Abstract
Recent cosmological analyses rely on the ability to accurately sample from high-dimensional posterior distributions. A variety of algorithms have been applied in the field, but justification of the particular sampler choice and settings is often lacking. Here, we investigate three such samplers to motivate and validate the algorithm and settings used for the Dark Energy Survey (DES) analyses of the first 3 yr (Y3) of data from combined measurements of weak lensing and galaxy clustering. We employ the full DES Year 1 likelihood alongside a much faster approximate likelihood, which enables us to assess the outcomes from each sampler choice and demonstrate the robustness of our full results. We find that the ellipsoidal nested sampling algorithm MULTINEST reports inconsistent estimates of the Bayesian evidence and somewhat narrower parameter credible intervals than the sliced nested sampling implemented in POLYCHORD. We compare the findings from MULTINEST and POLYCHORD with parameter inference from the Metropolis–Hastings algorithm, finding good agreement. We determine that POLYCHORD provides a good balance of speed and robustness for posterior and evidence estimation, and recommend different settings for testing purposes and final chains for analyses with DES Y3 data. Our methodology can readily be reproduced to obtain suitable sampler settings for future surveys.
| Original language | English |
|---|---|
| Pages (from-to) | 1184-1199 |
| Number of pages | 16 |
| Journal | Monthly Notices of the Royal Astronomical Society |
| Volume | 521 |
| Issue number | 1 |
| DOIs | |
| State | Published - May 1 2023 |
Funding
PL acknowledges STFC Consolidated Grants ST/R000476/1 and ST/T000473/1. NW is supported by the Chamberlain fellowship at Lawrence Berkeley National Laboratory. The analysis used the software tools SCIPY (Jones et al. 2001), NUMPY (Oliphant 2006), MATPLOTLIB (Hunter 2007), CAMB (Lewis et al. 2000; Howlett et al. 2012), GETDIST (Lewis 2019), MULTINEST (Feroz & Hobson 2008; Feroz et al. 2009, 2019), POLYCHORD (Handley et al. 2015a, b), ANESTHETIC (Handley 2019), and COSMOSIS (Zuntz et al. 2015). Elements of the DES modelling pipeline additionally use COSMOLIKE (Krause & Eifler 2017), HALOFIT (Bird, Viel & Haehnelt 2012; Takahashi et al. 2012), FAST-PT (McEwen et al. 2016), and NICAEA (Kilbinger et al. 2009). This work was supported through computational resources and services provided by the National Energy Research Scientific Computing Center (NERSC), a U.S. Department of Energy Office of Science User Facility operated under Contract No. DE-AC02-05CH11231; and by the Sherlock cluster, supported by Stanford University and the Stanford Research Computing Center. Funding for the DES Projects has been provided by the U.S. Department of Energy, the U.S. National Science Foundation, the Ministry of Science and Education of Spain, the Science and Technology Facilities Council of the United Kingdom, the Higher Education Funding Council for England, the National Center for Supercomputing Applications at the University of Illinois at Urbana-Champaign, the Kavli Institute of Cosmological Physics at the University of Chicago, the Center for Cosmology and Astro-Particle Physics at the Ohio State University, the Mitchell Institute for Fundamental Physics and Astronomy at Texas A&M University, Financiadora de Estudos e Projetos, Funda\u00E7\u00E3o Carlos Chagas Filho de Amparo \u00E0 Pesquisa do Estado do Rio de Janeiro, Conselho Nacional de Desenvolvimento Cient\u00EDfico e Tecnol\u00F3gico and the Minist\u00E9rio da Ci\u00EAncia, Tecnologia e Inova\u00E7\u00E3o, the Deutsche Forschungsgemeinschaft and the Collaborating Institutions in the Dark Energy Survey. The Collaborating Institutions are Argonne National Laboratory, the University of California at Santa Cruz, the University of Cambridge, Centro de Investigaciones Energ\u00E9ticas, Medioambientales y Tecnol\u00F3gicas-Madrid, the University of Chicago, University College London, the