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Dark matter self-scattering is one of key ingredients for small-scale structure of the Universe, while dark matter annihilation is important for the indirect measurements. There is a strong correlation between the velocity-dependent…

High Energy Physics - Phenomenology · Physics 2024-03-22 Ayuki Kamada , Takumi Kuwahara , Ami Patel

Weak lensing surveys are expected to provide direct measurements of the statistics of the projected dark matter distribution. Most analytical studies of weak lensing statistics have been limited to quasilinear scales as they relied on…

Astrophysics · Physics 2009-10-31 Dipak Munshi , Bhuvnesh Jain

Tracer tests in natural porous media sometimes show abnormalities that suggest considering a fractional variant of the Advection Diffusion Equation supplemented by a time derivative of non-integer order. We are describing an inverse method…

Classical Physics · Physics 2017-06-30 Boris Maryshev , Alain Cartalade , Christelle Latrille , Marie-Christine Néel

A new first-order theory of relativistic dissipation has been recently proposed, where viscous effects are incorporated using the traditional Navier-Stokes framework. Its main novelty is the avoidance of dynamical instabilities by allowing…

General Relativity and Quantum Cosmology · Physics 2025-08-27 Lorenzo Gavassino

This article investigates the averaging of a scalar degree of freedom that couples universally to matter. It quantifies the approximation of smoothing the matter distribution before solving the Klein--Gordon equation. In the case of Yukawa…

General Relativity and Quantum Cosmology · Physics 2025-05-08 Jean-Philippe Uzan , Hugo Lévy

We prove two basic conjectures on the distribution of the smallest singular value of random n times n matrices with independent entries. Under minimal moment assumptions, we show that the smallest singular value is of order n^{-1/2}, which…

Probability · Mathematics 2016-12-23 Mark Rudelson , Roman Vershynin

Kaczmarz method is one popular iterative method for solving inverse problems, especially in computed tomography. Recently, it was established that a randomized version of the method enjoys an exponential convergence for well-posed problems,…

Numerical Analysis · Mathematics 2017-12-06 Yuling Jiao , Bangti Jin , Xiliang Lu

We derive asymptotic estimates for distribution functions related to the Schinzel-Szekeres function. These results are then used in three different applications: the longest simple path in the divisor graph, a problem of Erd\H{o}s about a…

Number Theory · Mathematics 2025-06-16 Andreas Weingartner

The problem of modeling the relationship between univariate distributions and one or more explanatory variables has found increasing interest. Traditional functional data methods cannot be applied directly to distributional data because of…

Methodology · Statistics 2025-02-04 Yidong Zhou , Hans-Georg Müller

We study the problem of measurement-induced decoherence using the phase-space approach employing the Gaussian-smoothed Wigner distribution function. Our investigation is based on the notion that measurement-induced decoherence is…

Quantum Physics · Physics 2009-11-10 Yong-Jin Chun , Hai-Woong Lee

We describe an efficient algorithm for calculating the statistics of weak lensing by large-scale structure based on a tiled set of independent particle-mesh N-body simulations which telescope in resolution along the line of sight. This…

Astrophysics · Physics 2008-11-26 Martin White , Wayne Hu

A popular class of problem in statistics deals with estimating the support of a density from $n$ observations drawn at random from a $d$-dimensional distribution. The one-dimensional case reduces to estimating the end points of a univariate…

Statistics Theory · Mathematics 2018-04-27 Victor-Emmanuel Brunel , Jason M. Klusowski , Dana Yang

Deconvolution is a statistical inverse problem to estimate the distribution of a random variable based on its noisy observations. Despite the extensive studies on the topic, deconvolution with unknown noise distribution remains as a…

Statistics Theory · Mathematics 2020-04-06 Devavrat Shah , Dogyoon Song

Fitting the model "A" to dark matter direct detection data, when the model that underlies the data is "B", introduces a theoretical bias in the fit. We perform a quantitative study of the theoretical bias in dark matter direct detection,…

High Energy Physics - Phenomenology · Physics 2015-07-20 Riccardo Catena

We study the problem of estimating the coefficients of a diffusion (X_t,t\geq 0); the estimation is based on discrete data X_{n\Delta},n=0,1,...,N. The sampling frequency \Delta^{-1} is constant, and asymptotics are taken as the number N of…

Statistics Theory · Mathematics 2007-06-13 Emmanuel Gobet , Marc Hoffmann , Markus Reiss

Topic of this article are tomographic measurements of the integrated Sachs-Wolfe effect with specifically designed, orthogonal polynomials which project out statistically independent modes of the galaxy distribution. The polynomials are…

Cosmology and Nongalactic Astrophysics · Physics 2015-06-03 Gero Jürgens , Bjoern Malte Schäfer

The presence of dark energy in the Universe is inferred directly from the accelerated expansion of the Universe, and indirectly, from measurements of cosmic microwave background (CMB) anisotropy. Dark energy contributes about 2/3 of the…

Astrophysics · Physics 2011-05-05 Dragan Huterer , Michael S. Turner

This paper is concerned with an inverse random source problem for the one-dimensional stochastic Helmholtz equation with attenuation. The source is assumed to be a microlocally isotropic Gaussian random field with its covariance operator…

Numerical Analysis · Mathematics 2020-09-30 Peijun Li , Xu Wang

A 3-D inverse medium problem in the frequency domain is considered. Another name for this problem is Coefficient Inverse Problem. The goal is to reconstruct spatially distributed dielectric constants from scattering data. Potential…

Numerical Analysis · Mathematics 2016-05-23 Michael V. Klibanov , Hui Liu , Loc H. Nguyen

We introduce and initiate the study of new parameters associated with any norm and any log-concave measure on $\mathbb R^n$, which provide sharp distributional inequalities. In the Gaussian context this investigation sheds light to the…

Functional Analysis · Mathematics 2017-10-23 Grigoris Paouris , Petros Valettas