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-search_query=cat:astro-ph.*+AND+lastUpdatedDate:[202407242000+TO+202407302000]&start=0&max_results=5000
-<h1>New astro-ph.* submissions cross listed on stat.*, physics.data-an, cs.LG, cs.AI staritng 202407242000 and ending 202407302000</h1>Feed last updated: 2024-07-30T00:00:00-04:00<a href="http://arxiv.org/pdf/2407.17667v1"><h2>Tackling the Problem of Distributional Shifts: Correcting Misspecified,
-  High-Dimensional Data-Driven Priors for Inverse Problems</h2></a>Authors:  Gabriel Missael Barco, Alexandre Adam, Connor Stone, Yashar Hezaveh, Laurence Perreault-Levasseur</br>Comments: 17 pages, 15 figures, Submitted to The Astrophysical Journal</br>Primary Category: astro-ph.IM</br>All Categories: astro-ph.IM, astro-ph.CO, cs.LG</br><p>Bayesian inference for inverse problems hinges critically on the choice of
-priors. In the absence of specific prior information, population-level
-distributions can serve as effective priors for parameters of interest. With
-the advent of machine learning, the use of data-driven population-level
-distributions (encoded, e.g., in a trained deep neural network) as priors is
-emerging as an appealing alternative to simple parametric priors in a variety
-of inverse problems. However, in many astrophysical applications, it is often
-difficult or even impossible to acquire independent and identically distributed
-samples from the underlying data-generating process of interest to train these
-models. In these cases, corrupted data or a surrogate, e.g. a simulator, is
-often used to produce training samples, meaning that there is a risk of
-obtaining misspecified priors. This, in turn, can bias the inferred posteriors
-in ways that are difficult to quantify, which limits the potential
-applicability of these models in real-world scenarios. In this work, we propose
-addressing this issue by iteratively updating the population-level
-distributions by retraining the model with posterior samples from different
-sets of observations and showcase the potential of this method on the problem
-of background image reconstruction in strong gravitational lensing when
-score-based models are used as data-driven priors. We show that starting from a
-misspecified prior distribution, the updated distribution becomes progressively
-closer to the underlying population-level distribution, and the resulting
-posterior samples exhibit reduced bias after several updates.</p></br><a href="http://arxiv.org/pdf/2407.19481v1"><h2>What can we learn about Reionization astrophysical parameters using
+search_query=cat:astro-ph.*+AND+lastUpdatedDate:[202407252000+TO+202407312000]&start=0&max_results=5000
+<h1>New astro-ph.* submissions cross listed on stat.*, physics.data-an, cs.AI, cs.LG staritng 202407252000 and ending 202407312000</h1>Feed last updated: 2024-07-31T00:00:00-04:00<a href="http://arxiv.org/pdf/2407.19481v1"><h2>What can we learn about Reionization astrophysical parameters using
   Gaussian Process Regression?</h2></a>Authors:  Purba Mukherjee, Antara Dey, Supratik Pal</br>Comments: No comment found</br>Primary Category: astro-ph.CO</br>All Categories: astro-ph.CO, astro-ph.IM, cs.LG</br><p>Reionization is one of the least understood processes in the evolution
 history of the Universe, mostly because of the numerous astrophysical processes
 occurring simultaneously about which we do not have a very clear idea so far.
@@ -53,7 +31,17 @@ <h1>New astro-ph.* submissions cross listed on stat.*, physics.data-an, cs.LG, c
 $\sim 6$ million trainable parameters with training times $\lesssim 24$ hours.
