what they want, the chatbot should be able to provide directional cues to help them. For example, instead of asking the user what they would like to do, the bot can give a list of things it can do for the user. This way, the user can easily figure out all of the options that the bot has for them. 2. The Chatbot Should Connect with Users on a Personal Level You should make a chatbot that effectively incorporates personalization. Your bot should address users by their names, greet them when the conversation begins, and give personalized suggestions based on previous conversations. Adding a personalized in your chatbot conversations can help you break the ice and give your users a feeling that they are talking to a real human. 3. The Chatbot Should Use Fillers for Time Gaps If the bot needs a certain amount of time to process a request, it is a good idea to use fillers to keep the user engaged for that time. If you leave a user waiting, it can break the flow of the conversation and, in the worst case, the user may also leave the conversation. It is a good idea to use random trivia facts as fillers during conversations. 4. The Chatbot Should Understand Emotions The capability to include emotions in chat is what makes humans different. If you want to make your chatbots more human-like, it is important that they are able to understand the emotions behind a conversation and respond accordingly. If a user accomplishes a task, your chatbot should respond with a cheerful reply. Similarly, if the chatbot can’t help a user the right way, it should be able to convey an apology for the inconvenience caused. A good idea is to use emoticons. You should try to integrate the most basic and commonly used emoticons in your chatbot conversations. Image source: Singularika Conclusion Today, when customers seek instant responses to their queries, chatbots are a popular solution. They are already all over Facebook Messenger and are steadily making their way to other messaging mediums as well. What’s interesting is that users have grown fond of chatbots and they don’t mind interacting with them to improve their shopping experiences. The real challenge, though, lies in building a chatbot that is engaging, interactive, and is able to provide real value to users. With easy-to-use , making a chatbot is now quick and hassle-free. And with unprecedented technological advancements in artificial intelligence and machine learning, the future of chatbots looks bright. chatbot platforms This article published on Shane Barker ’s blog. About The Author is the Founder and CEO of and . You can connect with him on , , , and . Shane Barker Content Solutions Gifographics Twitter Facebook LinkedIn Instagram

The Beginner’s Guide to Making a Chatbot

Introduction Making the cluster highly available and minimizing downtime is fundamental for any modern service. In the context of , achieving high availability involves the deployment and distribution of resources across various levels within the infrastructure: Azure Kubernetes Service Regions. Creating multiple clusters in different Azure regions is the most expensive yet reliable option. Zones. Availability zones can be used to improve single-cluster availability. Nodes. At this level, we can distribute workload between virtual machines. In this article, I’m going to focus on zone resiliency. In Azure Kubernetes Service (AKS), zone resiliency plays a crucial role when we need to achieve high availability of applications. Before diving deeper into networking and storage aspects of AKS zone resiliency, I’d like to touch upon some common terminology. Availability zone I could invent my definition here, but in fact, availability zones are best described in the Microsoft documentation: Azure availability zones are physically and logically separated datacenters with their own independent power source, network, and cooling. Connected with an extremely low-latency network, they become a building block to delivering high availability applications. Availability zones ensure that if there's an event impacting a datacenter site—for example, if someone cuts the power, or there are issues with cooling—your data will be protected. In short, availability zones operate independently, ensuring that a failure in one zone does not affect the others. Such isolation significantly enhances fault tolerance and application availability. Not all regions have the same number of availability zones: while most have three zones, some have two or even one. Control plane A Kubernetes cluster consists of two main parts—the control plane and the nodes. Both components can use multi-zone availability. The control plane is automatically distributed among zones. I found two different statements in Azure documentation on how this distribution works: It’s distributed between all available regions. It’s distributed between zones you have nodes in. From a practical point of view, these approaches have no big difference because, normally, we will have nodes in all the zones. Node pools We can specify zone parameters when creating a Kubernetes cluster. This would affect the system node pool only. az aks create … --zones You need to specify zone parameters for every zone you create. az aks nodepool add … --zones The most obvious option is always to select all three zones for each node pool. It works fine in most cases, but some limitations related to networking and storage may arise. Let’s take a closer look at each type of limitation. Zones and networking Let’s say one of our pods needs access to the internet. There are several ways we can configure outbound networking in AKS: Load Balancer NAT Gateway User Defined Routing The last option can be implemented in several different ways: you can route your traffic to that would be zone-redundant, or you can route your traffic to a single VM that wouldn’t be zone-redundant. We’re not going to cover all possible options in this article. Azure Firewall Using multiple zones per node pool works fine with the Load Balancer option because Azure Load Balancer is a zone-redundant resource. Yet, it doesn’t work that well with the NAT gateway. Virtual network in Azure is zone agnostic, meaning that it exists in each zone, and it’s safe to use one virtual network (or virtual network subnet) in multizone scenarios. However, the NAT gateway is always deployed in one zone, meaning that even if nodes are distributed among the zones, they are still connected to a single virtual network subnet and a NAT in a single zone. Unfortunately, it’s not possible to connect multiple NATs to one subnet. to overcome this limitation is to create three node pools instead of one and assign one node pool to one zone. The solution In this configuration, you must connect each node pool to a separate virtual network subnet. The configuration with NAT is more complicated, so there should be a solid reason to choose it. To understand why it can be beneficial to use a NAT gateway, we need to dive deeper into the outbound traffic architecture in Azure. According to Azure , there are four outbound traffic options. documentation Here, we can see that the load balancer is production-grade; however, NAT is the best. The reason for that is very well described in the section of Azure documentation titled And below. What are SNAT ports? In brief, when you have only a small subset of nodes that actively use outbound connectivity, you can face port , meaning that new outbound connections will fail. For example, you have a pod with a replication factor of 5 and 120 nodes in a cluster. Each pod replica can open about 500 connections to the database simultaneously. All other pods in a cluster don’t actively use outbound connectivity, and we have only one IP address assigned to a load balancer. exhaustion In this configuration, each node could create only 256 connections to the same destination IP and port because all the ports have been statically allocated among all the nodes. So, only 256 connections out of the required 500 would be opened. The exact configuration with the NAT gateway will be able to establish all necessary connections because the port would be allocated dynamically based on actual demand. One IP address gives us 64000 ports, and the demand is 500*5=2500, which is more than enough. This can be a problem even for relatively small but highly loaded clusters. For a cluster with 1 to 50 nodes, only 1024 ports would be allocated to each node. Of course, if all the pods and nodes use outbound connectivity in the same way, static distribution is not an issue, and you need to add more public IP addresses to the load balancer or NAT. Here is the full PowerShell script to create the described configuration: # Variables
$RESOURCE_GROUP="dt-test-cluster"
$LOCATION="westus2"
$VNET_NAME="dt-vnet"
$SUBNET1_NAME="dt-subnet1"
$SUBNET2_NAME="dt-subnet2"
$NAT_GW1_NAME="dt-natgw1"
$NAT_GW2_NAME="dt-natgw2"
$PIP1_NAME="dt-pip-natgw1"
$PIP2_NAME="dt-pip-natgw2"
$AKS_CLUSTER_NAME="dt-aks-cluster"
$NODE_POOL1_NAME="dtnodepool1"
$NODE_POOL2_NAME="dtnodepool2"

