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Steve: a hierarchical Bayesian model for supernova cosmology

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Steve: a hierarchical Bayesian model for supernova cosmology. / Hilton, S. R.; Davis, T. M.; Kim, Alex G.; Brout, D.; D'Andrea, Christopher Brian; Kessler, Richard; Lasker, J.; Lidman, Christopher E.; Macaulay, Edward Robert Mark; Möller, A.; Sako, M.; Scolnic, Daniel M.; Smith, M.; Wolf, R. C.; Childress, Michael J.; Morganson, E. P.; Allam, S.; Annis, James T.; Avila, S.; Bertin, Emmanuel; Brooks, D.; Burke, David L.; Rosell, Aurelio Carnero; Kind, M. Carrasco; Cunha, C. E.; da Costa, Luiz Alberto Nicolaci; Davis, Clare; De Vicente, Juan; DePoy, D. L.; Doel, Peter; Eifler, Tim F.; Flaugher, B.; Fosalba, P.; Frieman, Joshua A.; García-Bellido, J.; Gaztanaga, Enrique; Gerdes, David W.; Gruendl, Robert A.; Gschwend, Julia; Gutierrez, G.; Hartley, William G.; Hollowood, Devon L.; Honscheid, Klaus; Krause, E.; Kuehn, Kyler; Kuropatkin, Nikolay; Lahav, Ofer; Lima, M.; Geimba Maia, Marcio Antonio; March, M.; Marshall, Jennifer L.; Menanteau, Felipe; Miquel, Ramon; Ogando, Ricardo L. C.; Plazas, Andres A.; Sanchez, Eusebio; Scarpine, Vic; Schindler, R.; Schubnell, Michael; Serrano, Santiago; Sevilla-Noarbe, Ignacio; Soares-Santos, Marcelle; Sobreira, F.; Suchyta, Eric; Tarle, Gregory; Thomas, Daniel; Vikram, V.; Zhang, Y.

In: The Astrophysical Journal, Vol. 876, No. 1, 15, 29.04.2019, p. 1-21.

Research output: Contribution to journalArticle

Harvard

Hilton, SR, Davis, TM, Kim, AG, Brout, D, D'Andrea, CB, Kessler, R, Lasker, J, Lidman, CE, Macaulay, ERM, Möller, A, Sako, M, Scolnic, DM, Smith, M, Wolf, RC, Childress, MJ, Morganson, EP, Allam, S, Annis, JT, Avila, S, Bertin, E, Brooks, D, Burke, DL, Rosell, AC, Kind, MC, Cunha, CE, da Costa, LAN, Davis, C, De Vicente, J, DePoy, DL, Doel, P, Eifler, TF, Flaugher, B, Fosalba, P, Frieman, JA, García-Bellido, J, Gaztanaga, E, Gerdes, DW, Gruendl, RA, Gschwend, J, Gutierrez, G, Hartley, WG, Hollowood, DL, Honscheid, K, Krause, E, Kuehn, K, Kuropatkin, N, Lahav, O, Lima, M, Geimba Maia, MA, March, M, Marshall, JL, Menanteau, F, Miquel, R, Ogando, RLC, Plazas, AA, Sanchez, E, Scarpine, V, Schindler, R, Schubnell, M, Serrano, S, Sevilla-Noarbe, I, Soares-Santos, M, Sobreira, F, Suchyta, E, Tarle, G, Thomas, D, Vikram, V & Zhang, Y 2019, 'Steve: a hierarchical Bayesian model for supernova cosmology', The Astrophysical Journal, vol. 876, no. 1, 15, pp. 1-21. https://doi.org/10.3847/1538-4357/ab13a3

APA

Hilton, S. R., Davis, T. M., Kim, A. G., Brout, D., D'Andrea, C. B., Kessler, R., Lasker, J., Lidman, C. E., Macaulay, E. R. M., Möller, A., Sako, M., Scolnic, D. M., Smith, M., Wolf, R. C., Childress, M. J., Morganson, E. P., Allam, S., Annis, J. T., Avila, S., ... Zhang, Y. (2019). Steve: a hierarchical Bayesian model for supernova cosmology. The Astrophysical Journal, 876(1), 1-21. [15]. https://doi.org/10.3847/1538-4357/ab13a3

