Related papers: Parametrizing the local dark matter speed distribu…
We present a new method to estimate redshift distributions and galaxy-dark matter bias parameters using correlation functions in a fully data driven and self-consistent manner. Unlike other machine learning, template, or correlation…
We study the dynamical properties of dark energy based on a large family of PADE parameterizations for which the dark energy density evolves as a ratio between two polynomials in the scale factor of the universe. Using the latest…
The Density Matrix Renormalization Group (DMRG) has become a powerful numerical method that can be applied to low-dimensional strongly correlated fermionic and bosonic systems. It allows for a very precise calculation of static, dynamic and…
To improve the performance in identifying the faults under strong noise for rotating machinery, this paper presents a dynamic feature reconstruction signal graph method, which plays the key role of the proposed end-to-end fault diagnosis…
Several numerical evaluations of the density and distribution of convolution of independent gamma variables are compared in their accuracy and speed. In application to renewal processes, an efficient formula is derived for the probability…
The theoretical interpretation of dark matter (DM) experiments is hindered by uncertainties on the dark matter density and velocity distribution inside the Solar System. In order to quantify those uncertainties, we present a parameter that…
We describe a numerical scheme for evaluating the posterior moments of Bayesian linear regression models with partial pooling of the coefficients. The principal analytical tool of the evaluation is a change of basis from coefficient space…
Observation of gamma-rays from dwarf galaxies is an effective way to search for particle dark matter. Using 4-year data of Fermi-LAT observations on a series of Milky Way satellites, we develop a general way to search for the signals from…
From the distributional characterizations that lie at the heart of Stein's method we derive explicit formulae for the mass functions of discrete probability laws that identify those distributions. These identities are applied to develop…
This paper introduces a novel parameterization to characterize unknown linear time-invariant systems using noisy data. The presented parameterization describes exactly the set of all systems consistent with the available data. We then…
We introduce a method for fast estimation of data-adapted, spatio-temporally dependent regularization parameter-maps for variational image reconstruction, focusing on total variation (TV)-minimization. Our approach is inspired by recent…
A theoretical analysis is given of the equation of motion method, due to Alben et al., to compute the eigenvalue distribution (density of states) of very large matrices. The salient feature of this method is that for matrices of the kind…
Nonparametric estimation of a mixing density based on observations from the corresponding mixture is a challenging statistical problem. This paper surveys the literature on a fast, recursive estimator based on the predictive recursion…
We study the systematic problems in determining the local dark matter density $\rho_{dm}(R_\odot)$ from kinematics of stars in the Solar Neighbourhood, using a simulated Milky Way-like galaxy. We introduce a new unbiased method for…
We study the spectral properties of a class of random matrices where the matrix elements depend exponentially on the distance between uniformly and randomly distributed points. This model arises naturally in various physical contexts, such…
This manuscript presents a diagnostic analysis of three dark energy models resulting from the parametrization of the deceleration parameter. These models exhibit intriguing features, including late-time acceleration and a cosmological phase…
We develop a novel Bayesian methodology aimed at reliably and precisely inferring the distribution of dark matter within the Milky Way using rotation curve data. We identify a subset of the available rotation curve tracers that are mutually…
This paper presents a randomized algorithm for computing the near-optimal low-rank dynamic mode decomposition (DMD). Randomized algorithms are emerging techniques to compute low-rank matrix approximations at a fraction of the cost of…
In this thesis, I study the expected direct and indirect detection signals of dark matter. More precisely, I study three aspects of dark matter; I use hydrodynamic simulations to extract properties of weakly interacting dark matter that are…
We discuss the relevance of directional detection experiments in the post-discovery era and propose a method to extract the local dark matter phase space distribution from directional data. The first feature of this method is a…