# Create Resource Group
az group create --name $RESOURCE_GROUP --location $LOCATION

# Create Virtual Network and Adjusted Subnets
az network vnet create --resource-group $RESOURCE_GROUP --name $VNET_NAME --address-prefixes 10.1.0.0/16 --subnet-name $SUBNET1_NAME --subnet-prefix 10.1.2.0/24
az network vnet subnet create --resource-group $RESOURCE_GROUP --vnet-name $VNET_NAME --name $SUBNET2_NAME --address-prefix 10.1.3.0/24

# Create Public IPs for NAT Gateways
$publicIP1 = az network public-ip create --resource-group $RESOURCE_GROUP --name $PIP1_NAME --location $LOCATION --sku Standard --zone 1 --query publicIp.id --output tsv
$publicIP2 = az network public-ip create --resource-group $RESOURCE_GROUP --name $PIP2_NAME --location $LOCATION --sku Standard --zone 2 --query publicIp.id --output tsv

# Create NAT Gateways in different zones and associate them with the subnets
az network nat gateway create --resource-group $RESOURCE_GROUP --name $NAT_GW1_NAME --location $LOCATION --zone 1 --public-ip-addresses $publicIP1
az network nat gateway create --resource-group $RESOURCE_GROUP --name $NAT_GW2_NAME --location $LOCATION --zone 2 --public-ip-addresses $publicIP2
az network vnet subnet update --resource-group $RESOURCE_GROUP --vnet-name $VNET_NAME --name $SUBNET1_NAME --nat-gateway $NAT_GW1_NAME
az network vnet subnet update --resource-group $RESOURCE_GROUP --vnet-name $VNET_NAME --name $SUBNET2_NAME --nat-gateway $NAT_GW2_NAME

