The LSST-DESC 3x2pt Tomography Optimization Challenge
- Autores
- Zuntz, Joe; Lanusse, Francois; Malz, Alex I.; Wright, Angus H.; Slosar, Anze; Abolfathi, Bela; Alonso, David; Bault, Abby; Bom, Clecio R.; Brescia, Massimo; Broussard, Adam; Campagne, Jean Eric; Cavuoti, Stefano; Cypriano, Eduardo S.; Fraga, Bernardo M. O.; Gawiser, Eric; Gonzalez, Elizabeth Johana; Green, Dylan; Hatfield, Peter; Iyer, Kartheik; Kirkby, David; Nicola, Andrina; Nourbakhsh, Erfan; Park, Andy; Teixeira, Gabriel; Heitmann, Katrin; Kovacs, Eve; Mao, Yao Yuan
- Año de publicación
- 2021
- Idioma
- inglés
- Tipo de recurso
- artículo
- Estado
- versión publicada
- Descripción
- This paper presents the results of the Rubin Observatory Dark Energy Science Collaboration (DESC) 3x2pt tomography challenge, which served as a first step toward optimizing the tomographic binning strategy for the main DESC analysis. The task of choosing an optimal tomographic binning scheme for a photometric survey is made particularly delicate in the context of a metacalibrated lensing catalogue, as only the photometry from the bands included in the metacalibration process (usually riz and potentially g) can be used in sample definition. The goal of the challenge was to collect and compare bin assignment strategies under various metrics of a standard 3x2pt cosmology analysis in a highly idealized setting to establish a baseline for realistically complex follow-up studies; in this preliminary study, we used two sets of cosmological simulations of galaxy redshifts and photometry under a simple noise model neglecting photometric outliers and variation in observing conditions, and contributed algorithms were provided with a representative and complete training set. We review and evaluate the entries to the challenge, finding that even from this limited photometry information, multiple algorithms can separate tomographic bins reasonably well, reaching figures-of-merit scores close to the attainable maximum. We further find that adding the g band to riz photometry improves metric performance by ~15% and that the optimal bin assignment strategy depends strongly on the science case: which figure-of-merit is to be optimized, and which observables (clustering, lensing, or both) are included.
Fil: Zuntz, Joe. University of Hawaii at Manoa; Estados Unidos
Fil: Lanusse, Francois. Université Paris Sud; Francia
Fil: Malz, Alex I.. Ruhr Universität Bochum; Alemania
Fil: Wright, Angus H.. Ruhr Universität Bochum; Alemania
Fil: Slosar, Anze. Brookhaven National Laboratory; Estados Unidos
Fil: Abolfathi, Bela. University Of California At Los Angeles. Department Of Physics And Astronomy.; Estados Unidos
Fil: Alonso, David. University of Oxford; Reino Unido
Fil: Bault, Abby. University Of California At Los Angeles. Department Of Physics And Astronomy.; Estados Unidos
Fil: Bom, Clecio R.. Centro Brasileiro de Pesquisas Físicas; Brasil
Fil: Brescia, Massimo. Istituto Nazionale di Astrofisica; Italia
Fil: Broussard, Adam. Texas A&M University; Estados Unidos
Fil: Campagne, Jean Eric. Université Paris Sud; Francia
Fil: Cavuoti, Stefano. Istituto Nazionale di Astrofisica; Italia
Fil: Cypriano, Eduardo S.. Universidade do Sao Paulo. Instituto de Astronomia, Geofísica e Ciências Atmosféricas; Brasil
Fil: Fraga, Bernardo M. O.. Centro Brasileiro de Pesquisas Físicas; Brasil
Fil: Gawiser, Eric. Rutgers University; Estados Unidos
Fil: Gonzalez, Elizabeth Johana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Astronomía Teórica y Experimental. Universidad Nacional de Córdoba. Observatorio Astronómico de Córdoba. Instituto de Astronomía Teórica y Experimental; Argentina. Universidad Nacional de Córdoba. Observatorio Astronómico de Córdoba; Argentina
Fil: Green, Dylan. University Of California At Los Angeles. Department Of Physics And Astronomy.; Estados Unidos
Fil: Hatfield, Peter. University of Oxford; Reino Unido
Fil: Iyer, Kartheik. Dunlap Institute for Astronomy & Astrophysics; Canadá
Fil: Kirkby, David. University Of California At Los Angeles. Department Of Physics And Astronomy.; Estados Unidos
