Projects per year
Over the past decades Bayesian methods have become increasingly popular in astronomy and physics as stochastic samplers have enabled efficient investigation of high-dimensional likelihood surfaces. In this work we develop a hierarchical Bayesian inference framework to detect the presence of dark matter annihilation events in data from the Cherenkov Telescope Array (CTA). Gamma-ray events are weighted based on their measured sky position Ω̂m and energy Em in order to derive a posterior distribution for the dark matter's velocity averaged cross section 〈σv〉. The dark matter signal model and the astrophysical background model are cast as prior distributions for (Ω̂m , Em ). The shape of these prior distributions can be fixed based on first-principle models; or one may adopt flexible priors to include theoretical uncertainty, for example, in the dark matter annihilation spectrum or the astrophysical distribution of sky location. We demonstrate the utility of this formalism using simulated data with a Galactic Centre signal from scalar singlet dark-matter model. The sensitivity according to our method is comparable to previous estimates of the CTA sensitivity.
- Bayesian reasoning
- dark matter detectors
- gamma ray detectors
Kobakhidze, A., Balazs, C., Ramsey-Musolf, M. J. & Fowlie, A.
13/12/21 → 12/12/24
Bailes, M., McClelland, D. E., Levin, Y., Blair, D. G., Scott, S. M., Ottaway, D. J., Melatos, A., Veitch, P. J., Wen, L., Shaddock, D. A., Slagmolen, B. J. J., Zhao, C., Evans, R. J., Ju, L., Galloway, D., Thrane, E., Hurley, J. R., Coward, D. M., Cooke, J., Couch, W., Hobbs, G. B., Reitze, D., Rowan, S., Cai, R., Adhikari, R. X., Danzmann, K., Mavalvala, N., Kulkarni, S. R., Kramer, M., Branchesi, M., Gehrels, N., Weinstein, A. J. R., Steeghs, D., Bock, D. & Lasky, P.
1/01/17 → 31/03/24
White, M., Balazs, C., Williams, A. G., Athron, P., Roos, L. & Parker, M. A.
1/01/18 → 31/12/20