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
3.2 Line detection
The CurtainsF4F step gives us a set of stars which produce an over-density in the feature space. We still need to filter out the overdensities that are particularly line like, as we are interested in stellar streams. We employ a well known line finding algorithm to estimate the line parameters of the stream via the Hough transform, as was done in (Shih et al. 2021, 2023). Since we do not apply a fiducial cut to eliminate stars outside a 10◦ radius, we can use the full set of stars that pass the CurtainsF4F cut in the patch to estimate the line parameters.
For a given star located at (𝜙′, 𝜆′), the Hough transform is a mapping from the 𝜙-𝜆 space to the parameter space 𝜌 − 𝜃, defined as:
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