Fil: Nicola, Andrina. University of Princeton; Estados Unidos
Fil: Nourbakhsh, Erfan. University Of California At Los Angeles. Department Of Physics And Astronomy.; Estados Unidos
Fil: Park, Andy. University Of California At Los Angeles. Department Of Physics And Astronomy.; Estados Unidos
Fil: Teixeira, Gabriel. Centro Brasileiro de Pesquisas Físicas; Brasil
Fil: Heitmann, Katrin. Argonne National Laboratory; Estados Unidos
Fil: Kovacs, Eve. Argonne National Laboratory; Estados Unidos
Fil: Mao, Yao Yuan. University of Utah; Estados Unidos - Materia
-
TOMOGRAPHY
CHALLENGE
LENSING - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
- Repositorio
.jpg)
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/170946
Ver los metadatos del registro completo
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The LSST-DESC 3x2pt Tomography Optimization ChallengeZuntz, JoeLanusse, FrancoisMalz, Alex I.Wright, Angus H.Slosar, AnzeAbolfathi, BelaAlonso, DavidBault, AbbyBom, Clecio R.Brescia, MassimoBroussard, AdamCampagne, Jean EricCavuoti, StefanoCypriano, Eduardo S.Fraga, Bernardo M. O.Gawiser, EricGonzalez, Elizabeth JohanaGreen, DylanHatfield, PeterIyer, KartheikKirkby, DavidNicola, AndrinaNourbakhsh, ErfanPark, AndyTeixeira, GabrielHeitmann, KatrinKovacs, EveMao, Yao YuanTOMOGRAPHYCHALLENGELENSINGhttps://purl.org/becyt/ford/1.3https://purl.org/becyt/ford/1This paper presents the results of the Rubin Observatory Dark Energy Science Collaboration (DESC) 3x2pt tomography challenge, which served as a first step toward optimizing the tomographic binning strategy for the main DESC analysis. The task of choosing an optimal tomographic binning scheme for a photometric survey is made particularly delicate in the context of a metacalibrated lensing catalogue, as only the photometry from the bands included in the metacalibration process (usually riz and potentially g) can be used in sample definition. The goal of the challenge was to collect and compare bin assignment strategies under various metrics of a standard 3x2pt cosmology analysis in a highly idealized setting to establish a baseline for realistically complex follow-up studies; in this preliminary study, we used two sets of cosmological simulations of galaxy redshifts and photometry under a simple noise model neglecting photometric outliers and variation in observing conditions, and contributed algorithms were provided with a representative and complete training set. We review and evaluate the entries to the challenge, finding that even from this limited photometry information, multiple algorithms can separate tomographic bins reasonably well, reaching figures-of-merit scores close to the attainable maximum. We further find that adding the g band to riz photometry improves metric performance by ~15% and that the optimal bin assignment strategy depends strongly on the science case: which figure-of-merit is to be optimized, and which observables (clustering, lensing, or both) are included.Fil: Zuntz, Joe. University of Hawaii at Manoa; Estados UnidosFil: Lanusse, Francois. Université Paris Sud; FranciaFil: Malz, Alex I.. Ruhr Universität Bochum; AlemaniaFil: Wright, Angus H.. Ruhr Universität Bochum; AlemaniaFil: Slosar, Anze. Brookhaven National Laboratory; Estados UnidosFil: Abolfathi, Bela. University Of California At Los Angeles. Department Of Physics And Astronomy.; Estados UnidosFil: Alonso, David. University of Oxford; Reino UnidoFil: Bault, Abby. University Of California At Los Angeles. Department Of Physics And Astronomy.; Estados UnidosFil: Bom, Clecio R.. Centro Brasileiro de Pesquisas Físicas; BrasilFil: Brescia, Massimo. Istituto Nazionale di Astrofisica; ItaliaFil: Broussard, Adam. Texas A&M University; Estados UnidosFil: Campagne, Jean Eric. Université Paris Sud; FranciaFil: Cavuoti, Stefano. Istituto Nazionale di Astrofisica; ItaliaFil: Cypriano, Eduardo S.. Universidade do Sao Paulo. Instituto de Astronomia, Geofísica e Ciências Atmosféricas; BrasilFil: Fraga, Bernardo M. O.. Centro Brasileiro de Pesquisas Físicas; BrasilFil: Gawiser, Eric. Rutgers University; Estados UnidosFil: