arXiv subjects

Elena Sellentin

Publications and source records attributed to Elena Sellentin.

41 records · Page 3Linked to original sources

arXiv2017

Quantifying lost information due to covariance matrix estimation in parameter inference

Parameter inference with an estimated covariance matrix systematically loses information due to the remaining uncertainty of the covariance matrix. Here, we quantify this loss of precision and develop a framework to hypothetically restore it, which allows to judge how far away a given analysis is from the ideal case of a known covariance matrix. We point out that it is insufficient to estimate this loss by debiasing a Fisher matrix as previously done, due to a fundamental inequality that describes how biases arise in non-linear functions. We therefore develop direct estimators for parameter credibility contours and the figure of merit. We apply our results to DES Science Verification weak lensing data, detecting a 10% loss of information that increases their credibility contours. No significant loss of information is found for KiDS. For a Euclid-like survey, with about 10 nuisance parameters we find that 2900 simulations are sufficient to limit the systematically lost information to 1%, with an additional uncertainty of about 2%. Without any nuisance parameters 1900 simulations are sufficient to only lose 1% of information. We also derive an estimator for the Fisher matrix of the unknown true covariance matrix, two estimators of its inverse with different physical meanings, and an estimator for the optimally achievable figure of merit. The formalism here quantifies the gains to be made by running more simulated datasets, allowing decisions to be made about numbers of simulations in an informed way.

astro-ph.CO↗

arXiv2016

Parameter inference with estimated covariance matrices

When inferring parameters from a Gaussian-distributed data set by computing a likelihood, a covariance matrix is needed that describes the data errors and their correlations. If the covariance matrix is not known a priori, it may be estimated and thereby becomes a random object with some intrinsic uncertainty itself. We show how to infer parameters in the presence of such an estimated covariance matrix, by marginalising over the true covariance matrix, conditioned on its estimated value. This leads to a likelihood function that is no longer Gaussian, but rather an adapted version of a multivariate t-distribution, which has the same numerical complexity as the multivariate Gaussian. As expected, marginalisation over the true covariance matrix improves inference when compared with Hartlap et al.'s method, which uses an unbiased estimate of the inverse covariance matrix but still assumes that the likelihood is Gaussian.

astro-ph.CO↗

arXiv2015

Detecting the cosmological neutrino background in the CMB

Three relativistic particles in addition to the photon are detected in the cosmic microwave background (CMB). In the standard model of cosmology, these are interpreted as the three neutrino species. However, at the time of CMB-decoupling, neutrinos are not only relativistic but they are also freestreaming. Here, we investigate, whether the CMB is sensitive to this defining feature of neutrinos, or whether the CMB-data allow to replace neutrinos with a relativistic fluid. We show that free streaming particles are preferred over a relativistic perfect fluid with $Δχ^2\simeq 21$. We also study the possibility to replace the neutrinos by a viscous fluid and find that a relativistic viscous fluid with either the standard values $c_{\rm eff}^2=c_{\rm vis}^2=1/3$ or best fit values for $c_{\rm eff}^2$ and $c_{\rm vis}^2$ has $Δχ^2=20$ and thus cannot provide a good fit to present CMB data either.

astro-ph.CO↗

arXiv2015

A fast, always positive definite and normalizable approximation of non-Gaussian likelihoods

In this paper we extent the previously published DALI-approximation for likelihoods to cases in which the parameter dependency is in the covariance matrix. The approximation recovers non-Gaussian likelihoods, and reduces to the Fisher matrix approach in the case of Gaussianity. It works with the minimal assumptions of having Gaussian errors on the data, and a covariance matrix that possesses a converging Taylor approximation. The resulting approximation works in cases of severe parameter degeneracies and in cases where the Fisher matrix is singular. It is at least $1000$ times faster than a typical Monte Carlo Markov Chain run over the same parameter space. Two example applications, to cases of extremely non-Gaussian likelihoods, are presented -- one demonstrates how the method succeeds in reconstructing completely a ring-shaped likelihood. A public code is released here: http://lnasellentin.github.io/DALI/

astro-ph.CO↗

arXiv2015

Non-Gaussian forecasts of weak lensing with and without priors

Assuming a Euclid-like weak lensing data set, we compare different methods of dealing with its inherent parameter degeneracies. Including priors into a data analysis can mask the information content of a given data set alone. However, since the information content of a data set is usually estimated with the Fisher matrix, priors are added in order to enforce an approximately Gaussian likelihood. Here, we compare priorless forecasts to more conventional forecasts that use priors. We find strongly non-Gaussian likelihoods for 2d-weak lensing if no priors are used, which we approximate with the DALI-expansion. Without priors, the Fisher matrix of the 2d-weak lensing likelihood includes unphysical values of $Ω_m$ and $h$, since it does not capture the shape of the likelihood well. The Cramer-Rao inequality then does not need to apply. We find that DALI and Monte Carlo Markov Chains predict the presence of a dark energy with high significance, whereas a Fisher forecast of the same data set also allows decelerated expansion. We also find that a 2d-weak lensing analysis provides a sharp lower limit on the Hubble constant of $h > 0.4$, even if the equation of state of dark energy is jointly constrained by the data. This is not predicted by the Fisher matrix and usually masked in other works by a sharp prior on $h$. Additionally, we find that DALI estimates Figures of Merit in the presence of non-Gaussianities better than the Fisher matrix. We additionally demonstrate how DALI allows switching to a Hamiltonian Monte Carlo sampling of a highly curved likelihood with acceptance rates of $\approx 0.5$, an effective covering of the parameter space, and numerically effectively costless leapfrog steps. This shows how quick forecasts can be upgraded to accurate forecasts whenever needed. Results were gained with the public code from http://lnasellentin.github.io/DALI/

astro-ph.CO↗