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-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
+search_query=cat:astro-ph.*+AND+lastUpdatedDate:[202407262000+TO+202408012000]&start=0&max_results=5000
+<h1>New astro-ph.* submissions cross listed on cs.LG, cs.AI, stat.*, physics.data-an staritng 202407262000 and ending 202408012000</h1>Feed last updated: 2024-08-01T00:00:00-04:00<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.
@@ -20,18 +20,7 @@ <h1>New astro-ph.* submissions cross listed on stat.*, physics.data-an, cs.AI, c
 parameters for $\Lambda$CDM model. The results make the GPR-based
 reconstruction technique a robust learning process and the inferences on the
 astrophysical parameters obtained therefrom are quite reliable that can be used
-for future analysis.</p></br><a href="http://arxiv.org/pdf/2407.19048v1"><h2>Rapid Likelihood Free Inference of Compact Binary Coalescences using
-  Accelerated Hardware</h2></a>Authors:  Deep Chatterjee, Ethan Marx, William Benoit, Ravi Kumar, Malina Desai, Ekaterina Govorkova, Alec Gunny, Eric Moreno, Rafia Omer, Ryan Raikman, Muhammed Saleem, Shrey Aggarwal, Michael W. Coughlin, Philip Harris, Erik Katsavounidis</br>Comments: Submitted to MLST</br>Primary Category: gr-qc</br>All Categories: gr-qc, astro-ph.IM, cs.LG</br><p>We report a gravitational-wave parameter estimation algorithm, AMPLFI, based
-on likelihood-free inference using normalizing flows. The focus of AMPLFI is to
-perform real-time parameter estimation for candidates detected by
-machine-learning based compact binary coalescence search, Aframe. We present
-details of our algorithm and optimizations done related to data-loading and
-pre-processing on accelerated hardware. We train our model using binary
-black-hole (BBH) simulations on real LIGO-Virgo detector noise. Our model has
-$\sim 6$ 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/2407.20432v1"><h2>Neural Surrogate HMC: Accelerated Hamiltonian Monte Carlo with a Neural
+for future analysis.</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
@@ -41,28 +30,7 @@ <h1>New astro-ph.* submissions cross listed on stat.*, physics.data-an, cs.AI, c
 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
-simulations, we can reliably model the 3D matter overdensity field for the f(R)
-Hu-Sawicki modified gravity (MG) model down to $k_{\rm max}=0.5$ h/Mpc.
-Combining these simulations with the Quijote $\nu$CDM collection, we then
-conduct a Fisher forecast of the marginalized constraints obtained on gravity
-using the WST coefficients and the matter power spectrum at redshift z=0. Our
-results demonstrate that the WST substantially improves upon the 1$\sigma$
-error obtained on the parameter that captures deviations from standard General
-Relativity (GR), yielding a tenfold improvement compared to the corresponding
-matter power spectrum result. At the same time, the WST also enhances the
-precision on the $\Lambda$CDM parameters and the sum of neutrino masses, by
-factors of 1.2-3.4 compared to the matter power spectrum, respectively. Despite
-the overall reduction in the WST performance when we focus on larger scales, it
-still provides a relatively $4.5\times$ tighter 1$\sigma$ error for the MG
-parameter at $k_{\rm max}=0.2$ h/Mpc, highlighting its great sensitivity to the
-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/2407.21008v1"><h2>Bayesian technique to combine independently-trained Machine-Learning
+sampling from the posterior of latent parameters in the Parker equation.</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
@@ -80,23 +48,4 @@ <h1>New astro-ph.* submissions cross listed on stat.*, physics.data-an, cs.AI, c
 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
-dataset. In this paper, we present a hybrid approach, where such physics-based
-summaries are augmented by a set of compressed neural summary statistics that
-are optimised to extract the extra information that is not captured by the
-predefined summaries. The resulting statistics are very powerful inputs to
-simulation-based or implicit inference of model parameters. We apply this
-generalisation of Information Maximising Neural Networks (IMNNs) to parameter
-constraints from tomographic weak gravitational lensing convergence maps to
-find summary statistics that are explicitly optimised to complement angular
-power spectrum estimates. We study several dark matter simulation resolutions
-in low- and high-noise regimes. We show that i) the information-update
-formalism extracts at least $3\times$ and up to $8\times$ as much information
-as the angular power spectrum in all noise regimes, ii) the network summaries
-are highly complementary to existing 2-point summaries, and iii) our formalism
-allows for networks with smaller, physically-informed architectures to match
-much larger regression networks with far fewer simulations needed to obtain
-asymptotically optimal inference.</p></br>
+detection experiments described in this work.</p></br>