Gonzalez, Elizabeth Johana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Astronomía Teórica y Experimental. Universidad Nacional de Córdoba. Observatorio Astronómico de Córdoba. Instituto de Astronomía Teórica y Experimental; Argentina. Universidad Nacional de Córdoba. Observatorio Astronómico de Córdoba; ArgentinaFil: Green, Dylan. University Of California At Los Angeles. Department Of Physics And Astronomy.; Estados UnidosFil: Hatfield, Peter. University of Oxford; Reino UnidoFil: Iyer, Kartheik. Dunlap Institute for Astronomy & Astrophysics; CanadáFil: Kirkby, David. University Of California At Los Angeles. Department Of Physics And Astronomy.; Estados UnidosFil: Nicola, Andrina. University of Princeton; Estados UnidosFil: Nourbakhsh, Erfan. University Of California At Los Angeles. Department Of Physics And Astronomy.; Estados UnidosFil: Park, Andy. University Of California At Los Angeles. Department Of Physics And Astronomy.; Estados UnidosFil: Teixeira, Gabriel. Centro Brasileiro de Pesquisas Físicas; BrasilFil: Heitmann, Katrin. Argonne National Laboratory; Estados UnidosFil: Kovacs, Eve. Argonne National Laboratory; Estados UnidosFil: Mao, Yao Yuan. University of Utah; Estados UnidosMaynooth Academic Publishing2021-10info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/170946Zuntz, Joe; Lanusse, Francois; Malz, Alex I.; Wright, Angus H.; Slosar, Anze; et al.; The LSST-DESC 3x2pt Tomography Optimization Challenge; Maynooth Academic Publishing; The Open Journal of Astrophysics; 4; 13; 10-2021; 1-262565-6120CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.21105/astro.2108.13418info:eu-repo/semantics/altIdentifier/arxiv/https://arxiv.org/abs/2108.13418v2info:eu-repo/semantics/altIdentifier/url/https://astro.theoj.org/article/29530-the-lsst-desc-3x2pt-tomography-optimization-challengeinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-29T09:57:42Zoai:ri.conicet.gov.ar:11336/170946instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-29 09:57:42.705CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse |
| dc.title.none.fl_str_mv |
The LSST-DESC 3x2pt Tomography Optimization Challenge |
| title |
The LSST-DESC 3x2pt Tomography Optimization Challenge |
| spellingShingle |
The LSST-DESC 3x2pt Tomography Optimization Challenge Zuntz, Joe TOMOGRAPHY CHALLENGE LENSING |
| title_short |
The LSST-DESC 3x2pt Tomography Optimization Challenge |
| title_full |
The LSST-DESC 3x2pt Tomography Optimization Challenge |
| title_fullStr |
The LSST-DESC 3x2pt Tomography Optimization Challenge |
| title_full_unstemmed |
The LSST-DESC 3x2pt Tomography Optimization Challenge |
| title_sort |
The LSST-DESC 3x2pt Tomography Optimization Challenge |
| dc.creator.none.fl_str_mv |
Zuntz, Joe Lanusse, Francois Malz, Alex I. Wright, Angus H. Slosar, Anze Abolfathi, Bela Alonso, David Bault, Abby Bom, Clecio R. Brescia, Massimo Broussard, Adam Campagne, Jean Eric Cavuoti, Stefano Cypriano, Eduardo S. Fraga, Bernardo M. O. Gawiser, Eric Gonzalez, Elizabeth Johana Green, Dylan Hatfield, Peter Iyer, Kartheik Kirkby, David Nicola, Andrina Nourbakhsh, Erfan Park, Andy Teixeira, Gabriel Heitmann, Katrin Kovacs, Eve Mao, Yao Yuan |
| author |
Zuntz, Joe |
| author_facet |
Zuntz, Joe Lanusse, Francois Malz, Alex I. Wright, Angus H. Slosar, Anze Abolfathi, Bela Alonso, David Bault, Abby Bom, Clecio R. Brescia, Massimo Broussard, Adam Campagne, Jean Eric Cavuoti, Stefano Cypriano, Eduardo S. Fraga, Bernardo M. O. Gawiser, Eric Gonzalez, Elizabeth Johana Green, Dylan Hatfield, Peter Iyer, Kartheik Kirkby, David Nicola, Andrina Nourbakhsh, Erfan Park, Andy Teixeira, Gabriel Heitmann, Katrin Kovacs, Eve Mao, Yao Yuan |
| author_role |
author |
| author2 |
Lanusse, Francois Malz, Alex I. Wright, Angus H. Slosar, Anze Abolfathi, Bela Alonso, David Bault, Abby Bom, Clecio R. Brescia, Massimo Broussard, Adam Campagne, Jean Eric Cavuoti, Stefano Cypriano, Eduardo S. Fraga, Bernardo M. O. Gawiser, Eric Gonzalez, Elizabeth Johana Green, Dylan Hatfield, Peter Iyer, Kartheik Kirkby, David Nicola, Andrina Nourbakhsh, Erfan Park, Andy Teixeira, Gabriel Heitmann, Katrin Kovacs, Eve Mao, Yao Yuan |
