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1. Results of the Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC)

   https://doi.org/10.3847/1538-4365/accd6a  

   Hložek, R; Malz, A I; Ponder, K A; Dai, M; Narayan, G; Ishida, E_E O; Jr, T Allam; Bahmanyar, A; Bi, X; Biswas, R; et al (July 2023, The Astrophysical Journal Supplement Series)

   Abstract Next-generation surveys like the Legacy Survey of Space and Time (LSST) on the Vera C. Rubin Observatory (Rubin) will generate orders of magnitude more discoveries of transients and variable stars than previous surveys. To prepare for this data deluge, we developed the Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC), a competition that aimed to catalyze the development of robust classifiers under LSST-like conditions of a nonrepresentative training set for a large photometric test set of imbalanced classes. Over 1000 teams participated in PLAsTiCC, which was hosted in the Kaggle data science competition platform between 2018 September 28 and 2018 December 17, ultimately identifying three winners in 2019 February. Participants produced classifiers employing a diverse set of machine-learning techniques including hybrid combinations and ensemble averages of a range of approaches, among them boosted decision trees, neural networks, and multilayer perceptrons. The strong performance of the top three classifiers on Type Ia supernovae and kilonovae represent a major improvement over the current state of the art within astronomy. This paper summarizes the most promising methods and evaluates their results in detail, highlighting future directions both for classifier development and simulation needs for a next-generation PLAsTiCC data set. 

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2. Rubin Observatory LSST Transients and Variable Stars Roadmap

   https://doi.org/10.1088/1538-3873/acdb9a  

   Hambleton, Kelly M; Bianco, Federica B; Street, Rachel; Bell, Keaton; Buckley, David; Graham, Melissa; Hernitschek, Nina; Lund, Michael B; Mason, Elena; Pepper, Joshua; et al (October 2023, Publications of the Astronomical Society of the Pacific)

   Abstract The Vera C. Rubin Legacy Survey of Space and Time (LSST) 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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3. Toward the Automated Detection of Light Echoes in Synoptic Surveys: Considerations on the Application of Deep Convolutional Neural Networks

   https://doi.org/10.3847/1538-3881/ac9409  

   Li, Xiaolong; Bianco, Federica B.; Dobler, Gregory; Partoush, Roee; Rest, Armin; Acero-Cuellar, Tatiana; Clarke, Riley; Fortino, Willow Fox; Khakpash, Somayeh; Lian, Ming (November 2022, The Astronomical Journal)

   Abstract Light echoes (LEs) are the reflections of astrophysical transients off of interstellar dust. They are fascinating astronomical phenomena that enable studies of the scattering dust as well as of the original transients. LEs, however, are rare and extremely difficult to detect as they appear as faint, diffuse, time-evolving features. The detection of LEs still largely relies on human inspection of images, a method unfeasible in the era of large synoptic surveys. The Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) will generate an unprecedented amount of astronomical imaging data at high spatial resolution, exquisite image quality, and over tens of thousands of square degrees of sky: an ideal survey for LEs. However, the Rubin data processing pipelines are optimized for the detection of point sources and will entirely miss LEs. Over the past several years, artificial intelligence (AI) object-detection frameworks have achieved and surpassed real-time, human-level performance. In this work, we leverage a data set from the Asteroid Terrestrial-impact Last Alert System telescope to test a popular AI object-detection framework, You Only Look Once, or YOLO, developed by the computer-vision community, to demonstrate the potential of AI for the detection of LEs in astronomical images. We find that an AI framework can reach human-level performance even with a size- and quality-limited data set. We explore and highlight challenges, including class imbalance and label incompleteness, and road map the work required to build an end-to-end pipeline for the automated detection and study of LEs in high-throughput astronomical surveys. 

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4. Synergies between Roman Galactic Plane Survey and other major surveys

   Kruszynska, K; Street, R; Gough-Kelly, S; Bonito, R; Prisinzano, L; Trivedi, O; Gandhi, P; Hundertmark, M; Tsapras, Y; Di_Criscienzo, M; et al (June 2024, arXiv)

   Nancy Grace Roman Space Telescope will revolutionize our understanding of the Galactic Bulge with its Galactic Bulge Time Domain survey. At the same time, Rubin Observatories's Legacy Survey of Space and Time (LSST) will monitor billions of stars in the Milky Way. The proposed Roman survey of the Galactic Plane, with its NIR passbands and exquisite spacial resolution, promises groundbreaking insights for a wide range of time-domain galactic astrophysics. In this white paper, we describe the scientific returns possible from the combination of the Roman Galactic Plane Survey with the data from LSST. 

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5. From Data to Software to Science with the Rubin Observatory LSST

   Breivik, Katelyn; Connolly, Andrew; Ford, Saavik; and, others (August 2022, https://arxiv.org/)

   The Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) dataset will dramatically alter our understanding of the Universe, from the origins of the Solar System to the nature of dark matter and dark energy. Much of this research will depend on the existence of robust, tested, and scalable algorithms, software, and services. Identifying and developing such tools ahead of time has the potential to significantly accelerate the delivery of early science from LSST. Developing these collaboratively, and making them broadly available, can enable more inclusive and equitable collaboration on LSST science.iv To facilitate such opportunities, a community workshop entitled “From Data to Software to Science with the Rubin Observatory LSST” was organized by the LSST Interdisciplinary Network for Collaboration and Computing (LINCC) and partners, and held at the Flatiron Institute in New York, March 28-30th 2022. The workshop included over 50 in-person attendees invited from over 300 applications. It identified seven key software areas of need: (i) scalable cross-matching and distributed joining of catalogs, (ii) robust photometric redshift determination, (iii) software for determination of selection functions, (iv) frameworks for scalable time-series analyses, (v) services for image access and reprocessing at scale, (vi) object image access (cutouts) and analysis at scale, and (vii) scalable job execution systems. This white paper summarizes the discussions of this workshop. It considers the motivating science use cases, identified cross-cutting algorithms, software, and services, their high-level technical specifications, and the principles of inclusive collaborations needed to develop them. We provide it as a useful roadmap of needs, as well as to spur action and collaboration between groups and individuals looking to develop reusable software for early LSST science. 

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