# Create AKS Cluster with two node pools in different zones and assign subnets
$subnet1Id = "/subscriptions/$(az account show --query id --output tsv)/resourceGroups/$RESOURCE_GROUP/providers/Microsoft.Network/virtualNetworks/$VNET_NAME/subnets/$SUBNET1_NAME"
$subnet2Id = "/subscriptions/$(az account show --query id --output tsv)/resourceGroups/$RESOURCE_GROUP/providers/Microsoft.Network/virtualNetworks/$VNET_NAME/subnets/$SUBNET2_NAME"

az aks create --resource-group $RESOURCE_GROUP --name $AKS_CLUSTER_NAME --node-count 1 --enable-addons monitoring --generate-ssh-keys --nodepool-name $NODE_POOL1_NAME --vnet-subnet-id $subnet1Id --zones 1 --outbound-type userAssignedNATGateway
az aks nodepool add --resource-group $RESOURCE_GROUP --cluster-name $AKS_CLUSTER_NAME --name $NODE_POOL2_NAME --node-count 1 --vnet-subnet-id $subnet2Id --zones 2 Please note that this script is not production-ready because only the parameters related to the outbound networking were configured. Once the configuration is created, you can test it with the following commands: az aks get-credentials --resource-group dt-test-cluster --name dt-aks-cluster
kubectl run ubuntu --image=ubuntu -- sleep infinity
kubectl run ubuntu --image=ubuntu2 -- sleep infinity Then, log in to both of the images with: kubectl exec -it ubuntu -- /bin/sh And run apt-get update
apt-get install -y curl
curl http://httpbin.org/ip As we have two node pools and don’t have an affinity, the scheduler will distribute pods between different node pools. So, the first curl should give you one of the public IPs and the second — another IP. Zones and storage Another potential pitfall is storage. The most common and convenient way to persist data in Kubernetes is to use a storage class and persistent volume claim. In Azure, several built-in storage classes, such as Azure Files or , support network remote storage. In addition, several storage classes support the so-called Managed Drive, which can be treated as VM’s local drive. Azure Blob Storage Let’s say we created a persistent volume claim (PVC) on the Azure default storage classes: .
We can use the following command to understand what Azure storage we actually use: managed-csi-premium kubectl describe sc managed-csi-premium
Name:              	managed-csi-premium
IsDefaultClass:    	No
Annotations:       	<none>
Provisioner:       	disk.csi.azure.com
Parameters:        	skuname=Premium_LRS
AllowVolumeExpansion:  True
MountOptions:      	<none>
ReclaimPolicy:     	Delete
VolumeBindingMode: 	WaitForFirstConsumer
Events:            	<none> This class connects Managed Drive to a node VM. We can understand this because provisioner is used. The storage is . This disk has local redundancy, meaning your data is stored only in one region. disk.csi.azure.com Premium_LRS Let’s imagine that something happened to VMs in Zone 1, but the storage in Zone 1 is unaffected. In this case, we will also lose access to the data because cross-zone disc connections are not supported in Azure. The best approach to avoid such an issue is to use ZRS or zone redundant storage. For this type of storage, Azure synchronously replicates every write operation across all the zones, so we always have three up-to-date replicas. There is no such storage class by default, so we need to create one. kind: StorageClass
apiVersion: storage.k8s.io/v1
metadata:
name: test-for-article
provisioner: disk.csi.azure.com
parameters:
skuname: Premium_ZRS
allowVolumeExpansion: true
reclaimPolicy: Delete
volumeBindingMode: WaitForFirstConsumer The storage class we used mounted a managed disk to a virtual machine. We can use other provisioners, for example, , to mount a file share. For such provisioners, data can be accessed from any zone. file.csi.azure.com For the provisioner that mounts a managed disk to a virtual machine, we can choose one of the following SKUs: disk.csi.azure.com Premium_LRS Premium_ZRS Standard_LRS StandardSSD_LRS StandardSSD_ZRS UltraSSD_LRS PremiumV2_LRS As you can see, we have LRS and ZRS options. For the that mounts a file share in the Azure Storage Account, there are three additional : file.csi.azure.com SKUs available Standard_GRS: Standard geo-redundant storage Standard_RAGRS: Standard read-access geo-redundant storage Standard_RAGZRS: Standard read-access geo-zone-redundant storage These three options replicate data in different regions. It can be useful in case of problems with the whole region, but the failover is manual. It may cause some data losses because of the asynchronous nature of the cross-region replication. Conclusion Zone resiliency in AKS is pivotal for ensuring the high availability of applications. When addressing AKS zone resiliency, you should consider the following limitations and best practices: Networking Azure Load Balancer is inherently zone-redundant, facilitating seamless deployment across multiple zones. On the contrary, the NAT gateway, which provides the best outbound traffic characteristics, is zone-specific. Implementing it requires creating separate node pools for each zone and tying each to a unique virtual network subnet. Storage The best approach to persisting data in the AKS zone resilience cluster is to use zone redundant storage. You may need to create custom storage classes to get zone redundant storage. While AKS offers various tools to ensure zone resiliency, it is important to consider which approaches will suit your cluster's technical characteristics and requirements best. Properly configuring node pools, understanding the intricacies of outbound traffic, and employing zone-redundant storage solutions can help achieve a genuinely resilient AKS deployment.

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