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-search_query=cat:astro-ph.*+AND+lastUpdatedDate:[202501072000+TO+202501132000]&start=0&max_results=5000
-<h1>New astro-ph.* submissions cross listed on cs.AI, physics.data-an, cs.LG, stat.* staritng 202501072000 and ending 202501132000</h1>Feed last updated: 2025-01-12T00:00:00-05:00
+search_query=cat:astro-ph.*+AND+lastUpdatedDate:[202501082000+TO+202501142000]&start=0&max_results=5000
+<h1>New astro-ph.* submissions cross listed on cs.AI, cs.LG, stat.*, physics.data-an staritng 202501082000 and ending 202501142000</h1>Feed last updated: 2025-01-13T00:00:00-05:00<a href="http://arxiv.org/pdf/2501.06293v1"><h2>LensNet: Enhancing Real-time Microlensing Event Discovery with Recurrent
+  Neural Networks in the Korea Microlensing Telescope Network</h2></a>Authors:  Javier Viaña, Kyu-Ha Hwang, Zoë de Beurs, Jennifer C. Yee, Andrew Vanderburg, Michael D. Albrow, Sun-Ju Chung, Andrew Gould, Cheongho Han, Youn Kil Jung, Yoon-Hyun Ryu, In-Gu Shin, Yossi Shvartzvald, Hongjing Yang, Weicheng Zang, Sang-Mok Cha, Dong-Jin Kim, Seung-Lee Kim, Chung-Uk Lee, Dong-Joo Lee, Yongseok Lee, Byeong-Gon Park, Richard W. Pogge</br>Comments: 23 pages, 13 figures, Accepted for publication in the The
+  Astronomical Journal</br>Primary Category: astro-ph.IM</br>All Categories: astro-ph.IM, astro-ph.EP, astro-ph.GA, cs.AI, 85-08, J.2</br><p>Traditional microlensing event vetting methods require highly trained human
+experts, and the process is both complex and time-consuming. This reliance on
+manual inspection often leads to inefficiencies and constrains the ability to
+scale for widespread exoplanet detection, ultimately hindering discovery rates.
+To address the limits of traditional microlensing event vetting, we have
+developed LensNet, a machine learning pipeline specifically designed to
+distinguish legitimate microlensing events from false positives caused by
+instrumental artifacts, such as pixel bleed trails and diffraction spikes. Our
+system operates in conjunction with a preliminary algorithm that detects
+increasing trends in flux. These flagged instances are then passed to LensNet
+for further classification, allowing for timely alerts and follow-up
+observations. Tailored for the multi-observatory setup of the Korea
+Microlensing Telescope Network (KMTNet) and trained on a rich dataset of
+manually classified events, LensNet is optimized for early detection and
+warning of microlensing occurrences, enabling astronomers to organize follow-up
+observations promptly. The internal model of the pipeline employs a
+multi-branch Recurrent Neural Network (RNN) architecture that evaluates
+time-series flux data with contextual information, including sky background,
+the full width at half maximum of the target star, flux errors, PSF quality
+flags, and air mass for each observation. We demonstrate a classification
+accuracy above 87.5%, and anticipate further improvements as we expand our
+training set and continue to refine the algorithm.</p></br><a href="http://arxiv.org/pdf/2501.06532v1"><h2>Determination of galaxy photometric redshifts using Conditional
+  Generative Adversarial Networks (CGANs)</h2></a>Authors:  M. Garcia-Fernandez</br>Comments: No comment found</br>Primary Category: astro-ph.IM</br>All Categories: astro-ph.IM, astro-ph.CO, cs.AI</br><p>Accurate and reliable photometric redshifts determination is one of the key
+aspects for wide-field photometric surveys. Determination of photometric
+redshift for galaxies, has been traditionally solved by use of machine-learning
+and artificial intelligence techniques trained on a calibration sample of
+galaxies, where both photometry and spectrometry are determined. On this paper,
+we present a new algorithmic approach for determining photometric redshifts of
+galaxies using Conditional Generative Adversarial Networks (CGANs). Proposed
+CGAN implementation, approaches photometric redshift determination as a
+probabilistic regression, where instead of determining a single value for the
+estimated redshift of the galaxy, a full probability density is computed. The
+methodology proposed, is tested with data from Dark Energy Survey (DES) Y1 data
+and compared with other existing algorithm such as a Random Forest regressor.</p></br>