Steve: A Hierarchical Bayesian Model for Supernova Cosmology

S. R. Hinton; T. M. Davis; A. G. Kim; D. Brout; C. B. D'Andrea; R. Kessler; J. Lasker; C. Lidman; E. Macaulay; A. Möller; M. Sako; D. Scolnic; M. Smith; R. C. Wolf; M. Childress; E. Morganson; S. Allam; J. Annis; S. Avila; E. Bertin; D. Brooks; D. L. Burke; A. Carnero Rosell; M. Carrasco Kind; J. Carretero; C. E. Cunha; L. N.Da Costa; C. Davis; J. De Vicente; D. L. Depoy; P. Doel; T. F. Eifler; B. Flaugher; P. Fosalba; J. Frieman; J. García-Bellido; E. Gaztanaga; D. W. Gerdes; R. A. Gruendl; J. Gschwend; G. Gutierrez; W. G. Hartley; D. L. Hollowood; K. Honscheid; E. Krause; K. Kuehn; N. Kuropatkin; O. Lahav; M. Lima; M. A.G. Maia; M. March; J. L. Marshall; F. Menanteau; R. Miquel; R. L.C. Ogando; A. A. Plazas; E. Sanchez; V. Scarpine; R. Schindler; M. Schubnell; S. Serrano; I. Sevilla-Noarbe; M. Soares-Santos; F. Sobreira; E. Suchyta; G. Tarle; D. Thomas; V. Vikram; Y. Zhang

doi:10.3847/1538-4357/ab13a3

Steve: A Hierarchical Bayesian Model for Supernova Cosmology

S. R. Hinton, T. M. Davis, A. G. Kim, D. Brout, C. B. D'Andrea, R. Kessler, J. Lasker, C. Lidman, E. Macaulay, A. Möller, M. Sako, D. Scolnic, M. Smith, R. C. Wolf, M. Childress, E. Morganson, S. Allam, J. Annis, S. Avila, E. BertinD. Brooks, D. L. Burke, A. Carnero Rosell, M. Carrasco Kind, J. Carretero, C. E. Cunha, L. N.Da Costa, C. Davis, J. De Vicente, D. L. Depoy, P. Doel, T. F. Eifler, B. Flaugher, P. Fosalba, J. Frieman, J. García-Bellido, E. Gaztanaga, D. W. Gerdes, R. A. Gruendl, J. Gschwend, G. Gutierrez, W. G. Hartley, D. L. Hollowood, K. Honscheid, E. Krause, K. Kuehn, N. Kuropatkin, O. Lahav, M. Lima, M. A.G. Maia, M. March, J. L. Marshall, F. Menanteau, R. Miquel, R. L.C. Ogando, A. A. Plazas, E. Sanchez, V. Scarpine, R. Schindler, M. Schubnell, S. Serrano, I. Sevilla-Noarbe, M. Soares-Santos, F. Sobreira, E. Suchyta, G. Tarle, D. Thomas, V. Vikram, Y. Zhang

Department of Morphological Sciences

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Abstract

© 2019. The American Astronomical Society. All rights reserved. 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.

Original language	English
Article number	15
Journal	Astrophysical Journal
Volume	876
DOIs	https://doi.org/10.3847/1538-4357/ab13a3
Publication status	Published - 1 May 2019

Keywords

dark energy
methods: data analysis

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10.3847/1538-4357/ab13a3

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Hinton, S. R., Davis, T. M., Kim, A. G., Brout, D., D'Andrea, C. B., Kessler, R., Lasker, J., Lidman, C., Macaulay, E., Möller, A., Sako, M., Scolnic, D., Smith, M., Wolf, R. C., Childress, M., Morganson, E., Allam, S., Annis, J., Avila, S., ... Zhang, Y. (2019). Steve: A Hierarchical Bayesian Model for Supernova Cosmology. Astrophysical Journal, 876, [15]. https://doi.org/10.3847/1538-4357/ab13a3

@article{49077d67c5d34c0d928f4ee883da815b,

title = "Steve: A Hierarchical Bayesian Model for Supernova Cosmology",

abstract = "{\textcopyright} 2019. The American Astronomical Society. All rights reserved. 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 = "dark energy, methods: data analysis",

