2019MNRAS.488.2616C -
Mon. Not. R. Astron. Soc., 488, 2616-2628 (2019/September-2)
Dwarf spheroidal J-factor likelihoods for generalized NFW profiles.
CHIAPPO A., COHEN-TANUGI J., CONRAD J. and STRIGARI L.E.
Abstract (from CDS):
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 γ-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.
Abstract Copyright:
© 2019 The Author(s) Published by Oxford University Press on behalf of the Royal Astronomical Society
Journal keyword(s):
galaxies: dwarf - galaxies: kinematics and dynamics - dark matter
Simbad objects:
12
Full paper
View the references in ADS
To bookmark this query, right click on this link: simbad:2019MNRAS.488.2616C and select 'bookmark this link' or equivalent in the popup menu