Vancouver

Hilton SR, Davis TM, Kim AG, Brout D, D'Andrea CB, Kessler R et al. Steve: a hierarchical Bayesian model for supernova cosmology. The Astrophysical Journal. 2019 Apr 29;876(1):1-21. 15. https://doi.org/10.3847/1538-4357/ab13a3

Author

Hilton, S. R. ; Davis, T. M. ; Kim, Alex G. ; Brout, D. ; D'Andrea, Christopher Brian ; Kessler, Richard ; Lasker, J. ; Lidman, Christopher E. ; Macaulay, Edward Robert Mark ; Möller, A. ; Sako, M. ; Scolnic, Daniel M. ; Smith, M. ; Wolf, R. C. ; Childress, Michael J. ; Morganson, E. P. ; Allam, S. ; Annis, James T. ; Avila, S. ; Bertin, Emmanuel ; Brooks, D. ; Burke, David L. ; Rosell, Aurelio Carnero ; Kind, M. Carrasco ; Cunha, C. E. ; da Costa, Luiz Alberto Nicolaci ; Davis, Clare ; De Vicente, Juan ; DePoy, D. L. ; Doel, Peter ; Eifler, Tim F. ; Flaugher, B. ; Fosalba, P. ; Frieman, Joshua A. ; García-Bellido, J. ; Gaztanaga, Enrique ; Gerdes, David W. ; Gruendl, Robert A. ; Gschwend, Julia ; Gutierrez, G. ; Hartley, William G. ; Hollowood, Devon L. ; Honscheid, Klaus ; Krause, E. ; Kuehn, Kyler ; Kuropatkin, Nikolay ; Lahav, Ofer ; Lima, M. ; Geimba Maia, Marcio Antonio ; March, M. ; Marshall, Jennifer L. ; Menanteau, Felipe ; Miquel, Ramon ; Ogando, Ricardo L. C. ; Plazas, Andres A. ; Sanchez, Eusebio ; Scarpine, Vic ; Schindler, R. ; Schubnell, Michael ; Serrano, Santiago ; Sevilla-Noarbe, Ignacio ; Soares-Santos, Marcelle ; Sobreira, F. ; Suchyta, Eric ; Tarle, Gregory ; Thomas, Daniel ; Vikram, V. ; Zhang, Y. / Steve: a hierarchical Bayesian model for supernova cosmology. In: The Astrophysical Journal. 2019 ; Vol. 876, No. 1. pp. 1-21.

