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-search_query=cat:astro-ph.*+AND+lastUpdatedDate:[202411192000+TO+202411252000]&start=0&max_results=5000
-<h1>New astro-ph.* submissions cross listed on cs.AI, stat.*, cs.LG, physics.data-an staritng 202411192000 and ending 202411252000</h1>Feed last updated: 2024-11-24T00:00:00-05:00<a href="http://arxiv.org/pdf/2411.14748v1"><h2>Cosmological Analysis with Calibrated Neural Quantile Estimation and
+search_query=cat:astro-ph.*+AND+lastUpdatedDate:[202411202000+TO+202411262000]&start=0&max_results=5000
+<h1>New astro-ph.* submissions cross listed on stat.*, cs.AI, cs.LG, physics.data-an staritng 202411202000 and ending 202411262000</h1>Feed last updated: 2024-11-25T00:00:00-05:00<a href="http://arxiv.org/pdf/2411.16339v1"><h2>Solaris: A Foundation Model of the Sun</h2></a>Authors:  Harris Abdul Majid, Pietro Sittoni, Francesco Tudisco</br>Comments: No comment found</br>Primary Category: astro-ph.SR</br>All Categories: astro-ph.SR, astro-ph.IM, cs.LG, physics.space-ph</br><p>Foundation models have demonstrated remarkable success across various
+scientific domains, motivating our exploration of their potential in solar
+physics. In this paper, we present Solaris, the first foundation model for
+forecasting the Sun's atmosphere. We leverage 13 years of full-disk,
+multi-wavelength solar imagery from the Solar Dynamics Observatory, spanning a
+complete solar cycle, to pre-train Solaris for 12-hour interval forecasting.
+Solaris is built on a large-scale 3D Swin Transformer architecture with 109
+million parameters. We demonstrate Solaris' ability to generalize by
+fine-tuning on a low-data regime using a single wavelength (1700 {\AA}), that
+was not included in pre-training, outperforming models trained from scratch on
+this specific wavelength. Our results indicate that Solaris can effectively
+capture the complex dynamics of the solar atmosphere and transform solar
+forecasting.</p></br><a href="http://arxiv.org/pdf/2411.14748v1"><h2>Cosmological Analysis with Calibrated Neural Quantile Estimation and
   Approximate Simulators</h2></a>Authors:  He Jia</br>Comments: 5+4 pages, 5+3 figures, to be submitted, comments are welcome</br>Primary Category: astro-ph.CO</br>All Categories: astro-ph.CO, astro-ph.IM, cs.LG</br><p>A major challenge in extracting information from current and upcoming surveys
 of cosmological Large-Scale Structure (LSS) is the limited availability of
 computationally expensive high-fidelity simulations. We introduce Neural
@@ -32,18 +44,24 @@ <h1>New astro-ph.* submissions cross listed on cs.AI, stat.*, cs.LG, physics.dat
 response analysis of BabyIAXO's sensitivity to the axion-photon coupling.
 Though focusing on the Primakoff solar flux component, our virtual helioscope
 model can be used to test different production mechanisms, allowing for direct
-comparisons within a unified framework.</p></br><a href="http://arxiv.org/pdf/2411.13402v1"><h2>Extraction of gravitational wave signals in realistic LISA data</h2></a>Authors:  Eleonora Castelli, Quentin Baghi, John G. Baker, Jacob Slutsky, Jérôme Bobin, Nikolaos Karnesis, Antoine Petiteau, Orion Sauter, Peter Wass, William J. Weber</br>Comments: 28 pages, 14 figures</br>Primary Category: gr-qc</br>All Categories: gr-qc, astro-ph.IM, physics.data-an</br><p>The Laser Interferometer Space Antenna (LISA) mission is being developed by
-ESA with NASA participation. As it has recently passed the Mission Adoption
-milestone, models of the instruments and noise performance are becoming more
-detailed, and likewise prototype data analyses must as well. Assumptions such
-as Gaussianity, Stationarity, and continuous data continuity are unrealistic,
-and must be replaced with physically motivated data simulations, and data
-analysis methods adapted to accommodate such likely imperfections. To this end,
-the LISA Data Challenges have produced datasets featuring time-varying and
-unequal constellation armlength, and measurement artifacts including data
-interruptions and instrumental transients. In this work, we assess the impact
-of these data artifacts on the inference of Galactic Binary and Massive Black
-Hole properties. Our analysis shows that the treatment of noise transients and
-gaps is necessary for effective parameter estimation. We find that
-straightforward mitigation techniques can significantly suppress artifacts,
-albeit leaving a non-negligible impact on aspects of the science.</p></br>
+comparisons within a unified framework.</p></br><a href="http://arxiv.org/pdf/2411.16556v1"><h2>Anomaly Detection and RFI Classification with Unsupervised Learning in
+  Narrowband Radio Technosignature Searches</h2></a>Authors:  Ben Jacobson-Bell, Steve Croft, Carmen Choza, Alex Andersson, Daniel Bautista, Vishal Gajjar, Matthew Lebofsky, David H. E. MacMahon, Caleb Painter, Andrew P. V. Siemion</br>Comments: 20 pages, 14 figures, submitted to AJ</br>Primary Category: astro-ph.IM</br>All Categories: astro-ph.IM, cs.LG</br><p>The search for radio technosignatures is an anomaly detection problem:
+candidate signals represent needles of interest in the proverbial haystack of
+radio-frequency interference (RFI). Current search frameworks find an enormity
+of false-positive signals, especially in large surveys, requiring manual
+follow-up to a sometimes prohibitive degree. Unsupervised learning provides an
+algorithmic way to winnow the most anomalous signals from the chaff, as well as
+group together RFI signals that bear morphological similarities. We present
+GLOBULAR (Grouping Low-frequency Observations By Unsupervised Learning After
+Reduction) clustering, a signal processing method that uses HDBSCAN to reduce
+the false-positive rate and isolate outlier signals for further analysis. When
+combined with a standard narrowband signal detection and spatial filtering
+pipeline, such as turboSETI, GLOBULAR clustering offers significant
+improvements in the false-positive rate over the standard pipeline alone,
+suggesting dramatic potential for the amelioration of manual follow-up
+requirements for future large surveys. By removing RFI signals in regions of
+high spectral occupancy, GLOBULAR clustering may also enable the detection of
+signals missed by the standard pipeline. We benchmark our method against the
+Choza et al. (2024) turboSETI-only search of 97 nearby galaxies at L-band,
+demonstrating a false-positive hit reduction rate of 93.1% and a false-positive
+event reduction rate of 99.3%.</p></br>