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-search_query=cat:astro-ph.*+AND+lastUpdatedDate:[202407232000+TO+202407292000]&start=0&max_results=5000
-<h1>New astro-ph.* submissions cross listed on cs.LG, stat.*, physics.data-an, cs.AI staritng 202407232000 and ending 202407292000</h1>Feed last updated: 2024-07-29T00:00:00-04:00<a href="http://arxiv.org/pdf/2407.16917v1"><h2>TelescopeML -- I. An End-to-End Python Package for Interpreting
-  Telescope Datasets through Training Machine Learning Models, Generating
-  Statistical Reports, and Visualizing Results</h2></a>Authors:  Ehsan, Gharib-Nezhad, Natasha E. Batalha, Hamed Valizadegan, Miguel J. S. Martinho, Mahdi Habibi, Gopal Nookula</br>Comments: Please find the accepted paper with complete reference list at
-  https://joss.theoj.org/papers/10.21105/joss.06346</br>Primary Category: astro-ph.IM</br>All Categories: astro-ph.IM, astro-ph.EP, cs.LG</br><p>We are on the verge of a revolutionary era in space exploration, thanks to
-advancements in telescopes such as the James Webb Space Telescope
-(\textit{JWST}). High-resolution, high signal-to-noise spectra from exoplanet
-and brown dwarf atmospheres have been collected over the past few decades,
-requiring the development of accurate and reliable pipelines and tools for
-their analysis. Accurately and swiftly determining the spectroscopic parameters
-from the observational spectra of these objects is crucial for understanding
-their atmospheric composition and guiding future follow-up observations.
-\texttt{TelescopeML} is a Python package developed to perform three main tasks:
-1. Process the synthetic astronomical datasets for training a CNN model and
-prepare the observational dataset for later use for prediction; 2. Train a CNN
-model by implementing the optimal hyperparameters; and 3. Deploy the trained
-CNN models on the actual observational data to derive the output spectroscopic
-parameters.</p></br><a href="http://arxiv.org/pdf/2407.17667v1"><h2>Tackling the Problem of Distributional Shifts: Correcting Misspecified,
+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
@@ -37,7 +21,39 @@ <h1>New astro-ph.* submissions cross listed on cs.LG, stat.*, physics.data-an, c
 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/2407.18647v1"><h2>Towards unveiling the large-scale nature of gravity with the wavelet
+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
+  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.
+In this article, we use the Gaussian Process Regression (GPR) method to learn
+the reionization history and infer the astrophysical parameters. We reconstruct
+the UV luminosity density function using the HFF and early JWST data. From the
+reconstructed history of reionization, the global differential brightness
+temperature fluctuation during this epoch has been computed. We perform MCMC
+analysis of the global 21-cm signal using the instrumental specifications of
+SARAS, in combination with Lyman-$\alpha$ ionization fraction data, Planck
+optical depth measurements and UV luminosity data. Our analysis reveals that
+GPR can help infer the astrophysical parameters in a model-agnostic way than
+conventional methods. Additionally, we analyze the 21-cm power spectrum using
+the reconstructed history of reionization and demonstrate how the future 21-cm
+mission SKA, in combination with Planck and Lyman-$\alpha$ forest data,
+improves the bounds on the reionization astrophysical parameters by doing a
+joint MCMC analysis for the astrophysical parameters plus 6 cosmological
+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/2407.18647v1"><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
@@ -59,7 +75,7 @@ <h1>New astro-ph.* submissions cross listed on cs.LG, stat.*, physics.data-an, c
 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
-  power spectrum</h2></a>Authors:  T. Lucas Makinen, Tom Charnock, Natalia Porqueres, Axel Lapel, Alan Heavens, Benjamin D. Wandelt</br>Comments: 16 pages, 11 figures. Submitted to JCAP. We provide publicly
+  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