Table of Links
3. SkyCURTAINs Method and 3.1 CurtainsF4F
5. Conclusion, Acknowledgments, Data Availability, and References
APPENDIX A: CurtainsF4F TRAINING AND HYPERPARAMETER TUNING DETAILS
A1. CurtainsF4F features preprocessing
A1 CurtainsF4F features preprocessing
The first step in training the CurtainsF4F model is to define the SR and SB regions in 𝜇𝜆. In this work, we chose the SR in a given patch to ensure that the GD-1 stream is fully contained. The SB region, defined as the region adjacent to the SR is chosen to be ∼ 6 mas/yr wide. This ensures sufficient training statistics for the base and the top flow model. The features used for training the CurtainsF4F model are: [𝜙, 𝜆, 𝐺, 𝐺BP − 𝐺RP, 𝜇 ∗ 𝜙 ] and the conditional feature is 𝜇𝜆. As these features have different dynamic ranges, we opt to further scale them to ensure a stable model training. All features are first scaled to be in the range [0, 1]. The 𝐺 feature has a sharp cutoff at 20.2 which proves to be a difficult feature for generative models to learn. To mitigate this, we apply a logit transformation to the 𝐺 feature. The logit transformation is defined as:
Finally, all features are scaled to be in the range [−3, 3].
The data for the base and top flow training is divided into training and validation sets in a 80 : 20 ratio.
Authors:
(1) Debajyoti Sengupta, Département de physique nucléaire et corpusculaire, University of Geneva, Switzerland ([email protected]);
(2) Stephen Mulligan, Département de physique nucléaire et corpusculaire, University of Geneva, Switzerland;
(3) David Shih, NHETC, Dept. of Physics and Astronomy, Rutgers, Piscataway, NJ 08854, USA;
(4) John Andrew Raine,, Département de physique nucléaire et corpusculaire, University of Geneva, Switzerland;
(5) Tobias Golling, Département de physique nucléaire et corpusculaire, University of Geneva, Switzerland.
This paper is
