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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 (SNIa) 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, SNIa populations, and systematic uncertainties. Selection effects are characterised using Monte-Carlo simulations. We demonstrate its implementation by fitting realisations of SNIa datasets where the SNIa model closely follows that used in Steve. Next, we validate on more realistic SNANA simulations of SNIa samples from the Dark Energy Survey and low-redshift surveys. These simulated datasets contain more than 60000 SNeIa, 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 SNIa cosmology fitting, and reveals small w-biases that depend on the simulated SNIa properties, in particular the intrinsic SNIa 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,more » and therefore future development will focus on minimising the sensitivity of Steve to the SNIa intrinsic scatter model.« less

Authors:
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Publication Date:
Research Org.:
SLAC National Accelerator Lab., Menlo Park, CA (United States); Univ. of Michigan, Ann Arbor, MI (United States); Fermi National Accelerator Lab. (FNAL), Batavia, IL (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE Office of Science (SC), High Energy Physics (HEP); USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)
Contributing Org.:
DES Collaboration; DES
OSTI Identifier:
1526927
Alternate Identifier(s):
OSTI ID: 1529360; OSTI ID: 1531222; OSTI ID: 1582334; OSTI ID: 1725992
Report Number(s):
arXiv:1811.02381; FERMILAB-PUB-19-050-A-AD-AE-CD
Journal ID: ISSN 1538-4357
Grant/Contract Number:  
AC02-76SF00515; SC0019193; AC02-07CH11359; AC05-00OR22725; AC02-05CH11231
Resource Type:
Accepted Manuscript
Journal Name:
The Astrophysical Journal (Online)
Additional Journal Information:
Journal Name: The Astrophysical Journal (Online); Journal Volume: 876; Journal Issue: 1; Journal ID: ISSN 1538-4357
Publisher:
Institute of Physics (IOP)
Country of Publication:
United States
Language:
English
Subject:
79 ASTRONOMY AND ASTROPHYSICS; cosmology: supernovae

Citation Formats

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., Bertin, E., Brooks, D., Burke, D. L., Rosell, A. Carnero, Kind, M. Carrasco, Carretero, J., Cunha, C. E., Costa, L. N. da, Davis, C., Vicente, J. De, DePoy, D. L., Doel, P., Eifler, T. F., Flaugher, B., Fosalba, P., Frieman, J., García-Bellido, J., Gaztanaga, E., Gerdes, D. W., Gruendl, R. A., Gschwend, J., Gutierrez, G., Hartley, W. G., Hollowood, D. L., Honscheid, K., Krause, E., Kuehn, K., Kuropatkin, N., Lahav, O., Lima, M., Maia, M. A. G., March, M., Marshall, J. L., Menanteau, F., Miquel, R., Ogando, R. L. C., Plazas, A. A., 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., and Zhang, Y. Steve: A Hierarchical Bayesian Model for Supernova Cosmology. United States: N. p., 2019. Web. https://doi.org/10.3847/1538-4357/ab13a3.
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., Bertin, E., Brooks, D., Burke, D. L., Rosell, A. Carnero, Kind, M. Carrasco, Carretero, J., Cunha, C. E., Costa, L. N. da, Davis, C., Vicente, J. De, DePoy, D. L., Doel, P., Eifler, T. F., Flaugher, B., Fosalba, P., Frieman, J., García-Bellido, J., Gaztanaga, E., Gerdes, D. W., Gruendl, R. A., Gschwend, J., Gutierrez, G., Hartley, W. G., Hollowood, D. L., Honscheid, K., Krause, E., Kuehn, K., Kuropatkin, N., Lahav, O., Lima, M., Maia, M. A. G., March, M., Marshall, J. L., Menanteau, F., Miquel, R., Ogando, R. L. C., Plazas, A. A., 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. Steve: A Hierarchical Bayesian Model for Supernova Cosmology. United States. https://doi.org/10.3847/1538-4357/ab13a3
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., Bertin, E., Brooks, D., Burke, D. L., Rosell, A. Carnero, Kind, M. Carrasco, Carretero, J., Cunha, C. E., Costa, L. N. da, Davis, C., Vicente, J. De, DePoy, D. L., Doel, P., Eifler, T. F., Flaugher, B., Fosalba, P., Frieman, J., García-Bellido, J., Gaztanaga, E., Gerdes, D. W., Gruendl, R. A., Gschwend, J., Gutierrez, G., Hartley, W. G., Hollowood, D. L., Honscheid, K., Krause, E., Kuehn, K., Kuropatkin, N., Lahav, O., Lima, M., Maia, M. A. G., March, M., Marshall, J. L., Menanteau, F., Miquel, R., Ogando, R. L. C., Plazas, A. A., 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., and Zhang, Y. Mon . "Steve: A Hierarchical Bayesian Model for Supernova Cosmology". United States. https://doi.org/10.3847/1538-4357/ab13a3. https://www.osti.gov/servlets/purl/1526927.
@article{osti_1526927,
title = {Steve: A Hierarchical Bayesian Model for Supernova Cosmology},
author = {Hinton, S. R. and Davis, T. M. and Kim, A. G. and Brout, D. and D’Andrea, C. B. and Kessler, R. and Lasker, J. and Lidman, C. and Macaulay, E. and Möller, A. and Sako, M. and Scolnic, D. and Smith, M. and Wolf, R. C. and Childress, M. and Morganson, E. and Allam, S. and Annis, J. and Avila, S. and Bertin, E. and Brooks, D. and Burke, D. L. and Rosell, A. Carnero and Kind, M. Carrasco and Carretero, J. and Cunha, C. E. and Costa, L. N. da and Davis, C. and Vicente, J. De and DePoy, D. L. and Doel, P. and Eifler, T. F. and Flaugher, B. and Fosalba, P. and Frieman, J. and García-Bellido, J. and Gaztanaga, E. and Gerdes, D. W. and Gruendl, R. A. and Gschwend, J. and Gutierrez, G. and Hartley, W. G. and Hollowood, D. L. and Honscheid, K. and Krause, E. and Kuehn, K. and Kuropatkin, N. and Lahav, O. and Lima, M. and Maia, M. A. G. and March, M. and Marshall, J. L. and Menanteau, F. and Miquel, R. and Ogando, R. L. C. and Plazas, A. A. and Sanchez, E. and Scarpine, V. and Schindler, R. and Schubnell, M. and Serrano, S. and Sevilla-Noarbe, I. and Soares-Santos, M. and Sobreira, F. and Suchyta, E. and Tarle, G. and Thomas, D. and Vikram, V. and Zhang, Y.},
abstractNote = {We present a new Bayesian hierarchical model (BHM) named Steve for performing type Ia supernova (SNIa) 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, SNIa populations, and systematic uncertainties. Selection effects are characterised using Monte-Carlo simulations. We demonstrate its implementation by fitting realisations of SNIa datasets where the SNIa model closely follows that used in Steve. Next, we validate on more realistic SNANA simulations of SNIa samples from the Dark Energy Survey and low-redshift surveys. These simulated datasets contain more than 60000 SNeIa, 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 SNIa cosmology fitting, and reveals small w-biases that depend on the simulated SNIa properties, in particular the intrinsic SNIa 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, and therefore future development will focus on minimising the sensitivity of Steve to the SNIa intrinsic scatter model.},
doi = {10.3847/1538-4357/ab13a3},
journal = {The Astrophysical Journal (Online)},
number = 1,
volume = 876,
place = {United States},
year = {2019},
month = {4}
}

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