Bibtex

@article{72e729f7c1334cd1be172d693909dd38,
title = "Steve: a hierarchical Bayesian model for supernova cosmology",
abstract = "We present a new Bayesian hierarchical model (BHM) named Steve for performing Type Ia supernova (SN Ia) cosmology fits. This advances previous works by including an improved treatment of Malmquist bias, accounting for additional sources of systematic uncertainty, and increasing numerical efficiency. Given light-curve fit parameters, redshifts, and host-galaxy masses, we fit Steve simultaneously for parameters describing cosmology, SN Ia populations, and systematic uncertainties. Selection effects are characterized using Monte Carlo simulations. We demonstrate its implementation by fitting realizations of SN Ia data sets where the SN Ia model closely follows that used in Steve. Next, we validate on more realistic SNANA simulations of SN Ia samples from the Dark Energy Survey and low-redshift surveys (DES Collaboration et al. 2018). These simulated data sets contain more than 60,000 SNe Ia, which we use to evaluate biases in the recovery of cosmological parameters, specifically the equation of state of dark energy, w. This is the most rigorous test of a BHM method applied to SN Ia cosmology fitting and reveals small w biases that depend on the simulated SN Ia properties, in particular the intrinsic SN Ia scatter model. This w bias is less than 0.03 on average, less than half the statistical uncertainty on w. These simulation test results are a concern for BHM cosmology fitting applications on large upcoming surveys; therefore, future development will focus on minimizing the sensitivity of Steve to the SN Ia intrinsic scatter model.",
keywords = "RCUK, AST-1138766, AST-1536171",
author = "Hilton, {S. R.} and Davis, {T. M.} and Kim, {Alex G.} and D. Brout and D'Andrea, {Christopher Brian} and Richard Kessler and J. Lasker and Lidman, {Christopher E.} and Macaulay, {Edward Robert Mark} and A. M{\"o}ller and M. Sako and Scolnic, {Daniel M.} and M. Smith and Wolf, {R. C.} and Childress, {Michael J.} and Morganson, {E. P.} and S. Allam and Annis, {James T.} and S. Avila and Emmanuel Bertin and D. Brooks and Burke, {David L.} and Rosell, {Aurelio Carnero} and Kind, {M. Carrasco} and Cunha, {C. E.} and {da Costa}, {Luiz Alberto Nicolaci} and Clare Davis and {De Vicente}, Juan and DePoy, {D. L.} and Peter Doel and Eifler, {Tim F.} and B. Flaugher and P. Fosalba and Frieman, {Joshua A.} and J. Garc{\'i}a-Bellido and Enrique Gaztanaga and Gerdes, {David W.} and Gruendl, {Robert A.} and Julia Gschwend and G. Gutierrez and Hartley, {William G.} and Hollowood, {Devon L.} and Klaus Honscheid and E. Krause and Kyler Kuehn and Nikolay Kuropatkin and Ofer Lahav and M. Lima and {Geimba Maia}, {Marcio Antonio} and M. March and Marshall, {Jennifer L.} and Felipe Menanteau and Ramon Miquel and Ogando, {Ricardo L. C.} and Plazas, {Andres A.} and Eusebio Sanchez and Vic Scarpine and R. Schindler and Michael Schubnell and Santiago Serrano and Ignacio Sevilla-Noarbe and Marcelle Soares-Santos and F. Sobreira and Eric Suchyta and Gregory Tarle and Daniel Thomas and V. Vikram and Y. Zhang",
year = "2019",
month = apr,
day = "29",
doi = "10.3847/1538-4357/ab13a3",
language = "English",
volume = "876",
pages = "1--21",
journal = "The Astrophysical Journal",
issn = "0004-637X",
publisher = "IOP Publishing",
number = "1",

}

RIS

TY - JOUR

T1 - Steve: a hierarchical Bayesian model for supernova cosmology

AU - Hilton, S. R.

AU - Davis, T. M.

AU - Kim, Alex G.

AU - Brout, D.

AU - D'Andrea, Christopher Brian

AU - Kessler, Richard

AU - Lasker, J.

AU - Lidman, Christopher E.

AU - Macaulay, Edward Robert Mark

AU - Möller, A.

AU - Sako, M.

AU - Scolnic, Daniel M.

AU - Smith, M.

AU - Wolf, R. C.

AU - Childress, Michael J.

AU - Morganson, E. P.

AU - Allam, S.

AU - Annis, James T.

AU - Avila, S.

AU - Bertin, Emmanuel

AU - Brooks, D.

AU - Burke, David L.

AU - Rosell, Aurelio Carnero

AU - Kind, M. Carrasco

AU - Cunha, C. E.

AU - da Costa, Luiz Alberto Nicolaci

AU - Davis, Clare

AU - De Vicente, Juan

AU - DePoy, D. L.

AU - Doel, Peter

AU - Eifler, Tim F.

AU - Flaugher, B.

AU - Fosalba, P.

AU - Frieman, Joshua A.

AU - García-Bellido, J.

AU - Gaztanaga, Enrique

AU - Gerdes, David W.

AU - Gruendl, Robert A.

AU - Gschwend, Julia

AU - Gutierrez, G.

AU - Hartley, William G.

AU - Hollowood, Devon L.

AU - Honscheid, Klaus

AU - Krause, E.

