Imperial College London
Alternate

ProfessorDanielMortlock

Faculty of Natural Sciences, Department of Physics

Professor of Astrophysics and Statistics
 
 
 
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Contact

 

+44 (0)20 7594 7878d.mortlock Website

 
 
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Location

 

1018ABlackett LaboratorySouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Leistedt:2016:mnras/stw1304,
author = {Leistedt, B and Mortlock, DJ and Peiris, HV},
doi = {mnras/stw1304},
journal = {Monthly Notices of the Royal Astronomical Society},
pages = {4258--4267},
title = {Hierarchical Bayesian inference of galaxy redshift distributions from photometric surveys},
url = {http://dx.doi.org/10.1093/mnras/stw1304},
volume = {460},
year = {2016}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Accurately characterizing the redshift distributions of galaxies is essential for analysing deep photometric surveys and testing cosmological models. We present a technique to simultaneously infer redshift distributions and individual redshifts from photometric galaxy catalogues. Our model constructs a piecewise constant representation (effectively a histogram) of the distribution of galaxy types and redshifts, the parameters of which are efficiently inferred from noisy photometric flux measurements. This approach can be seen as a generalization of template-fitting photometric redshift methods and relies on a library of spectral templates to relate the photometric fluxes of individual galaxies to their redshifts. We illustrate this technique on simulated galaxy survey data, and demonstrate that it delivers correct posterior distributions on the underlying type and redshift distributions, as well as on the individual types and redshifts of galaxies. We show that even with uninformative priors, large photometric errors and parameter degeneracies, the redshift and type distributions can be recovered robustly thanks to the hierarchical nature of the model, which is not possible with common photometric redshift estimation techniques. As a result, redshift uncertainties can be fully propagated in cosmological analyses for the first time, fulfilling an essential requirement for the current and future generations of surveys.
AU  - Leistedt,B
AU  - Mortlock,DJ
AU  - Peiris,HV
DO  - mnras/stw1304
EP  - 4267
PY  - 2016///
SN  - 1365-2966
SP  - 4258
TI  - Hierarchical Bayesian inference of galaxy redshift distributions from photometric surveys
T2  - Monthly Notices of the Royal Astronomical Society
UR  - http://dx.doi.org/10.1093/mnras/stw1304
UR  - http://hdl.handle.net/10044/1/39176
VL  - 460
ER  -
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