| author2_role |
author author author author author author author author author author author author author author author author author author author author author author author author author author author |
| dc.subject.none.fl_str_mv |
TOMOGRAPHY CHALLENGE LENSING |
| topic |
TOMOGRAPHY CHALLENGE LENSING |
| purl_subject.fl_str_mv |
https://purl.org/becyt/ford/1.3 https://purl.org/becyt/ford/1 |
| dc.description.none.fl_txt_mv |
This paper presents the results of the Rubin Observatory Dark Energy Science Collaboration (DESC) 3x2pt tomography challenge, which served as a first step toward optimizing the tomographic binning strategy for the main DESC analysis. The task of choosing an optimal tomographic binning scheme for a photometric survey is made particularly delicate in the context of a metacalibrated lensing catalogue, as only the photometry from the bands included in the metacalibration process (usually riz and potentially g) can be used in sample definition. The goal of the challenge was to collect and compare bin assignment strategies under various metrics of a standard 3x2pt cosmology analysis in a highly idealized setting to establish a baseline for realistically complex follow-up studies; in this preliminary study, we used two sets of cosmological simulations of galaxy redshifts and photometry under a simple noise model neglecting photometric outliers and variation in observing conditions, and contributed algorithms were provided with a representative and complete training set. We review and evaluate the entries to the challenge, finding that even from this limited photometry information, multiple algorithms can separate tomographic bins reasonably well, reaching figures-of-merit scores close to the attainable maximum. We further find that adding the g band to riz photometry improves metric performance by ~15% and that the optimal bin assignment strategy depends strongly on the science case: which figure-of-merit is to be optimized, and which observables (clustering, lensing, or both) are included. Fil: Zuntz, Joe. University of Hawaii at Manoa; Estados Unidos Fil: Lanusse, Francois. Université Paris Sud; Francia Fil: Malz, Alex I.. Ruhr Universität Bochum; Alemania Fil: Wright, Angus H.. Ruhr Universität Bochum; Alemania Fil: Slosar, Anze. Brookhaven National Laboratory; Estados Unidos Fil: Abolfathi, Bela. University Of California At Los Angeles. Department Of Physics And Astronomy.; Estados Unidos Fil: Alonso, David. University of Oxford; Reino Unido Fil: Bault, Abby. University Of California At Los Angeles. Department Of Physics And Astronomy.; Estados Unidos Fil: Bom, Clecio R.. Centro Brasileiro de Pesquisas Físicas; Brasil Fil: Brescia, Massimo. Istituto Nazionale di Astrofisica; Italia Fil: Broussard, Adam. Texas A&M University; Estados Unidos Fil: Campagne, Jean Eric. Université Paris Sud; Francia Fil: Cavuoti, Stefano. Istituto Nazionale di Astrofisica; Italia Fil: Cypriano, Eduardo S.. Universidade do Sao Paulo. Instituto de Astronomia, Geofísica e Ciências Atmosféricas; Brasil Fil: Fraga, Bernardo M. O.. Centro Brasileiro de Pesquisas Físicas; Brasil Fil: Gawiser, Eric. Rutgers University; Estados Unidos Fil: Gonzalez, Elizabeth Johana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Córdoba. Instituto de Astronomía Teórica y Experimental. Universidad Nacional de Córdoba. Observatorio Astronómico de Córdoba. Instituto de Astronomía Teórica y Experimental; Argentina. Universidad Nacional de Córdoba. Observatorio Astronómico de Córdoba; Argentina Fil: Green, Dylan. University Of California At Los Angeles. Department Of Physics And Astronomy.; Estados Unidos Fil: Hatfield, Peter. University of Oxford; Reino Unido Fil: Iyer, Kartheik. Dunlap Institute for Astronomy & Astrophysics; Canadá Fil: Kirkby, David. University Of California At Los Angeles. Department Of Physics And Astronomy.; Estados Unidos Fil: Nicola, Andrina. University of Princeton; Estados Unidos Fil: Nourbakhsh, Erfan. University Of California At Los Angeles. Department Of Physics And Astronomy.; Estados Unidos Fil: Park, Andy. University Of California At Los Angeles. Department Of Physics And Astronomy.; Estados Unidos Fil: Teixeira, Gabriel. Centro Brasileiro de Pesquisas Físicas; Brasil Fil: Heitmann, Katrin. Argonne National Laboratory; Estados Unidos Fil: Kovacs, Eve. Argonne National Laboratory; Estados Unidos Fil: Mao, Yao Yuan. University of Utah; Estados Unidos |
