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Article overview
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Halo detection via large-scale Bayesian inference | | Alexander I. Merson
; Jens Jasche
; Filipe B. Abdalla
; Ofer Lahav
; Benjamin Wandelt
; D. Heath Jones
; Matthew Colless
; | | Date: |
13 May 2015 | | Abstract: | We describe a novel and fully Bayesian approach to detect halos of different
masses in cosmological observations and to quantify corresponding
uncertainties. To demonstrate the capability of this approach we perform a
Bayesian analysis of a realistic spectroscopic galaxy mock catalogue with the
previously developed HADES inference algorithm. This procedure provides us with
accurately inferred three-dimensional density fields and corresponding
quantification of uncertainties inherent to any cosmological observation. Based
upon these results we develop a novel Bayesian methodology to detect halos of
different masses in cosmological observations subject to noise and systematic
uncertainties. Specifically, we use a Bayesian chain rule to connect properties
of halos found in simulations with actual observations. In an entirely Bayesian
manner, this approach returns detection probabilities for halos above specified
mass thresholds throughout the entire observed domain. We present maps of such
detection probabilities and demonstrate the validity of this approach within
our mock scenario. The 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. | | Source: | arXiv, 1505.3528 | | Services: | Forum | Review | PDF | Favorites | |
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