arXiv Open Access 2022

From Data to Software to Science with the Rubin Observatory LSST

Katelyn Breivik Andrew J. Connolly K. E. Saavik Ford Mario Jurić Rachel Mandelbaum +95 lainnya

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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.

Topik & Kata Kunci

astro-ph.IM

Penulis (100)

Katelyn Breivik

Andrew J. Connolly

K. E. Saavik Ford

Mario Jurić

Rachel Mandelbaum

Adam A. Miller

Dara Norman

Knut Olsen

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

Nicolás 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 Ilić

Emille E. O. Ishida

Tomislav Jurkić

Arun Kannawadi

Alekzander Kosakowski

Andjelka B. Kovačević

Jeremy Kubica

François 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 Nikolić

Robert Nikutta

Matt O'Dowd

Charlotte Olsen

Sarah Pearson

Ilhuiyolitzin Villicana Pedraza

Mark Popinchalk

Luka C. Popović

Tyler A. Pritchard

Bruno C. Quint

Viktor Radović

Fabio Ragosta

Gabriele Riccio

Alexander H. Riley

Agata Rożek

Paula Sánchez-Sáez

Luis M. Sarro

Clare Saunders

Đorđe V. Savić

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

José Antonio Vázquez-Mata

Laura Venuti

Samuel Wyatt

Weixiang Yu

Ann Zabludoff

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Breivik, K., Connolly, A.J., Ford, K.E.S., Jurić, M., Mandelbaum, R., Miller, A.A. et al. (2022). From Data to Software to Science with the Rubin Observatory LSST. https://arxiv.org/abs/2208.02781

@article{breivik2022, author = {Katelyn Breivik and Andrew J. Connolly and K. E. Saavik Ford and Mario Jurić and Rachel Mandelbaum and Adam A. Miller and Dara Norman and Knut Olsen and William O'Mullane and Adrian Price-Whelan and Timothy Sacco and J. L. Sokoloski and Ashley Villar and Viviana Acquaviva and Tomas Ahumada and Yusra AlSayyad and Catarina S. Alves and Igor Andreoni and Timo Anguita and Henry J. Best and Federica B. Bianco and Rosaria Bonito and Andrew Bradshaw and Colin J. Burke and Andresa Rodrigues de Campos and Matteo Cantiello and Neven Caplar and Colin Orion Chandler and James Chan and Luiz Nicolaci da Costa and Shany Danieli and James R. A. Davenport and Giulio Fabbian and Joshua Fagin and Alexander Gagliano and Christa Gall and Nicolás Garavito Camargo and Eric Gawiser and Suvi Gezari and Andreja Gomboc and Alma X. Gonzalez-Morales and Matthew J. Graham and Julia Gschwend and Leanne P. Guy and Matthew J. Holman and Henry H. Hsieh and Markus Hundertmark and Dragana Ilić and Emille E. O. Ishida and Tomislav Jurkić and Arun Kannawadi and Alekzander Kosakowski and Andjelka B. Kovačević and Jeremy Kubica and François Lanusse and Ilin Lazar and W. Garrett Levine and Xiaolong Li and Jing Lu and Gerardo Juan Manuel Luna and Ashish A. Mahabal and Alex I. Malz and Yao-Yuan Mao and Ilija Medan and Joachim Moeyens and Mladen Nikolić and Robert Nikutta and Matt O'Dowd and Charlotte Olsen and Sarah Pearson and Ilhuiyolitzin Villicana Pedraza and Mark Popinchalk and Luka C. Popović and Tyler A. Pritchard and Bruno C. Quint and Viktor Radović and Fabio Ragosta and Gabriele Riccio and Alexander H. Riley and Agata Rożek and Paula Sánchez-Sáez and Luis M. Sarro and Clare Saunders and Đorđe V. Savić and Samuel Schmidt and Adam Scott and Raphael Shirley and Hayden R. Smotherman and Steven Stetzler and Kate Storey-Fisher and Rachel A. Street and David E. Trilling and Yiannis Tsapras and Sabina Ustamujic and Sjoert van Velzen and José Antonio Vázquez-Mata and Laura Venuti and Samuel Wyatt and Weixiang Yu and Ann Zabludoff}, title = {From Data to Software to Science with the Rubin Observatory LSST}, year = {2022}, journal = {arXiv}, url = {https://arxiv.org/abs/2208.02781}, }

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Tahun Terbit: 2022
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