AU - Kuehn, Kyler

AU - Kuropatkin, Nikolay

AU - Lahav, Ofer

AU - Lima, M.

AU - Geimba Maia, Marcio Antonio

AU - March, M.

AU - Marshall, Jennifer L.

AU - Menanteau, Felipe

AU - Miquel, Ramon

AU - Ogando, Ricardo L. C.

AU - Plazas, Andres A.

AU - Sanchez, Eusebio

AU - Scarpine, Vic

AU - Schindler, R.

AU - Schubnell, Michael

AU - Serrano, Santiago

AU - Sevilla-Noarbe, Ignacio

AU - Soares-Santos, Marcelle

AU - Sobreira, F.

AU - Suchyta, Eric

AU - Tarle, Gregory

AU - Thomas, Daniel

AU - Vikram, V.

AU - Zhang, Y.

PY - 2019/4/29

Y1 - 2019/4/29

N2 - We present a new Bayesian hierarchical model (BHM) named Steve for performing Type Ia supernova (SN Ia) cosmology fits. This advances previous works by including an improved treatment of Malmquist bias, accounting for additional sources of systematic uncertainty, and increasing numerical efficiency. Given light-curve fit parameters, redshifts, and host-galaxy masses, we fit Steve simultaneously for parameters describing cosmology, SN Ia populations, and systematic uncertainties. Selection effects are characterized using Monte Carlo simulations. We demonstrate its implementation by fitting realizations of SN Ia data sets where the SN Ia model closely follows that used in Steve. Next, we validate on more realistic SNANA simulations of SN Ia samples from the Dark Energy Survey and low-redshift surveys (DES Collaboration et al. 2018). These simulated data sets contain more than 60,000 SNe Ia, which we use to evaluate biases in the recovery of cosmological parameters, specifically the equation of state of dark energy, w. This is the most rigorous test of a BHM method applied to SN Ia cosmology fitting and reveals small w biases that depend on the simulated SN Ia properties, in particular the intrinsic SN Ia scatter model. This w bias is less than 0.03 on average, less than half the statistical uncertainty on w. These simulation test results are a concern for BHM cosmology fitting applications on large upcoming surveys; therefore, future development will focus on minimizing the sensitivity of Steve to the SN Ia intrinsic scatter model.

AB - We present a new Bayesian hierarchical model (BHM) named Steve for performing Type Ia supernova (SN Ia) cosmology fits. This advances previous works by including an improved treatment of Malmquist bias, accounting for additional sources of systematic uncertainty, and increasing numerical efficiency. Given light-curve fit parameters, redshifts, and host-galaxy masses, we fit Steve simultaneously for parameters describing cosmology, SN Ia populations, and systematic uncertainties. Selection effects are characterized using Monte Carlo simulations. We demonstrate its implementation by fitting realizations of SN Ia data sets where the SN Ia model closely follows that used in Steve. Next, we validate on more realistic SNANA simulations of SN Ia samples from the Dark Energy Survey and low-redshift surveys (DES Collaboration et al. 2018). These simulated data sets contain more than 60,000 SNe Ia, which we use to evaluate biases in the recovery of cosmological parameters, specifically the equation of state of dark energy, w. This is the most rigorous test of a BHM method applied to SN Ia cosmology fitting and reveals small w biases that depend on the simulated SN Ia properties, in particular the intrinsic SN Ia scatter model. This w bias is less than 0.03 on average, less than half the statistical uncertainty on w. These simulation test results are a concern for BHM cosmology fitting applications on large upcoming surveys; therefore, future development will focus on minimizing the sensitivity of Steve to the SN Ia intrinsic scatter model.

KW - RCUK

KW - AST-1138766

KW - AST-1536171

UR - http://discovery.ucl.ac.uk/10074849/

U2 - 10.3847/1538-4357/ab13a3

DO - 10.3847/1538-4357/ab13a3

M3 - Article

VL - 876

SP - 1

EP - 21

JO - The Astrophysical Journal

JF - The Astrophysical Journal

SN - 0004-637X

IS - 1

M1 - 15

ER -

ID: 14836196