pith. sign in

arxiv: 2208.02781 · v1 · pith:PNY73L2Nnew · submitted 2022-08-04 · 🌌 astro-ph.IM

From Data to Software to Science with the Rubin Observatory LSST

Katelyn Breivik , Andrew J. Connolly , K. E. Saavik Ford , Mario Juri\'c , Rachel Mandelbaum , Adam A. Miller , Dara Norman , Knut Olsen
show 92 more authors
William O'Mullane Adrian Price-Whelan Timothy Sacco J. L. Sokoloski Ashley Villar Viviana Acquaviva Tomas Ahumada Yusra AlSayyad Catarina S. Alves Igor Andreoni Timo Anguita Henry J. Best Federica B. Bianco Rosaria Bonito Andrew Bradshaw Colin J. Burke Andresa Rodrigues de Campos Matteo Cantiello Neven Caplar Colin Orion Chandler James Chan Luiz Nicolaci da Costa Shany Danieli James R. A. Davenport Giulio Fabbian Joshua Fagin Alexander Gagliano Christa Gall Nicol\'as Garavito Camargo Eric Gawiser Suvi Gezari Andreja Gomboc Alma X. Gonzalez-Morales Matthew J. Graham Julia Gschwend Leanne P. Guy Matthew J. Holman Henry H. Hsieh Markus Hundertmark Dragana Ili\'c Emille E. O. Ishida Tomislav Jurki\'c Arun Kannawadi Alekzander Kosakowski Andjelka B. Kova\v{c}evi\'c Jeremy Kubica Fran\c{c}ois Lanusse Ilin Lazar W. Garrett Levine Xiaolong Li Jing Lu Gerardo Juan Manuel Luna Ashish A. Mahabal Alex I. Malz Yao-Yuan Mao Ilija Medan Joachim Moeyens Mladen Nikoli\'c Robert Nikutta Matt O'Dowd Charlotte Olsen Sarah Pearson Ilhuiyolitzin Villicana Pedraza Mark Popinchalk Luka C. Popovi\'c Tyler A. Pritchard Bruno C. Quint Viktor Radovi\'c Fabio Ragosta Gabriele Riccio Alexander H. Riley Agata Ro\.zek Paula S\'anchez-S\'aez Luis M. Sarro Clare Saunders {\DJ}or{\dj}e V. Savi\'c Samuel Schmidt Adam Scott Raphael Shirley Hayden R. Smotherman Steven Stetzler Kate Storey-Fisher Rachel A. Street David E. Trilling Yiannis Tsapras Sabina Ustamujic Sjoert van Velzen Jos\'e Antonio V\'azquez-Mata Laura Venuti Samuel Wyatt Weixiang Yu Ann Zabludoff
This is my paper
classification 🌌 astro-ph.IM
keywords lsstsoftwaresciencescalablecollaborationobservatoryrubinservices
0
0 comments X
read the original abstract

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. 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.

This paper has not been read by Pith yet.

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Forward citations

Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. The NANOGrav 15 yr Data Set: Customized Chromatic Noise Models

    astro-ph.HE 2026-06 unverdicted novelty 5.0

    Customized chromatic noise models for 67 pulsars detect non-dispersive delays in 21 cases, alter achromatic noise inferences in 19, and enable solar wind density estimates over 1.5 cycles.

  2. A Meta-Learning Framework for Multitask Reverberation Mapping in Active Galactic Nuclei

    astro-ph.GA 2026-06 unverdicted novelty 5.0

    A meta-learning framework with ALNP, SOM clustering, and mixture density models improves AGN light-curve reconstruction by 60-70% and parameter recovery on simulated and ZTF data.

  3. The NANOGrav 15 yr Data Set: Impacts of Customized Chromatic Noise Models on Gravitational Wave Analyses

    astro-ph.CO 2026-06 unverdicted novelty 4.0

    Customized chromatic noise models applied to NANOGrav 15 yr data raise the Bayes factor for Hellings-Downs GWB correlations by a factor of ~8, lower the amplitude to 2.1e-15, and increase the spectral index to 3.5.