DES-Brazil Consortium, the University of Edinburgh, the Eidgen\u00F6ssische Technische Hochschule (ETH) Z\u00FCrich, Fermi National Accelerator Laboratory, the University of Illinois at Urbana-Champaign, the Institut de Ci\u00E8ncies de l\u2019Espai (IEEC/CSIC), the Institut de F\u00EDsica d\u2019Altes Energies, Lawrence Berkeley National Laboratory, the Ludwig-Maximilians Universit\u00E4t M\u00FCnchen and the associated Excellence Cluster Universe, the University of Michigan, NSF\u2019s NOIRLab, the University of Nottingham, The Ohio State University, the University of Pennsylvania, the University of Portsmouth, SLAC National Accelerator Laboratory, Stanford University, the University of Sussex, Texas A&M University, and the OzDES Membership Consortium. Based in part on observations at Cerro Tololo Inter-American Observatory at NSF\u2019s NOIRLab (NOIRLab Prop. ID 2012B-0001; PI: J. Frieman), which is managed by the Association of Universities for Research in Astronomy (AURA) under a cooperative agreement with the National Science Foundation. The DES data management system is supported by the National Science Foundation under Grant Numbers AST-1138766 and AST-1536171. The DES participants from Spanish institutions are partially supported by MICINN under grants ESP2017-89838, PGC2018-094773, PGC2018-102021, SEV-2016-0588, SEV-2016-0597, and MDM-2015-0509, some of which include ERDF funds from the European Union. IFAE is partially funded by the CERCA program of the Generalitat de Catalunya. Research leading to these results has received funding from the European Research Council under the European Union\u2019s Seventh Framework Program (FP7/2007-2013) including ERC grant agreements 240672, 291329, and 306478. We acknowledge support from the Brazilian Instituto Nacional de Ci\u00EAncia e Tecnologia (INCT) do e-Universo (CNPq grant 465376/2014-2). This manuscript has been authored by Fermi Research Alliance, LLC under Contract No. DE-AC02-07CH11359 with the U.S. Department of Energy, Office of Science, Office of High Energy Physics. The DES data management system is supported by the National Science Foundation under Grant Numbers AST-1138766 and AST-1536171. The DES participants from Spanish institutions are partially supported by MICINN under grants ESP2017-89838, PGC2018-094773, PGC2018-102021, SEV-2016-0588, SEV-2016-0597, and MDM-2015-0509, some of which include ERDF funds from the European Union. IFAE is partially funded by the CERCA program of the Generalitat de Catalunya. Research leading to these results has received funding from the European Research Council under the European Union\u2019s Seventh Framework Program (FP7/2007-2013) including ERC grant agreements 240672, 291329, and 306478. We acknowledge support from the Brazilian Instituto Nacional de Ci\u00EAncia e Tecnologia (INCT) do e-Universo (CNPq grant 465376/2014-2). This work was supported through computational resources and services provided by the National Energy Research Scientific Computing Center (NERSC), a U.S. Department of Energy Office of Science User Facility operated under Contract No. DE-AC02-05CH11231; and by the Sherlock cluster, supported by Stanford University and the Stanford Research Computing Center. PL acknowledges STFC Consolidated Grants ST/R000476/1 and ST/T000473/1. NW is supported by the Chamberlain fellowship at Lawrence Berkeley National Laboratory. Based in part on observations at Cerro Tololo Inter-American Observatory at NSF\u2019s NOIRLab (NOIRLab Prop. ID 2012B-0001; PI: J. Frieman), which is managed by the Association of Universities for Research in Astronomy (AURA) under a cooperative agreement with the National Science Foundation. This manuscript has been authored by Fermi Research Alliance, LLC under Contract No. DE-AC02-07CH11359 with the U.S. Department of Energy, Office of Science, Office of High Energy Physics.
Keywords
- cosmological parameters
- cosmology: observations
- large-scale structure of the Universe
- methods: statistical