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:2023:1538-4365/ac9d99,
author = {Leistedt, B and Alsing, J and Peiris, H and Mortlock, D and Leja, J},
doi = {1538-4365/ac9d99},
journal = {Astrophysical Journal Supplement Series},
pages = {1--12},
title = {Hierarchical Bayesian inference of photometric redshifts with stellar population synthesis models},
url = {http://dx.doi.org/10.3847/1538-4365/ac9d99},
volume = {264},
year = {2023}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - We present a Bayesian hierarchical framework to analyze photometric galaxy survey data with stellar population synthesis (SPS) models. Our method couples robust modeling of spectral energy distributions with a population model and a noise model to characterize the statistical properties of the galaxy populations and real observations, respectively. By self-consistently inferring all model parameters, from high-level hyperparameters to SPS parameters of individual galaxies, one can separate sources of bias and uncertainty in the data. We demonstrate the strengths and flexibility of this approach by deriving accurate photometric redshifts for a sample of spectroscopically confirmed galaxies in the COSMOS field, all with 26-band photometry and spectroscopic redshifts. We achieve a performance competitive with publicly released photometric redshift catalogs based on the same data. Prior to this work, this approach was computationally intractable in practice due to the heavy computational load of SPS model calls; we overcome this challenge by the addition of neural emulators. We find that the largest photometric residuals are associated with poor calibration for emission-line luminosities and thus build a framework to mitigate these effects. This combination of physics-based modeling accelerated with machine learning paves the path toward meeting the stringent requirements on the accuracy of photometric redshift estimation imposed by upcoming cosmological surveys. The approach also has the potential to create new links between cosmology and galaxy evolution through the analysis of photometric data sets.
AU  - Leistedt,B
AU  - Alsing,J
AU  - Peiris,H
AU  - Mortlock,D
AU  - Leja,J
DO  - 1538-4365/ac9d99
EP  - 12
PY  - 2023///
SN  - 0067-0049
SP  - 1
TI  - Hierarchical Bayesian inference of photometric redshifts with stellar population synthesis models
T2  - Astrophysical Journal Supplement Series
UR  - http://dx.doi.org/10.3847/1538-4365/ac9d99
UR  - https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000911840900001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=a2bf6146997ec60c407a63945d4e92bb
UR  - https://iopscience.iop.org/article/10.3847/1538-4365/ac9d99
UR  - http://hdl.handle.net/10044/1/101757
VL  - 264
ER  -
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