Learn-as-you-go acceleration of cosmological parameter estimates

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dc.contributor.author Aslanyan, G en
dc.contributor.author Easther, Richard en
dc.contributor.author Price, LC en
dc.date.accessioned 2016-03-11T03:46:02Z en
dc.date.issued 2015-09-01 en
dc.identifier.citation Journal of Cosmology and Astroparticle Physics, 2015, 2015 (9) en
dc.identifier.uri http://hdl.handle.net/2292/28448 en
dc.description.abstract Cosmological analyses can be accelerated by approximating slow calculations using a training set, which is either precomputed or generated dynamically. However, this approach is only safe if the approximations are well understood and controlled. This paper surveys issues associated with the use of machine-learning based emulation strategies for accelerating cosmological parameter estimation. We describe a learn-as-you-go algorithm that is implemented in the Cosmo++ code and (1) trains the emulator while simultaneously estimating posterior probabilities; (2) identifies unreliable estimates, computing the exact numerical likelihoods if necessary; and (3) progressively learns and updates the error model as the calculation progresses. We explicitly describe and model the emulation error and show how this can be propagated into the posterior probabilities. We apply these techniques to the Planck likelihood and the calculation of ΛCDM posterior probabilities. The computation is significantly accelerated without a pre-defined training set and uncertainties in the posterior probabilities are subdominant to statistical fluctuations. We have obtained a speedup factor of 6.5 for Metropolis-Hastings and 3.5 for nested sampling. Finally, we discuss the general requirements for a credible error model and show how to update them on-the-fly. en
dc.relation.ispartofseries Journal of Cosmology and Astroparticle Physics en
dc.rights Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher. Details obtained from http://www.sherpa.ac.uk/romeo/issn/1475-7516/ en
dc.rights.uri https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm en
dc.title Learn-as-you-go acceleration of cosmological parameter estimates en
dc.type Journal Article en
dc.identifier.doi 10.1088/1475-7516/2015/09/005 en
pubs.issue 9 en
pubs.volume 2015 en
dc.description.version Preprint en
dc.rights.accessrights http://purl.org/eprint/accessRights/OpenAccess en
pubs.subtype Article en
pubs.elements-id 488307 en
pubs.org-id Science en
pubs.org-id Physics en
pubs.arxiv-id 1506.01079 en
dc.identifier.eissn 1475-7516 en
pubs.record-created-at-source-date 2016-03-11 en

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