 Based on online deployment on a mock data stream of LIGO-Virgo data, Aframe +
 AMPLFI is able to pick up BBH candidates and infer parameters for real-time
-alerts from data acquisition with a net latency of $\sim 6$s.</p></br><a href="http://arxiv.org/pdf/2407.18647v1"><h2>Towards unveiling the large-scale nature of gravity with the wavelet
+alerts from data acquisition with a net latency of $\sim 6$s.</p></br><a href="http://arxiv.org/pdf/2407.20432v1"><h2>Neural Surrogate HMC: Accelerated Hamiltonian Monte Carlo with a Neural
+  Network Surrogate Likelihood</h2></a>Authors:  Linnea M Wolniewicz, Peter Sadowski, Claudio Corti</br>Comments: 5 pages, 3 figures, accepted at SPAICE Conference 2024</br>Primary Category: cs.LG</br>All Categories: cs.LG, astro-ph.HE, I.2.1</br><p>Bayesian Inference with Markov Chain Monte Carlo requires efficient
+computation of the likelihood function. In some scientific applications, the
+likelihood must be computed by numerically solving a partial differential
+equation, which can be prohibitively expensive. We demonstrate that some such
+problems can be made tractable by amortizing the computation with a surrogate
+likelihood function implemented by a neural network. We show that this has two
+additional benefits: reducing noise in the likelihood evaluations and providing
+fast gradient calculations. In experiments, the approach is applied to a model
+of heliospheric transport of galactic cosmic rays, where it enables efficient
+sampling from the posterior of latent parameters in the Parker equation.</p></br><a href="http://arxiv.org/pdf/2407.18647v1"><h2>Towards unveiling the large-scale nature of gravity with the wavelet
   scattering transform</h2></a>Authors:  Georgios Valogiannis, Francisco Villaescusa-Navarro, Marco Baldi</br>Comments: 19 pages, 15 figures, 1 table</br>Primary Category: astro-ph.CO</br>All Categories: astro-ph.CO, gr-qc, hep-ph, physics.data-an</br><p>We present the first application of the Wavelet Scattering Transform (WST) in
 order to constrain the nature of gravity using the three-dimensional (3D)
 large-scale structure of the universe. Utilizing the Quijote-MG N-body
@@ -74,7 +62,25 @@ <h1>New astro-ph.* submissions cross listed on stat.*, physics.data-an, cs.LG, c
 underlying gravity theory. This first proof-of-concept study reaffirms the
 constraining properties of the WST technique and paves the way for exciting
 future applications in order to perform precise large-scale tests of gravity
-with the new generation of cutting-edge cosmological data.</p></br><a href="http://arxiv.org/pdf/2407.18909v1"><h2>Hybrid summary statistics: neural weak lensing inference beyond the
+with the new generation of cutting-edge cosmological data.</p></br><a href="http://arxiv.org/pdf/2407.21008v1"><h2>Bayesian technique to combine independently-trained Machine-Learning
+  models applied to direct dark matter detection</h2></a>Authors:  David Cerdeno, Martin de los Rios, Andres D. Perez</br>Comments: 25 pages, 7 figures, 2 tables</br>Primary Category: hep-ph</br>All Categories: hep-ph, astro-ph.CO, physics.data-an</br><p>We carry out a Bayesian analysis of dark matter (DM) direct detection data to
+determine particle model parameters using the Truncated Marginal Neural Ratio
+Estimation (TMNRE) machine learning technique. TMNRE avoids an explicit
+calculation of the likelihood, which instead is estimated from simulated data,
+unlike in traditional Markov Chain Monte Carlo (MCMC) algorithms. This
+considerably speeds up, by several orders of magnitude, the computation of the
+posterior distributions, which allows to perform the Bayesian analysis of an
+otherwise computationally prohibitive number of benchmark points. In this
+article we demonstrate that, in the TMNRE framework, it is possible to include,
+combine, and remove different datasets in a modular fashion, which is fast and
+simple as there is no need to re-train the machine learning algorithm or to
+define a combined likelihood. In order to assess the performance of this
+method, we consider the case of WIMP DM with spin-dependent and independent
+interactions with protons and neutrons in a xenon experiment. After validating
+our results with MCMC, we employ the TMNRE procedure to determine the regions
+where the DM parameters can be reconstructed. Finally, we present CADDENA, a
+Python package that implements the modular Bayesian analysis of direct
+detection experiments described in this work.</p></br><a href="http://arxiv.org/pdf/2407.18909v1"><h2>Hybrid summary statistics: neural weak lensing inference beyond the
   power spectrum</h2></a>Authors:  T. Lucas Makinen, Alan Heavens, Natalia Porqueres, Tom Charnock, Axel Lapel, Benjamin D. Wandelt</br>Comments: 16 pages, 11 figures. Submitted to JCAP. We provide publicly
   available code at https://github.com/tlmakinen/hybridStatsWL</br>Primary Category: astro-ph.CO</br>All Categories: astro-ph.CO, cs.LG, physics.comp-ph, stat.ML, stat.OT</br><p>In inference problems, we often have domain knowledge which allows us to
 define summary statistics that capture most of the information content in a