scholarly journals Impact of spurious shear on cosmological parameter estimates from weak lensing observables

2014 ◽  
Vol 90 (12) ◽  
Author(s):  
Andrea Petri ◽  
Morgan May ◽  
Zoltán Haiman ◽  
Jan M. Kratochvil
Keyword(s):  
Cosmological Parameter ◽  
Weak Lensing ◽  
Parameter Estimates

scholarly journals Parameter inference for weak lensing using Gaussian Processes and MOPED

2020 ◽  
Vol 497 (2) ◽  
pp. 2213-2226
Author(s):  
Arrykrishna Mootoovaloo ◽  
Alan F Heavens ◽  
Andrew H Jaffe ◽  
Florent Leclercq
Keyword(s):  
Gaussian Process ◽  
Gaussian Processes ◽  
Cosmological Parameter ◽  
Weak Lensing ◽  
Summary Statistics ◽  
Parameter Inference ◽  
Leibler Divergence ◽  
Future Data ◽  
Summary Data ◽  
Fast Mechanism

ABSTRACT In this paper, we propose a Gaussian Process (GP) emulator for the calculation both of tomographic weak lensing band powers, and of coefficients of summary data massively compressed with the MOPED algorithm. In the former case cosmological parameter inference is accelerated by a factor of ∼10–30 compared with Boltzmann solver class applied to KiDS-450 weak lensing data. Much larger gains of order 103 will come with future data, and MOPED with GPs will be fast enough to permit the Limber approximation to be dropped, with acceleration in this case of ∼105. A potential advantage of GPs is that an error on the emulated function can be computed and this uncertainty incorporated into the likelihood. However, it is known that the GP error can be unreliable when applied to deterministic functions, and we find, using the Kullback–Leibler divergence between the emulator and class likelihoods, and from the uncertainties on the parameters, that agreement is better when the GP uncertainty is not used. In future, weak lensing surveys such as Euclid, and the Legacy Survey of Space and Time, will have up to ∼104 summary statistics, and inference will be correspondingly more challenging. However, since the speed of MOPED is determined not the number of summary data, but by the number of parameters, MOPED analysis scales almost perfectly, provided that a fast way to compute the theoretical MOPED coefficients is available. The GP provides such a fast mechanism.

scholarly journals Learn-as-you-go acceleration of cosmological parameter estimates

2015 ◽  
Vol 2015 (09) ◽  
pp. 005-005 ◽  
Author(s):  
Grigor Aslanyan ◽  
Richard Easther ◽  
Layne C. Price
Keyword(s):  
Cosmological Parameter ◽  
Parameter Estimates
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