author = "Hinton, {S. R.} and Davis, {T. M.} and Kim, {A. G.} and D. Brout and D'Andrea, {C. B.} and R. Kessler and J. Lasker and C. Lidman and E. Macaulay and A. M{\"o}ller and M. Sako and D. Scolnic and M. Smith and Wolf, {R. C.} and M. Childress and E. Morganson and S. Allam and J. Annis and S. Avila and E. Bertin and D. Brooks and Burke, {D. L.} and Rosell, {A. Carnero} and Kind, {M. Carrasco} and J. Carretero and Cunha, {C. E.} and Costa, {L. N.Da} and C. Davis and Vicente, {J. De} and Depoy, {D. L.} and P. Doel and Eifler, {T. F.} and B. Flaugher and P. Fosalba and J. Frieman and J. Garc{\'i}a-Bellido and E. Gaztanaga and Gerdes, {D. W.} and Gruendl, {R. A.} and J. Gschwend and G. Gutierrez and Hartley, {W. G.} and Hollowood, {D. L.} and K. Honscheid and E. Krause and K. Kuehn and N. Kuropatkin and O. Lahav and M. Lima and Maia, {M. A.G.} and M. March and Marshall, {J. L.} and F. Menanteau and R. Miquel and Ogando, {R. L.C.} and Plazas, {A. A.} and E. Sanchez and V. Scarpine and R. Schindler and M. Schubnell and S. Serrano and I. Sevilla-Noarbe and M. Soares-Santos and F. Sobreira and E. Suchyta and G. Tarle and D. Thomas and V. Vikram and Y. Zhang",

year = "2019",

month = may,

day = "1",

doi = "10.3847/1538-4357/ab13a3",

language = "English",

volume = "876",

journal = "Astrophysical Journal",

issn = "0004-637X",

}

Hinton, SR, Davis, TM, Kim, AG, Brout, D, D'Andrea, CB, Kessler, R, Lasker, J, Lidman, C, Macaulay, E, Möller, A, Sako, M, Scolnic, D, Smith, M, Wolf, RC, Childress, M, Morganson, E, Allam, S, Annis, J, Avila, S, Bertin, E, Brooks, D, Burke, DL, Rosell, AC, Kind, MC, Carretero, J, Cunha, CE, Costa, LND, Davis, C, Vicente, JD, Depoy, DL, Doel, P, Eifler, TF, Flaugher, B, Fosalba, P, Frieman, J, 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, Maia, MAG, 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', Astrophysical Journal, vol. 876, 15. https://doi.org/10.3847/1538-4357/ab13a3

TY - JOUR

T1 - Steve: A Hierarchical Bayesian Model for Supernova Cosmology

AU - Hinton, S. R.

AU - Davis, T. M.

AU - Kim, A. G.

AU - Brout, D.

AU - D'Andrea, C. B.

AU - Kessler, R.

AU - Lasker, J.

AU - Lidman, C.

AU - Macaulay, E.

AU - Möller, A.

AU - Sako, M.

AU - Scolnic, D.

AU - Smith, M.

AU - Wolf, R. C.

AU - Childress, M.

AU - Morganson, E.

AU - Allam, S.

AU - Annis, J.

AU - Avila, S.

AU - Bertin, E.

AU - Brooks, D.

AU - Burke, D. L.

AU - Rosell, A. Carnero

AU - Kind, M. Carrasco

AU - Carretero, J.

AU - Cunha, C. E.

AU - Costa, L. N.Da

AU - Davis, C.

AU - Vicente, J. De

AU - Depoy, D. L.

AU - Doel, P.

AU - Eifler, T. F.

AU - Flaugher, B.

AU - Fosalba, P.

AU - Frieman, J.

AU - García-Bellido, J.

AU - Gaztanaga, E.

AU - Gerdes, D. W.

AU - Gruendl, R. A.

AU - Gschwend, J.

AU - Gutierrez, G.

AU - Hartley, W. G.

AU - Hollowood, D. L.

AU - Honscheid, K.

AU - Krause, E.

AU - Kuehn, K.

AU - Kuropatkin, N.

AU - Lahav, O.

AU - Lima, M.

AU - Maia, M. A.G.

AU - March, M.

AU - Marshall, J. L.

AU - Menanteau, F.

AU - Miquel, R.

AU - Ogando, R. L.C.

AU - Plazas, A. A.

AU - Sanchez, E.

AU - Scarpine, V.

AU - Schindler, R.

AU - Schubnell, M.

AU - Serrano, S.

AU - Sevilla-Noarbe, I.

AU - Soares-Santos, M.

AU - Sobreira, F.

AU - Suchyta, E.

AU - Tarle, G.

AU - Thomas, D.

AU - Vikram, V.

AU - Zhang, Y.

PY - 2019/5/1

Y1 - 2019/5/1

N2 - © 2019. The American Astronomical Society. All rights reserved. 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 - © 2019. The American Astronomical Society. All rights reserved. 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 - dark energy

KW - methods: data analysis

U2 - 10.3847/1538-4357/ab13a3

DO - 10.3847/1538-4357/ab13a3

M3 - Article

SN - 0004-637X

VL - 876

JO - Astrophysical Journal

JF - Astrophysical Journal

M1 - 15

ER -

Steve: A Hierarchical Bayesian Model for Supernova Cosmology

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