Imperial College London
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ProfessorAlanHeavens

Faculty of Natural Sciences, Department of Physics

Chair in Astrostatistics
 
 
 
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Contact

 

+44 (0)20 7594 2930a.heavens Website

 
 
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Location

 

1018EBlackett LaboratorySouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@article{Heavens:2017:mnras/stx2326,
author = {Heavens, AF and Sellentin, E and de, Mijolla D and Vianello, A},
doi = {mnras/stx2326},
journal = {Monthly Notices of the Royal Astronomical Society},
pages = {4244--4250},
title = {Massive data compression for parameter-dependent covariance matrices},
url = {http://dx.doi.org/10.1093/mnras/stx2326},
volume = {472},
year = {2017}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - We show how the massive data compression algorithm MOPED can be used to reduce, by orders of magnitude, the number of simulated data sets which are required to estimate the covariance matrix required for the analysis of Gaussian-distributed data. This is relevant when the covariance matrix cannot be calculated directly. The compression is especially valuable when the covariance matrix varies with the model parameters. In this case, it may be prohibitively expensive to run enough simulations to estimate the full covariance matrix throughout the parameter space. This compression may be particularly valuable for the next generation of weak lensing surveys, such as proposed for Euclid and Large Synoptic Survey Telescope, for which the number of summary data (such as band power or shear correlation estimates) is very large, ∼104, due to the large number of tomographic redshift bins which the data will be divided into. In the pessimistic case where the covariance matrix is estimated separately for all points in an Monte Carlo Markov Chain analysis, this may require an unfeasible 109 simulations. We show here that MOPED can reduce this number by a factor of 1000, or a factor of ∼106 if some regularity in the covariance matrix is assumed, reducing the number of simulations required to a manageable 103, making an otherwise intractable analysis feasible.
AU  - Heavens,AF
AU  - Sellentin,E
AU  - de,Mijolla D
AU  - Vianello,A
DO  - mnras/stx2326
EP  - 4250
PY  - 2017///
SN  - 0035-8711
SP  - 4244
TI  - Massive data compression for parameter-dependent covariance matrices
T2  - Monthly Notices of the Royal Astronomical Society
UR  - http://dx.doi.org/10.1093/mnras/stx2326
UR  - http://hdl.handle.net/10044/1/50769
VL  - 472
ER  -
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