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We propose a method for depth estimation under different illumination conditions, i.e., day and night time. As photometry is uninformative in regions under low-illumination, we tackle the problem through a multi-sensor fusion approach,…

Computer Vision and Pattern Recognition · Computer Science 2024-05-28 Vadim Ezhov , Hyoungseob Park , Zhaoyang Zhang , Rishi Upadhyay , Howard Zhang , Chethan Chinder Chandrappa , Achuta Kadambi , Yunhao Ba , Julie Dorsey , Alex Wong

Large-scale structure distorts the images of background galaxies, which allows one to measure directly the projected distribution of dark matter in the universe and determine its power spectrum. Here we address the question of how to…

Astrophysics · Physics 2009-10-30 Uros Seljak

We propose a closed-form spectral framework for relative log-density estimation in linearly parameterized probabilistic models, including unnormalized and conditional models. This is achieved by representing the Kullback-Leibler (KL)…

Machine Learning · Computer Science 2026-05-12 Francis Bach

We present a data-driven technique to analyze multifrequency images from upcoming cosmological surveys mapping large sky area. Using full information from the data at the two-point level, our method can simultaneously constrain the…

Cosmology and Nongalactic Astrophysics · Physics 2023-03-01 Yun-Ting Cheng , Benjamin D. Wandelt , Tzu-Ching Chang , Olivier Dore

We propose a new ray-tracing algorithm to measure the weak lensing shear and convergence fields directly from N-body simulations. We calculate the deflection of the light rays lensed by the 3-D mass density field or gravitational potential…

Cosmology and Nongalactic Astrophysics · Physics 2015-03-17 Baojiu Li , Lindsay J. King , Gong-Bo Zhao , HongSheng Zhao

Machine learning models are increasingly used to predict material properties and accelerate atomistic simulations, but the reliability of their predictions depends on the representativeness of the training data. We present a scalable,…

Chemical Physics · Physics 2025-10-20 Daniel Willimetz , Lukáš Grajciar

We used wide area surveys over 39 deg$^2$ by the HerMES collaboration, performed with the Herschel Observatory SPIRE multi-wavelength camera, to estimate the low-redshift, $0.02<z<0.5$, monochromatic luminosity functions (LFs) of galaxies…

Precision measurements of the galaxy power spectrum P(k) require a data analysis pipeline that is both fast enough to be computationally feasible and accurate enough to take full advantage of high-quality data. We present a rigorous…

Astrophysics · Physics 2009-10-07 Max Tegmark , Andrew Hamilton , Michael Strauss , Michael Vogeley , Alexander Szalay

We have developed a Monte Carlo method to compute the luminosity function of galaxies, based on photometric redshifts, which takes into account the non-gaussianity of the probability functions, and the presence of degenerate solutions in…

Astrophysics · Physics 2009-11-07 Micol Bolzonella , Roser Pello' , Dario Maccagni

A new method is presented to obtain a non-parametric maximum likelihood estimate of the luminosity function and the selection function of a flux limited redshift survey. The method parameterizes the selection function as a series of…

Astrophysics · Physics 2009-10-30 Volker Springel , Simon D. M. White

This paper introduces a novel kernel density estimator (KDE) based on the generalised exponential (GE) distribution, designed specifically for positive continuous data. The proposed GE KDE offers a mathematically tractable form that avoids…

Methodology · Statistics 2026-02-18 Laura M. Craig , Wagner Barreto-Souza

We present a computational approach which is tailored for reducing the complexity of the description of extended systems at the density functional theory level. We define a recipe for generating a set of localized basis functions which are…

Materials Science · Physics 2019-04-16 Laura E. Ratcliff , Luigi Genovese

In this paper, we present a generic methodology for the efficient numerical approximation of the density function of the McKean-Vlasov SDEs. The weak error analysis for the projected process motivates us to combine the iterative Multilevel…

Numerical Analysis · Mathematics 2019-09-27 Denis Belomestny , Lukasz Szpruch , Shuren Tan

We describe a Bayesian approach to estimating luminosity functions. We derive the likelihood function and posterior probability distribution for the luminosity function, given the observed data, and we compare the Bayesian approach with…

Astrophysics · Physics 2009-11-13 Brandon C. Kelly , Xiaohui Fan , Marianne Vestergaard

A kernel density estimator (KDE) is one of the most popular non-parametric density estimators. In this paper we focus on a best bandwidth selection method for use in an analogue of a classical KDE using the tropical symmetric distance,…

Populations and Evolution · Quantitative Biology 2025-12-30 Ruriko Yoshida , Zhiwen Wang

Schlieren deflectometry aims at characterizing the deflections undergone by refracted incident light rays at any surface point of a transparent object. For smooth surfaces, each surface location is actually associated with a sparse…

Computer Vision and Pattern Recognition · Computer Science 2012-12-04 Prasad Sudhakar , Laurent Jacques , Xavier Dubois , Philippe Antoine , Luc Joannes

It is common practice in cosmology to model large-scale structure observables as lognormal random fields, and this approach has been successfully applied in the past to the matter density and weak lensing convergence fields separately. We…

Cosmology and Nongalactic Astrophysics · Physics 2017-07-18 Henrique S. Xavier , Filipe B. Abdalla , Benjamin Joachimi

In many cosmological inference problems, the likelihood (the probability of the observed data as a function of the unknown parameters) is unknown or intractable. This necessitates approximations and assumptions, which can lead to incorrect…

Cosmology and Nongalactic Astrophysics · Physics 2020-12-02 Niall Jeffrey , Justin Alsing , François Lanusse