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Under ideal conditions, the probability density function (PDF) of a random variable, such as a sensor measurement, would be well known and amenable to computation and communication tasks. However, this is often not the case, so the user…

Statistics Theory · Mathematics 2022-07-29 Shane Lubold , Clark N. Taylor

We present a new approach to automate the spectroscopic redshift reliability assessment based on machine learning (ML) and characteristics of the redshift probability density function (PDF). We propose to rephrase the spectroscopic redshift…

Instrumentation and Methods for Astrophysics · Physics 2018-04-04 S. Jamal , V. Le Brun , O. Le Fèvre , D. Vibert , A. Schmitt , C. Surace , Y. Copin , B. Garilli , M. Moresco , L. Pozzetti

Deep redshift surveys of the universe provide the basic ingredients to compute the probability distribution function (PDF) of galaxy fluctuations and to constrain its evolution with cosmic time. When this statistic is combined with…

Astrophysics · Physics 2008-02-13 C. Marinoni , O. Le Fevre , B. Meneux , the VVDS team

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

As a step towards a more accurate modelling of redshift-space distortions in galaxy surveys, we develop a general description of the probability distribution function of galaxy pairwise velocities within the framework of the so-called…

Cosmology and Nongalactic Astrophysics · Physics 2015-02-04 Davide Bianchi , Matteo Chiesa , Luigi Guzzo

In the context of count-in-cells statistics, the joint probability distribution of the density in two concentric spherical shells is predicted from first first principle for sigmas of the order of one. The agreement with simulation is found…

Cosmology and Nongalactic Astrophysics · Physics 2015-06-23 Francis Bernardeau , Sandrine Codis , Christophe Pichon

Lagrangian algorithms to simulate the evolution of cold dark matter (CDM) are invaluable tools to generate large suites of mock halo catalogues. In this paper, we first show that the main limitation of current semi-analytical schemes to…

Cosmology and Nongalactic Astrophysics · Physics 2021-06-04 Federico Tosone , Mark C. Neyrinck , Benjamin R. Granett , Luigi Guzzo , Nicola Vittorio

A sample of eight quasars observed at high resolution and signal-to-noise is used to determine the probability distribution function (PDF), the power spectrum, and the correlation function of the transmitted flux in the \lya forest, in…

At any given scale, 3$\times$2-point statistics extract only three numbers from the joint distribution of the cosmic matter density and galaxy density fluctuations: their variances and their covariance. It is well known that the full shape…

Cosmology and Nongalactic Astrophysics · Physics 2026-01-07 Oliver Friedrich , Lina Castiblanco , Anik Halder , Cora Uhlemann

In this Letter we investigate the shape of the probability distribution of column densities (PDF) in molecular clouds. Through the use of low-noise, extinction-calibrated \textit{Herschel}/\textit{Planck} emission data for eight molecular…

Solar and Stellar Astrophysics · Physics 2015-06-23 Marco Lombardi , João Alves , Charles J. Lada

We discuss the behaviour of the redshift- and real-space correlation functions from the ESO Slice Project (ESP) galaxy redshift survey. xi(s) for the whole survey is positive out to ~80/h Mpc, with a smooth break from a shallow power law.…

Astrophysics · Physics 2007-05-23 L. Guzzo , J. G. Bartlett , A. Cappi

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

Weak lensing provides a direct way of mapping the density distribution in the universe. To reconstruct the density field from the shear catalog, an important step is to build the shear field from the shear catalog, which can be quite…

Cosmology and Nongalactic Astrophysics · Physics 2023-07-27 Haoran Wang , Jun Zhang , Hekun Li , Cong Liu

We apply the scale-length method to several three dimensional samples of the Two degree Field Galaxy Redshift Survey. This method allows us to map in a quantitative and powerful way large scale structures in the distribution of galaxies…

Astrophysics · Physics 2009-08-03 Francesco Sylos Labini , Nikolay L. Vasilyev , Yurij V. Baryshev

Measurement of redshift-space distortions (RSD) offers an attractive method to directly probe the cosmic growth history of density perturbations. A distribution function approach where RSD can be written as a sum over density weighted…

Cosmology and Nongalactic Astrophysics · Physics 2015-05-30 Teppei Okumura , Uros Seljak , Patrick McDonald , Vincent Desjacques

We examine the Lagrangian divergence of the displacement field, arguably a more natural object than the density in a Lagrangian description of cosmological large-scale structure. This quantity, which we denote \psi, quantifies the…

Cosmology and Nongalactic Astrophysics · Physics 2012-12-17 Mark C. Neyrinck

We present a detailed analysis of the two-point correlation function, from the 2dF Galaxy Redshift Survey (2dFGRS). We estimate the redshift-space correlation function, xi(s), from which we measure the redshift-space clustering length,…

Many of the cosmological tests to be performed by planned dark energy experiments will require extremely well-characterized photometric redshift measurements. Current estimates are that the true mean redshift of the objects in each photo-z…

Cosmology and Nongalactic Astrophysics · Physics 2010-11-22 Daniel J. Matthews , Jeffrey A. Newman

The Polarizable Continuum Model (PCM) can be used in conjunction with Density Functional Theory (DFT) and its time-dependent extension (TDDFT) to simulate the electronic and optical properties of molecules and nanoparticles immersed in a…

Materials Science · Physics 2015-10-28 Alain Delgado , Stefano Corni , Stefano Pittalis , Carlo Andrea Rozzi

Uncertainty is critical to reliable decision-making with machine learning. Conformal prediction (CP) handles uncertainty by predicting a set on a test input, hoping the set to cover the true label with at least $(1-\alpha)$ confidence. This…

Machine Learning · Computer Science 2024-03-25 Rui Xu , Yue Sun , Chao Chen , Parv Venkitasubramaniam , Sihong Xie