A method for comparing non-nested models with application to astrophysical searches for new physics

Sara Algeri, Jan Conrad, David A. van Dyk

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

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 γ-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 γ-ray satellite, where non-nested hypotheses testing appears in the search for particle dark matter.

Original languageEnglish (US)
Pages (from-to)L84-L88
JournalMonthly Notices of the Royal Astronomical Society: Letters
Volume458
Issue number1
DOIs
StatePublished - May 1 2016
Externally publishedYes

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astrophysics
physics
hypothesis testing
dark matter
rays
physical phenomena
asymptotic properties
likelihood ratio
telescopes
simulation
method
test
decision
comparison
particle
parameter

Keywords

  • Astroparticle physics
  • Darkmatter
  • Methods: data analysis
  • Methods: statistical

Cite this

A method for comparing non-nested models with application to astrophysical searches for new physics. / Algeri, Sara; Conrad, Jan; van Dyk, David A.

In: Monthly Notices of the Royal Astronomical Society: Letters, Vol. 458, No. 1, 01.05.2016, p. L84-L88.

Research output: Contribution to journalArticle

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