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arxiv: 2208.04499 · v1 · pith:YI3ISJYZnew · submitted 2022-08-09 · 🌌 astro-ph.IM · astro-ph.GA· astro-ph.HE· astro-ph.SR

Rubin Observatory LSST Transients and Variable Stars Roadmap

Kelly M. Hambleton , Federica B. Bianco , Rachel Street , Keaton Bell , David Buckley , Melissa Graham , Nina Hernitschek , Michael B. Lund
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Elena Mason Joshua Pepper Andrej Prsa Markus Rabus Claudia M. Raiteri Robert Szabo Paula Szkody Igor Andreoni Simone Antoniucci Barbara Balmaverde Eric Bellm Rosaria Bonito Giuseppe Bono Maria Teresa Botticella Enzo Brocato Katja Bucar Bricman Enrico Cappellaro Maria Isabel Carnerero Ryan Chornock Riley Clarke Phil Cowperthwaite Antonino Cucchiara Filippo D'Ammando Kristen C. Dage Massimo Dall'Ora James R. A. Davenport Domitilla de Martino Giulia de Somma Marcella Di Criscienzo Rosanne Di Stefano Maria Drout Michele Fabrizio Giuliana Fiorentino Poshak Gandhi Alessia Garofalo Teresa Giannini Andreja Gomboc Laura Greggio Patrick Hartigan Markus Hundertmark Elizabeth Johnson Michael Johnson Tomislav Jurkic Somayeh Khakpash Silvio Leccia Xiaolong Li Davide Magurno Konstantin Malanchev Marcella Marconi Raffaella Margutti Silvia Marinoni Nicolas Mauron Roberto Molinaro Anais Moller Marc Moniez Tatiana Muraveva Ilaria Musella Chow-Choong Ngeow Andrea Pastorello Vincenzo Petrecca Silvia Piranomonte Fabio Ragosta Andrea Reguitti Chiara Righi Vincenzo Ripepi Liliana Rivera Sandoval Keivan G. Stassun Michael Stroh Giacomo Terreran Virginia Trimble Yiannis Tsapras Sjoert van Velzen Laura Venuti Jorick S. Vink
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classification 🌌 astro-ph.IM astro-ph.GAastro-ph.HEastro-ph.SR
keywords rubinlsstsciencetimevariableareaspotentialroadmap
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The Vera C. Rubin Legacy Survey of Space and Time holds the potential to revolutionize time domain astrophysics, reaching completely unexplored areas of the Universe and mapping variability time scales from minutes to a decade. To prepare to maximize the potential of the Rubin LSST data for the exploration of the transient and variable Universe, one of the four pillars of Rubin LSST science, the Transient and Variable Stars Science Collaboration, one of the eight Rubin LSST Science Collaborations, has identified research areas of interest and requirements, and paths to enable them. While our roadmap is ever-evolving, this document represents a snapshot of our plans and preparatory work in the final years and months leading up to the survey's first light.

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    Attentive Neural Processes outperform Gaussian Processes and neural networks on light curve interpolation quality, feature recovery, calibration, and speed for 15 transient classes under realistic Rubin cadences.