| description |
This paper presents the results of the Rubin Observatory Dark Energy Science Collaboration (DESC) 3x2pt tomography challenge, which served as a first step toward optimizing the tomographic binning strategy for the main DESC analysis. The task of choosing an optimal tomographic binning scheme for a photometric survey is made particularly delicate in the context of a metacalibrated lensing catalogue, as only the photometry from the bands included in the metacalibration process (usually riz and potentially g) can be used in sample definition. The goal of the challenge was to collect and compare bin assignment strategies under various metrics of a standard 3x2pt cosmology analysis in a highly idealized setting to establish a baseline for realistically complex follow-up studies; in this preliminary study, we used two sets of cosmological simulations of galaxy redshifts and photometry under a simple noise model neglecting photometric outliers and variation in observing conditions, and contributed algorithms were provided with a representative and complete training set. We review and evaluate the entries to the challenge, finding that even from this limited photometry information, multiple algorithms can separate tomographic bins reasonably well, reaching figures-of-merit scores close to the attainable maximum. We further find that adding the g band to riz photometry improves metric performance by ~15% and that the optimal bin assignment strategy depends strongly on the science case: which figure-of-merit is to be optimized, and which observables (clustering, lensing, or both) are included. |
| publishDate |
2021 |
| dc.date.none.fl_str_mv |
2021-10 |
| dc.type.none.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion http://purl.org/coar/resource_type/c_6501 info:ar-repo/semantics/articulo |
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article |
| status_str |
publishedVersion |
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http://hdl.handle.net/11336/170946 Zuntz, Joe; Lanusse, Francois; Malz, Alex I.; Wright, Angus H.; Slosar, Anze; et al.; The LSST-DESC 3x2pt Tomography Optimization Challenge; Maynooth Academic Publishing; The Open Journal of Astrophysics; 4; 13; 10-2021; 1-26 2565-6120 CONICET Digital CONICET |
| url |
http://hdl.handle.net/11336/170946 |
| identifier_str_mv |
Zuntz, Joe; Lanusse, Francois; Malz, Alex I.; Wright, Angus H.; Slosar, Anze; et al.; The LSST-DESC 3x2pt Tomography Optimization Challenge; Maynooth Academic Publishing; The Open Journal of Astrophysics; 4; 13; 10-2021; 1-26 2565-6120 CONICET Digital CONICET |
| dc.language.none.fl_str_mv |
eng |
| language |
eng |
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info:eu-repo/semantics/altIdentifier/doi/10.21105/astro.2108.13418 info:eu-repo/semantics/altIdentifier/arxiv/https://arxiv.org/abs/2108.13418v2 info:eu-repo/semantics/altIdentifier/url/https://astro.theoj.org/article/29530-the-lsst-desc-3x2pt-tomography-optimization-challenge |
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info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by-nc-sa/2.5/ar/ |
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openAccess |
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https://creativecommons.org/licenses/by-nc-sa/2.5/ar/ |
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application/pdf application/pdf |
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Maynooth Academic Publishing |
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Maynooth Academic Publishing |
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dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar |
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