scholarly journals Data compression and covariance matrix inspection: Cosmic shear

2021 ◽  
Vol 103 (10) ◽  
Author(s):  
Tassia Ferreira ◽  
Tianqing Zhang ◽  
Nianyi Chen ◽  
Scott Dodelson ◽  
Keyword(s):  
Data Compression ◽  
Covariance Matrix ◽  
Cosmic Shear

scholarly journals Cosmic shear tomography and efficient data compression using COSEBIs

2012 ◽  
Vol 542 ◽  
pp. A122 ◽  
Author(s):  
M. Asgari ◽  
P. Schneider ◽  
P. Simon
Keyword(s):  
Data Compression ◽  
Efficient Data ◽  
Cosmic Shear

scholarly journals Cosmic shear covariance matrix in wCDM: Cosmology matters

2019 ◽  
Vol 631 ◽  
pp. A160 ◽  
Author(s):  
J. Harnois-Déraps ◽  
B. Giblin ◽  
B. Joachimi
Keyword(s):  
Covariance Matrix ◽  
Gaussian Process Regression ◽  
Analytical Models ◽  
Test Case ◽  
Sampling Variance ◽  
Two Dimensional ◽  
Brute Force ◽  
Order Of Magnitude ◽  
Cosmic Shear ◽  
Parameter Constraints

We present here the cosmo-SLICS, a new suite of simulations specially designed for the analysis of current and upcoming weak lensing data beyond the standard two-point cosmic shear. We sampled the [Ωm, σ8, h, w0] parameter space at 25 points organised in a Latin hyper-cube, spanning a range that contains most of the 2σ posterior distribution from ongoing lensing surveys. At each of these nodes we evolved a pair of N-body simulations in which the sampling variance is highly suppressed, and ray-traced the volumes 800 times to further increase the effective sky coverage. We extracted a lensing covariance matrix from these pseudo-independent light-cones and show that it closely matches a brute-force construction based on an ensemble of 800 truly independent N-body runs. More precisely, a Fisher analysis reveals that both methods yield marginalized two-dimensional constraints that vary by less than 6% in area, a result that holds under different survey specifications and that matches to within 15% the area obtained from an analytical covariance calculation. Extending this comparison with our 25 wCDM models, we probed the cosmology dependence of the lensing covariance directly from numerical simulations, reproducing remarkably well the Fisher results from the analytical models at most cosmologies. We demonstrate that varying the cosmology at which the covariance matrix is evaluated in the first place might have an order of magnitude greater impact on the parameter constraints than varying the choice of covariance estimation technique. We present a test case in which we generate fast predictions for both the lensing signal and its associated variance with a flexible Gaussian process regression emulator, achieving an accuracy of a few percent on the former and 10% on the latter.

scholarly journals Massive data compression for parameter-dependent covariance matrices

2017 ◽  
Vol 472 (4) ◽  
pp. 4244-4250 ◽  
Author(s):  
Alan F. Heavens ◽  
Elena Sellentin ◽  
Damien de Mijolla ◽  
Alvise Vianello
Keyword(s):  
Data Compression ◽  
Covariance Matrix ◽  
Simulated Data ◽  
Model Parameters ◽  
Massive Data ◽  
Data Sets ◽  
Distributed Data ◽  
Chain Analysis ◽  
Parameter Dependent ◽  
Simulated Data Sets

Abstract 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.

scholarly journals A blinding solution for inference from astronomical data

2020 ◽  
Vol 492 (3) ◽  
pp. 3396-3407 ◽  
Author(s):  
Elena Sellentin
Keyword(s):  
Covariance Matrix ◽  
Direct Evidence ◽  
Uncertainty Assessment ◽  
Theoretical Physics ◽  
Distributed Data ◽  
Post Processing ◽  
Astronomical Data ◽  
Processing Step ◽  
Cosmic Shear ◽  
Physical Laws

ABSTRACT This paper presents a joint blinding and deblinding strategy for inference of physical laws from astronomical data. The strategy allows for up to three blinding stages, where the data may be blinded, the computations of theoretical physics may be blinded, and –assuming Gaussianly distributed data – the covariance matrix may be blinded. We found covariance blinding to be particularly effective, as it enables the blinder to determine close to exactly where the blinded posterior will peak. Accordingly, we present an algorithm which induces posterior shifts in predetermined directions by hiding untraceable biases in a covariance matrix. The associated deblinding takes the form of a numerically lightweight post-processing step, where the blinded posterior is multiplied with deblinding weights. We illustrate the blinding strategy for cosmic shear from KiDS-450, and show that even though there is no direct evidence of the KiDS-450 covariance matrix being biased, the famous cosmic shear tension with Planck could easily be induced by a mischaracterization of correlations between ξ− at the highest redshift and all lower redshifts. The blinding algorithm illustrates the increasing importance of accurate uncertainty assessment in astronomical inferences, as otherwise involuntary blinding through biases occurs.

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