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Dwarf spheroidal J-factor likelihoods for generalized NFW profiles
Stockholm University, Faculty of Science, The Oskar Klein Centre for Cosmo Particle Physics (OKC). Stockholm University, Faculty of Science, Department of Physics.ORCID iD: 0000-0003-0586-7634
Stockholm University, Faculty of Science, The Oskar Klein Centre for Cosmo Particle Physics (OKC). Stockholm University, Faculty of Science, Department of Physics.
Number of Authors: 42019 (English)In: Monthly notices of the Royal Astronomical Society, ISSN 0035-8711, E-ISSN 1365-2966, Vol. 488, no 2, p. 2616-2628Article in journal (Refereed) Published
Abstract [en]

Indirect detection strategies of particle dark matter (DM) in Dwarf spheroidal satellite galaxies (dSphs) typically entail searching for annihilation signals above the astrophysical background. To robustly compare model predictions with the observed fluxes of product particles, most analyses of astrophysical data - which are generally frequentist - rely on estimating the abundance of DM by calculating the so-called J factor. This quantity is usually inferred from the kinematic properties of the stellar population of a dSph using the Jeans equation, commonly by means of Bayesian techniques that entail the presence (and additional systematic uncertainty) of prior choice. Here, extending earlier work, we develop a scheme to derive the profile likelihood for J factors of dwarf spheroidals for models with five or more free parameters. We validate our method on a publicly available simulation suite, released by the Gaia Challenge, finding satisfactory statistical properties for bias and probability coverage. We present the profile likelihood function and maximum likelihood estimates for the J-factor of 10 dSphs. As an illustration, we apply these profile likelihoods to recently published analyses of gamma-ray data with the Fermi Large Area Telescope to derive new, consistent upper limits on the DM annihilation cross-section. We do this for a subset of systems, generally referred to as classical dwarfs. The implications of these findings for DM searches are discussed, together with future improvements and extensions of this technique.

Place, publisher, year, edition, pages
2019. Vol. 488, no 2, p. 2616-2628
Keywords [en]
galaxies: dwarf, galaxies: kinematics and dynamics, dark matter
National Category
Physical Sciences
Research subject
Physics
Identifiers
URN: urn:nbn:se:su:diva-172982DOI: 10.1093/mnras/stz1871ISI: 000482332500077OAI: oai:DiVA.org:su-172982DiVA, id: diva2:1351492
Available from: 2019-09-16 Created: 2019-09-16 Last updated: 2020-01-23Bibliographically approved
In thesis
1. Dark matter signal normalisation for dwarf spheroidal galaxies: A frequentist analysis of stellar kinematics for indirect Dark Matter searches
Open this publication in new window or tab >>Dark matter signal normalisation for dwarf spheroidal galaxies: A frequentist analysis of stellar kinematics for indirect Dark Matter searches
2019 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Indirect detection strategies of Dark Matter (DM) entail searching for signals of DM annihilation or decay, typically in the form of excess positrons or high-energy photons above the astrophysical background, originating from (inferred) DM-rich environments. Due to their characteristics, dwarf spheroidal satellite galaxies (dSphs) of the Milky Way are considered very promising targets for indirect particle DM identification. To compare model predictions with the observed fluxes of product particles, most analyses of astrophysical data - which are generally performed via frequentist statistics - rely on estimating the abundance of DM by calculating the so-called J-factor. This quantity is usually inferred from the kinematic properties of the stellar population of a dSph, performing a Jeans analysis by means of Bayesian techniques. Previous works have, therefore, combined different statistical methods when analysing astrophysical data from dSphs. This thesis describes the development of a new, fully frequentist approach for constructing the profile likelihood curve for J-factors of dSphs, which can be implemented in indirect DM searches. This method improves upon previous ones by producing data-driven expressions of the likelihood of J, thereby allowing a statistically consistent treatment of the astroparticle and astrometric data from dSphs. Using kinematic data from twenty one satellites of the Milky Way, we derive estimates of their maximum likelihood J-factor and its confidence intervals. The analyses are performed in two different frameworks: the standard scenario of a collisionless DM candidate and the possibility of a self-interacting DM species. In the former case, the obtained J-factors and their uncertainties are consistent with previous, Bayesian-derived values. In the latter, we present prior-less estimates for the Sommerfeld enhanced J-factor of dSphs. In agreement with earlier studies, we find J to be overestimated by several orders of magnitude when DM is allowed is attractively self-interact. In both cases we provide the profile likelihood curves obtained. This technique is validated on a publicly available simulation suite, released by Gaia Challenge, by evaluating its coverage and bias. The results of these tests indicate that the method possesses good statistical properties. Lastly, we discuss the implications of these findings for DM searches, together with future improvements and extensions of this technique.

Place, publisher, year, edition, pages
Stockholm: Department of Physics, Stockholm University, 2019. p. 131
Keywords
dark matter, dwarf galaxies, kinematics and dynamics of galaxies
National Category
Astronomy, Astrophysics and Cosmology
Research subject
Physics
Identifiers
urn:nbn:se:su:diva-165273 (URN)978-91-7797-560-1 (ISBN)978-91-7797-561-8 (ISBN)
Public defence
2019-03-13, FB52, AlbaNova universitetscentrum, Roslagstullsbacken 21, Stockholm, 13:00 (English)
Opponent
Supervisors
Note

At the time of the doctoral defense, the following paper was unpublished and had a status as follows: Paper 3: Manuscript.

Available from: 2019-02-18 Created: 2019-01-23 Last updated: 2019-12-12Bibliographically approved

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