From Data to Software to Science with the Rubin Observatory LSST. (arXiv:2208.02781v1 [astro-ph.IM])
<a href="http://arxiv.org/find/astro-ph/1/au:+Breivik_K/0/1/0/all/0/1">Katelyn Breivik</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Connolly_A/0/1/0/all/0/1">Andrew J. Connolly</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Ford_K/0/1/0/all/0/1">K. E. Saavik Ford</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Juric_M/0/1/0/all/0/1">Mario Juri&#x107;</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Mandelbaum_R/0/1/0/all/0/1">Rachel Mandelbaum</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Miller_A/0/1/0/all/0/1">Adam A. Miller</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Norman_D/0/1/0/all/0/1">Dara Norman</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Olsen_K/0/1/0/all/0/1">Knut Olsen</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+OMullane_W/0/1/0/all/0/1">William O&#x27;Mullane</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Price_Whelan_A/0/1/0/all/0/1">Adrian Price-Whelan</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Sacco_T/0/1/0/all/0/1">Timothy Sacco</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Sokoloski_J/0/1/0/all/0/1">J. L. Sokoloski</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Villar_A/0/1/0/all/0/1">Ashley Villar</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Acquaviva_V/0/1/0/all/0/1">Viviana Acquaviva</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Ahumada_T/0/1/0/all/0/1">Tomas Ahumada</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+AlSayyad_Y/0/1/0/all/0/1">Yusra AlSayyad</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Alves_C/0/1/0/all/0/1">Catarina S. Alves</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Andreoni_I/0/1/0/all/0/1">Igor Andreoni</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Anguita_T/0/1/0/all/0/1">Timo Anguita</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Best_H/0/1/0/all/0/1">Henry J. Best</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Bianco_F/0/1/0/all/0/1">Federica B. Bianco</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Bonito_R/0/1/0/all/0/1">Rosaria Bonito</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Bradshaw_A/0/1/0/all/0/1">Andrew Bradshaw</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Burke_C/0/1/0/all/0/1">Colin J. Burke</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Campos_A/0/1/0/all/0/1">Andresa Rodrigues de Campos</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Cantiello_M/0/1/0/all/0/1">Matteo Cantiello</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Caplar_N/0/1/0/all/0/1">Neven Caplar</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Chandler_C/0/1/0/all/0/1">Colin Orion Chandler</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Chan_J/0/1/0/all/0/1">James Chan</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Costa_L/0/1/0/all/0/1">Luiz Nicolaci da Costa</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Danieli_S/0/1/0/all/0/1">Shany Danieli</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Davenport_J/0/1/0/all/0/1">James R. A. Davenport</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Fabbian_G/0/1/0/all/0/1">Giulio Fabbian</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Fagin_J/0/1/0/all/0/1">Joshua Fagin</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Gagliano_A/0/1/0/all/0/1">Alexander Gagliano</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Gall_C/0/1/0/all/0/1">Christa Gall</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Camargo_N/0/1/0/all/0/1">Nicol&#xe1;s Garavito Camargo</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Gawiser_E/0/1/0/all/0/1">Eric Gawiser</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Gezari_S/0/1/0/all/0/1">Suvi Gezari</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Gomboc_A/0/1/0/all/0/1">Andreja Gomboc</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Gonzalez_Morales_A/0/1/0/all/0/1">Alma X. Gonzalez-Morales</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Graham_M/0/1/0/all/0/1">Matthew J. Graham</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Gschwend_J/0/1/0/all/0/1">Julia Gschwend</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Guy_L/0/1/0/all/0/1">Leanne P. Guy</a>, <a href="http://arxiv.org/find/astro-ph/1/au:+Holman_M/0/1/0/all/0/1">Matthew J. Holman</a>, et al. (55 additional authors not shown)

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.

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.

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