Merson, AI;
Jasche, J;
Abdalla, FB;
Lahav, O;
Wandelt, B;
Jones, DH;
Colless, M;
(2016)
Halo detection via large-scale Bayesian inference.
Monthly Notices of the Royal Astronomical Society (MNRAS)
, 460
(2)
pp. 1340-1355.
10.1093/mnras/stw948.
Preview |
Text
MNRAS-2016-Merson-1340-55.pdf - Published Version Download (5MB) | Preview |
Abstract
We present a proof-of-concept of a novel and fully Bayesian methodology designed to detect haloes of different masses in cosmological observations subject to noise and systematic uncertainties. Our methodology combines the previously published Bayesian large-scale structure inference algorithm, HAmiltonian Density Estimation and Sampling algorithm (HADES), and a Bayesian chain rule (the Blackwell–Rao estimator), which we use to connect the inferred density field to the properties of dark matter haloes. To demonstrate the capability of our approach, we construct a realistic galaxy mock catalogue emulating the wide-area 6-degree Field Galaxy Survey, which has a median redshift of approximately 0.05. Application of HADES to the catalogue provides us with accurately inferred three-dimensional density fields and corresponding quantification of uncertainties inherent to any cosmological observation. We then use a cosmological simulation to relate the amplitude of the density field to the probability of detecting a halo with mass above a specified threshold. With this information, we can sum over the HADES density field realisations to construct maps of detection probabilities and demonstrate the validity of this approach within our mock scenario. We find that the probability of successful detection of haloes in the mock catalogue increases as a function of the signal to noise of the local galaxy observations. Our proposed methodology can easily be extended to account for more complex scientific questions and is a promising novel tool to analyse the cosmic large-scale structure in observations.
| Type: | Article |
|---|---|
| Title: | Halo detection via large-scale Bayesian inference |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1093/mnras/stw948 |
| Publisher version: | http://doi.org/10.1093/mnras/stw948 |
| Language: | English |
| Additional information: | This article has been accepted for publication in Monthly Notices of the Royal Astronomical Society ©: 2016 The Authors. Published by Oxford University Press on behalf of the Royal Astronomical Society. All rights reserved. |
| Keywords: | Science & Technology, Physical Sciences, Astronomy & Astrophysics, methods: numerical, methods: statistical, galaxies: haloes, galaxies: clusters: general, dark matter, large-scale structure of Universe, DIGITAL SKY SURVEY, 6DF GALAXY SURVEY, POWER-SPECTRUM INFERENCE, MASS ASSEMBLY GAMA, X-RAY-CLUSTERS, DATA RELEASE, DARK-MATTER, WIENER RECONSTRUCTION, LUMINOSITY FUNCTIONS, SPECTROSCOPY SYSTEM |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Maths and Physical Sciences > Dept of Physics and Astronomy |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/1486077 |
Archive Staff Only
 |
View Item |
