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Experimental data in particle and nuclear physics, particle astrophysics, and radiation protection dosimetry are collected using experimental facilities that consist of a complex system of sensors, electronics, and software. Measured…

Data Analysis, Statistics and Probability · Physics 2026-03-04 Nikolay D. Gagunashvili

This work presents a methodological approach to generate realistic $\gamma$-ray light curves of pulsars, resembling reasonably well the observational ones observed by the Fermi-Large Area Telescope instrument, fitting at the same time their…

High Energy Astrophysical Phenomena · Physics 2024-04-03 Daniel Íñiguez-Pascual , Diego F. Torres , Daniele Viganò

The use of artificial gauge fields enables systems of uncharged particles to behave as if affected by external fields. Generated by geometry or external modulation, artificial gauge fields have been instrumental in demonstrating topological…

Physics has been transforming our view of nature for centuries. While combining physical knowledge with computational approaches has enabled detailed modeling of physical systems' evolution, understanding the emergence of patterns and…

Computational Physics · Physics 2025-06-09 Guang-Xing Li

A method to approximate continuous multi-dimensional probability density functions (PDFs) using their projections and correlations is described. The method is particularly useful for event classification when estimates of systematic…

Data Analysis, Statistics and Probability · Physics 2009-10-31 Dean Karlen

We consider the statistics of light amplitude fluctuations for the propagation of a laser beam subjected to multiple filamentation in an amplified Kerr media, with both linear and nonlinear dissipation. Dissipation arrests the catastrophic…

Chaotic Dynamics · Physics 2010-10-21 Pavel M. Lushnikov , Natalia Vladimirova

In many areas of applied statistics and machine learning, generating an arbitrary number of independent and identically distributed (i.i.d.) samples from a given distribution is a key task. When the distribution is known only through…

Artificial Intelligence · Computer Science 2021-10-29 Ulysse Marteau-Ferey , Francis Bach , Alessandro Rudi

Generating 3D shapes from single RGB images is essential in various applications such as robotics. Current approaches typically target images containing clear and complete visual descriptions of the object, without considering common…

Computer Vision and Pattern Recognition · Computer Science 2024-11-05 Yiheng Xiong , Angela Dai

In probability density function (PDF) methods a transport equation is solved numerically to compute the time and space dependent probability distribution of several flow variables in a turbulent flow. The joint PDF of the velocity…

Fluid Dynamics · Physics 2010-06-04 J. Bakosi , P. Franzese , Z. Boybeyi

We present a fast algorithm to produce light curves of distant stars undergoing microlensing near critical curves. The need of these type of algorithms is motivated by recent observations of microlensing events of distant stars at high…

Instrumentation and Methods for Astrophysics · Physics 2022-10-05 J. M. Diego

Perturbation Theory (PT) applied to a cosmological density field with Gaussian initial fluctuations suggests a specific hierarchy for the correlation functions when the variance is small. In particular quantitative predictions have been…

Astrophysics · Physics 2009-10-30 D. Munshi , F. Bernardeau , A. L. Melott , R. Schaeffer

We present a probabilistic generative approach for constructing topographic maps of light curves from eclipsing binary stars. The model defines a low-dimensional manifold of local noise models induced by a smooth non-linear mapping from a…

Solar and Stellar Astrophysics · Physics 2009-09-23 Nikolaos Gianniotis , Peter Tino , Steve Spreckley , Somak Raychaudhury

The idea of computer vision as the Bayesian inverse problem to computer graphics has a long history and an appealing elegance, but it has proved difficult to directly implement. Instead, most vision tasks are approached via complex…

Artificial Intelligence · Computer Science 2013-07-02 Vikash K. Mansinghka , Tejas D. Kulkarni , Yura N. Perov , Joshua B. Tenenbaum

Context. Whenever correlation functions are used for inference about cosmological parameters in the context of a Bayesian analysis, the likelihood function of correlation functions needs to be known. Usually, it is approximated as a…

Cosmology and Nongalactic Astrophysics · Physics 2013-08-06 Philipp Wilking , Peter Schneider

Theoretical investigations into the deflection angle caused by microlenses offer a direct path to uncovering principles of the cosmological microlensing effect. This work specifically concentrates on the the probability density function…

Astrophysics of Galaxies · Physics 2023-08-30 Wenwen Zheng , Hou-Zun Chen , Xuechun Chen , Guoliang Li

The probability distribution function (PDF) of the mass surface density is an essential characteristic of the structure of molecular clouds or the interstellar medium in general. Observations of the PDF of molecular clouds indicate a…

Astrophysics of Galaxies · Physics 2015-06-18 Joerg Fischera

In this study, we demonstrate some of the caveats in common statistical methods used for analysing astronomical variability timescales. We consider these issues specifically in the context of active galactic nuclei (AGNs) and use a more…

Astrophysics of Galaxies · Physics 2024-12-12 Sofia Kankkunen , Merja Tornikoski , Talvikki Hovatta

Experimental data in Particle and Nuclear physics, Particle Astrophysics and Radiation Protection Dosimetry are obtained from experimental facilities comprising a complex array of sensors, electronics and software. Computer simulation is…

Data Analysis, Statistics and Probability · Physics 2025-03-06 Nikolay D. Gagunashvili

One-point probability distribution functions (PDFs) of the cosmic matter density are powerful cosmological probes that extract non-Gaussian properties of the matter distribution and complement two-point statistics. Computing the covariance…

Cosmology and Nongalactic Astrophysics · Physics 2023-01-09 Cora Uhlemann , Oliver Friedrich , Aoife Boyle , Alex Gough , Alexandre Barthelemy , Francis Bernardeau , Sandrine Codis

In light of the widespread success of generative models, a significant amount of research has gone into speeding up their sampling time. However, generative models are often sampled multiple times to obtain a diverse set incurring a cost…

Machine Learning · Computer Science 2023-11-27 Gabriele Corso , Yilun Xu , Valentin de Bortoli , Regina Barzilay , Tommi Jaakkola