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We introduce a new adaptive and fully Bayesian grid-based method to model strong gravitational lenses with extended images. The primary goal of this method is to quantify the level of luminous and dark-mass substructure in massive galaxies,…

Astrophysics · Physics 2009-11-13 S. Vegetti , L. V. E. Koopmans

Bayesian model averaging is a procedure to obtain parameter constraints that account for the uncertainty about the correct cosmological model. We use recent cosmological observations and Bayesian model averaging to derive tight limits on…

Cosmology and Nongalactic Astrophysics · Physics 2012-06-07 M. Vardanyan , R. Trotta , J. Silk

Interpreting the observations of exoplanet atmospheres to constrain physical and chemical properties is typically done using Bayesian retrieval techniques. Because these methods require many model computations, a compromise is made between…

Earth and Planetary Astrophysics · Physics 2024-01-10 Francisco Ardévol Martínez , Michiel Min , Daniela Huppenkothen , Inga Kamp , Paul I. Palmer

We introduce a Bayesian approach to predictive density calibration and combination that accounts for parameter uncertainty and model set incompleteness through the use of random calibration functionals and random combination weights.…

Applications · Statistics 2016-10-26 Federico Bassetti , Roberto Casarin , Francesco Ravazzolo

We present a novel population-based Bayesian inference approach to model the average and population variance of spatial distribution of a set of observables from ensemble analysis of low signal-to-noise ratio measurements. The method…

Cosmology and Nongalactic Astrophysics · Physics 2020-12-23 Arya Farahi , Daisuke Nagai , Yang Chen

Planet induced sub-structures, like annular gaps, observed in dust emission from protoplanetary disks provide a unique probe to characterize unseen young planets. While deep learning based model has an edge in characterizing the planet's…

Earth and Planetary Astrophysics · Physics 2022-09-14 Sayantan Auddy , Ramit Dey , Min-Kai Lin , Daniel Carrera , Jacob B. Simon

We exploit the great potential offered by Bayesian Neural Networks (BNNs) to directly decipher the internal composition of neutron stars (NSs) based on their macroscopic properties. By analyzing a set of simulated observations, namely NS…

Nuclear Theory · Physics 2023-09-15 Valéria Carvalho , Márcio Ferreira , Tuhin Malik , Constança Providência

Despite major methodological developments, Bayesian inference for Gaussian graphical models remains challenging in high dimension due to the tremendous size of the model space. This article proposes a method to infer the marginal and…

Methodology · Statistics 2018-04-10 Gwenaël G. R. Leday , Sylvia Richardson

The recent JWST detections of carbon-bearing molecules in a habitable-zone sub-Neptune have opened a new era in the study of low-mass exoplanets. The sub-Neptune regime spans a wide diversity of planetary interiors and atmospheres not…

Modeling the interior of a planet is difficult because the small number of measured parameters is insufficient to constrain the many variables involved in describing the interior structure and composition. One solution is to invoke…

Earth and Planetary Astrophysics · Physics 2022-04-27 Joshua Podolak , Uri Malamud , Morris Podolak

The characterization of exoplanets relies on that of their host star. However, stellar evolution models cannot always be used to derive the mass and radius of individual stars, because many stellar internal parameters are poorly…

Earth and Planetary Astrophysics · Physics 2018-12-14 Aurélien Crida , Roxanne Ligi , Caroline Dorn , Yveline Lebreton

The determination of the abundance of volatiles in extrasolar planets is very important as it can provide constraints on transport in protoplanetary disks and on the formation location of planets. However, constraining the internal…

Earth and Planetary Astrophysics · Physics 2016-06-22 Yann Alibert

With the upcoming launch of space telescopes dedicated to the study of exoplanets, the \textit{Atmospheric Remote-Sensing Infrared Exoplanet Large-survey} (ARIEL) and the \textit{James Webb Space Telescope} (JWST), a new era is opening in…

We introduce a Bayesian solution to the problem of inferring the density profile of strong gravitational lenses when the lens galaxy may contain multiple dark or faint substructures. The source and lens models are based on a superposition…

Instrumentation and Methods for Astrophysics · Physics 2015-10-12 Brendon J. Brewer , David Huijser , Geraint F. Lewis

Many exoplanets have been discovered with radii of 1-4 Earth radii, between that of Earth and Neptune. A number of these are known to have densities consistent with solid compositions, while others are "sub-Neptunes" likely to have…

Earth and Planetary Astrophysics · Physics 2015-06-18 Alex R. Howe , Adam S. Burrows , Wesley Verne

Aims: The secondary atmospheres of terrestrial planets form and evolve as a consequence of interaction with the interior over geological time. We aim to quantify the influence of planetary bulk composition on the interior--atmosphere…

Earth and Planetary Astrophysics · Physics 2020-11-04 Rob J. Spaargaren , Maxim D. Ballmer , Dan J. Bower , Caroline Dorn , Paul J. Tackley

A probabilistic technique for the joint estimation of background and sources with the aim of detecting faint and extended celestial objects is described. Bayesian probability theory is applied to gain insight into the coexistence of…

Instrumentation and Methods for Astrophysics · Physics 2015-05-13 F. Guglielmetti , R. Fischer , V. Dose

Inverse problems constrained by partial differential equations are often ill-conditioned due to noisy and incomplete data or inherent non-uniqueness. A prominent example is full waveform inversion, which estimates Earth's subsurface…

Geophysics · Physics 2026-03-03 Ali Siahkoohi , Kamal Aghazade , Ali Gholami

Recent JWST observations of the sub-Neptune GJ 1214 b suggest that it hosts a high-metallicity (>100x solar), hazy atmosphere. Emission spectra of the planet show molecular absorption features, most likely due to atmospheric H2O. In light…

The standard approach to Bayesian inference is based on the assumption that the distribution of the data belongs to the chosen model class. However, even a small violation of this assumption can have a large impact on the outcome of a…

Methodology · Statistics 2015-06-22 Jeffrey W. Miller , David B. Dunson
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