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Accurately measuring magnetic field strength in the interstellar medium, including giant molecular clouds (GMCs), remains a significant challenge. We present a machine learning approach using Denoising Diffusion Probabilistic Models (DDPMs)…

Astrophysics of Galaxies · Physics 2025-01-09 Duo Xu , Jenna Karcheski , Chi-Yan Law , Ye Zhu , Chia-Jung Hsu , Jonathan C. Tan

The one-point statistics of column density distributions of turbulent molecular cloud models are investigated and compared with observations. In agreement with the observations, the number N of pixels with surface density S is distributed…

Astrophysics · Physics 2007-05-23 Andreas Burkert , Mordecai-Mark Mac Low

We present a non-perturbative calculation of the 1-point probability distribution function (PDF) for the spherically-averaged matter density field. The PDF is represented as a path integral and is evaluated using the saddle-point method. It…

Cosmology and Nongalactic Astrophysics · Physics 2019-03-20 Mikhail M. Ivanov , Alexander A. Kaurov , Sergey Sibiryakov

We present experimental results on statistics of polymer orientation angles relatively to shear plane and tumbling times in shear flow with thermal noise. Strong deviation of probability distribution functions (PDF) of these parameters from…

Chaotic Dynamics · Physics 2009-11-11 Sergiy Gerashchenko , Victor Steinberg

Complex turbulent motions of magnetized gas are ubiquitous in the interstellar medium. The source of this turbulence, however, is still poorly understood. Previous work suggests that compression caused by supernova shockwaves, gravity, or…

Astrophysics of Galaxies · Physics 2020-02-26 Ankush Mandal , Christoph Federrath , Bastian Körtgen

The properties of the probability distribution function of the cosmological continuous density field are studied. We present further developments and compare dynamically motivated methods to derive the PDF. One of them is based on the…

Astrophysics · Physics 2009-10-22 F. Bernardeau , L. Kofman

CDF2PDF is a method of PDF estimation by approximating CDF. The original idea of it was previously proposed in [1] called SIC. However, SIC requires additional hyper-parameter tunning, and no algorithms for computing higher order derivative…

Machine Learning · Statistics 2018-04-17 Shengdong Zhang

The calculation of the molecular column density from molecular spectral (rotational or ro-vibrational) transition measurements is one of the most basic quantities derived from molecular spectroscopy. Starting from first principles where we…

Instrumentation and Methods for Astrophysics · Physics 2017-04-10 Jeffrey G. Mangum , Yancy L. Shirley

A computer simulation model is used to study the density profile and flow of a miscible gaseous fluid mixture consisting of differing constituent masses ($m_A = m_B/3$) through an open matrix. The density profile is found to decay with the…

Statistical Mechanics · Physics 2009-11-07 R. B. Pandey , D. Stauffer , R. Seyfarth , Luis A. Cueva , J. F. Gettrust , Warren Wood

The probability density function (PDF) associated with a given set of samples is approximated by a piecewise-linear polynomial constructed with respect to a binning of the sample space. The kernel functions are a compactly supported basis…

Numerical Analysis · Mathematics 2020-08-04 Giacomo Capodaglio , Max Gunzburger

One of the fundamental problems in machine learning is the estimation of a probability distribution from data. Many techniques have been proposed to study the structure of data, most often building around the assumption that observations…

Machine Learning · Statistics 2013-02-22 Oren Rippel , Ryan Prescott Adams

We present an analytical model for density-split correlation functions, that probe galaxy clustering in different density environments. Specifically, we focus on the cross-correlation between density-split regions and the tracer density…

Cosmology and Nongalactic Astrophysics · Physics 2025-06-23 Mathilde Pinon , Arnaud de Mattia , Étienne Burtin , Vanina Ruhlmann-Kleider , Sandrine Codis , Enrique Paillas , Carolina Cuesta-Lazaro

In many scientific applications, the target probability distribution cannot be evaluated in closed form or sampled from directly. Instead, it can often be decomposed into multiple components, some of which are accessible only through…

Methodology · Statistics 2026-03-10 Roxana Darvishi , David C. Stenning , Ted von Hippel , Owen G. Ward

The probability distribution function (PDF) tails of the zonal flow structure formation and the PDF tails of momentum flux by incorporating effect of a shear flow in ion-temperature-gradient (ITG) turbulence are computed in the present…

Plasma Physics · Physics 2009-11-13 Johan Anderson , Eun-jin Kim

We discuss an approach to probabilistic forecasting based on two chained machine-learning steps: a dimensional reduction step that learns a reduction map of predictor information to a low-dimensional space in a manner designed to preserve…

Machine Learning · Statistics 2022-03-28 Nick Rittler , Carlo Graziani , Jiali Wang , Rao Kotamarthi

This paper delves into the significance of the tomographic probability density function (pdf) representation of quantum states, shedding light on the special classes of pdfs that can be tomograms. Instead of using wave functions or density…

Quantum Physics · Physics 2024-01-23 L. A. Markovich , J. Urbanetz , V. I. Man'ko

The molecular rearrangements of most fluids under flow and deformation do not directly follow the macroscopic strain field. In this work, we describe a phenomenological method for characterizing such non-affine deformation via the…

Soft Condensed Matter · Physics 2018-01-17 Guan-Rong Huang , Bin Wu , Yangyang Wang , Wei-Ren Chen

We derive an analytical theory of the PDF of density fluctuations in supersonic turbulence in the presence of gravity in star-forming clouds. The theory is based on a rigorous derivation of a combination of the Navier-Stokes continuity…

Astrophysics of Galaxies · Physics 2020-11-11 Etienne Jaupart , Gilles Chabrier

Using a probabilistic neural network and Lagrangian observations from the Global Drifter Program, we model the single particle transition probability density function (pdf) of ocean surface drifters. The transition pdf is represented by a…

Atmospheric and Oceanic Physics · Physics 2023-07-12 Martin T. Brolly

Fractional Brownian motion (fBm) structures are used to investigate the dependency of column density variance ({\sigma}_{\ln N}^2) in the turbulent interstellar medium on the variance of three-dimensional density ({\sigma}_{\ln\rho}^2) and…

Astrophysics of Galaxies · Physics 2015-06-12 Kwang-Il Seon
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