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A method for comparing non-nested models with application to astrophysical searches for new physics
Stockholm University, Faculty of Science, Department of Physics. Imperial College London, UK.ORCID iD: 0000-0001-7366-3866
Stockholm University, Faculty of Science, Department of Physics. Stockholm University, Faculty of Science, The Oskar Klein Centre for Cosmo Particle Physics (OKC). Imperial College London, UK.
Number of Authors: 32016 (English)In: Monthly notices of the Royal Astronomical Society, ISSN 0035-8711, E-ISSN 1365-2966, Vol. 458, no 1, p. L84-L88Article in journal (Refereed) Published
Abstract [en]

Searches for unknown physics and decisions between competing astrophysical models to explain data both rely on statistical hypothesis testing. The usual approach in searches for new physical phenomena is based on the statistical likelihood ratio test and its asymptotic properties. In the common situation, when neither of the two models under comparison is a special case of the other i.e. when the hypotheses are non-nested, this test is not applicable. In astrophysics, this problem occurs when two models that reside in different parameter spaces are to be compared. An important example is the recently reported excess emission in astrophysical gamma-rays and the question whether its origin is known astrophysics or dark matter. We develop and study a new, simple, generally applicable, frequentist method and validate its statistical properties using a suite of simulations studies. We exemplify it on realistic simulated data of the Fermi-Large Area Telescope gamma-ray satellite, where non-nested hypotheses testing appears in the search for particle dark matter.

Place, publisher, year, edition, pages
2016. Vol. 458, no 1, p. L84-L88
Keywords [en]
astroparticle physics, methods: data analysis, methods: statistical, dark matter
National Category
Physical Sciences
Identifiers
URN: urn:nbn:se:su:diva-175557DOI: 10.1093/mnrasl/slw025ISI: 000374226300018OAI: oai:DiVA.org:su-175557DiVA, id: diva2:1368821
Available from: 2019-11-08 Created: 2019-11-08 Last updated: 2019-11-08Bibliographically approved

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Algeri, SaraConrad, Jan
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