UNCOVER: JWST Spectroscopy of Three Cold Brown Dwarfs at Kiloparsec-scale Distances by Adam J. Burgasser, Rachel Bezanson, Ivo Labbe, Gabriel Brammer, Sam E. Cutler, Lukas J. Furtak, Jenny E. Greene, Roman Gerasimov, Joel Leja, Richard Pan, Sedona H. Price, Bingjie Wang, John R. Weaver, Katherine E. Whitaker, Seiji Fujimoto, Vasily Kokorev, Pratika Dayal, Themiya Nanayakkara, Christina C. Williams, Danilo Marchesini, Adi Zitrin, Pieter van Dokkum

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The JWST Hubble Sequence: The Rest-frame Optical Evolution of Galaxy Structure at 1.5 < z < 6.5 by Leonardo Ferreira, Christopher J. Conselice, Elizaveta Sazonova, Fabricio Ferrari, Joseph Caruana, Clár-Bríd Tohill, Geferson Lucatelli, Nathan Adams, Dimitrios Irodotou, Madeline A. Marshall, Will J. Roper, Christopher C. Lovell, Aprajita Verma, Duncan Austin, James Trussler, Stephen M. Wilkins

“…Additionally, we make our visual classifications public for the community.…”
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USmorph: An Updated Framework of Automatic Classification of Galaxy Morphologies and Its Application to Galaxies in the COSMOS Field by Jie Song, GuanWen Fang, Shuo Ba, Zesen Lin, Yizhou Gu, Chichun Zhou, Tao Wang, Cai-Na Hao, Guilin Liu, Hongxin Zhang, Yao Yao, Xu Kong

“…We find morphology to be strongly correlated with redshift and stellar mass. The consistency of these classification results with expected correlations among multiple parameters underscores the validity and reliability of our classification method, rendering it a valuable tool for future studies.…”
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Automated Classification of Massive Spectra Based on Enhanced Multi-Scale Coded Convolutional Neural Network by Bin Jiang, Donglai Wei, Jiazhen Liu, Shuting Wang, Liyun Cheng, Zihao Wang, Meixia Qu

“…Feature extraction plays an important role in the process of automatic spectra classification. The proper classification network can extract most of the common spectral features with minimum noise and individual features. …”
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Automated distant galaxy merger classifications from Space Telescope images using the Illustris simulation by Snyder, Gregory F, Rodriguez-Gomez, Vicente, Lotz, Jennifer M, Torrey, Paul, Quirk, Amanda CN, Hernquist, Lars, Vogelsberger, Mark, Freeman, Peter E

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Automated distant galaxy merger classifications from Space Telescope images using the Illustris simulation by Snyder, Gregory F, Rodriguez-Gomez, Vicente, Lotz, Jennifer M, Torrey, Paul A., Quirk, Amanda CN, Hernquist, Lars, Vogelsberger, Mark, Freeman, Peter E

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MEASNet. I. A Model for Barium Star Identification and s-process Abundance Estimation from LAMOST DR10 Low-resolution Survey by Shengjun Song, Xiaoming Kong, Yude Bu, Zhenping Yi, Meng Liu

“…After training, we apply the model to 4,083,003 stellar spectra from LAMOST DR10 LRS, successfully identifying 1,803,670 spectra of barium candidates ([Ba/Fe] ≥ 0.25 dex) along with their five s -process elemental abundances. …”
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An XMM-Newton View of the Symbiotic Stars HM Sge, NQ Gem, and PU Vul by Jesús A. Toalá, Marissa K. Botello, Laurence Sabin

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Photometric Completeness Modelled with Neural Networks by William E. Harris, Joshua S. Speagle

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The Presolar Grain Database. I. Silicon Carbide by Thomas Stephan, Reto Trappitsch, Peter Hoppe, Andrew M. Davis, Maitrayee Bose, Asmaa Boujibar, Frank Gyngard, K. Mairin Hynes, Nan Liu, Larry R. Nittler, Ryan C. Ogliore

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TESS Photometry of AM Her and AR UMa: Binary Parameters, Cyclotron Emission Modeling, and Mass Transfer Duty Cycles by Paul A. Mason, Pasi Hakala, Kinwah Wu, Paul E. Barrett, Krystian Iłkiewicz, Colin Littlefield, Lorena C. Monroy, Hasan C. Sezer, Francisco Jablonski, Peter Garnavich, Paula Szkody, Gavin Ramsay, Christopher Duffy, Simone Scaringi

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SDSS-IV MaNGA: Refining strong line diagnostic classifications using spatially resolved gas dynamics by Law, DR, Ji, X, Belfiore, F, Bershady, MA, Cappellari, M, Westfall, KB, Yan, R, Bizyaev, D, Brownstein, JR, Drory, N, Andrews, BH

“…Star-forming, AGN, and LI(N)ER regions are additionally well separated from each other in terms of their stellar velocity dispersion, stellar population age, H<em>&alpha;</em>&nbsp;equivalent width, and typical radius within a given galaxy. …”

Identifying Exoplanets with Deep Learning. V. Improved Light-curve Classification for TESS Full-frame Image Observations by Evan Tey, Dan Moldovan, Michelle Kunimoto, Chelsea X. Huang, Avi Shporer, Tansu Daylan, Daniel Muthukrishna, Andrew Vanderburg, Anne Dattilo, George R. Ricker, S. Seager

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DECam Multimessenger Astrophysics Pipeline. I. From Raw Data to Single-exposure Candidates by Shenming Fu, Thomas Matheson, Aaron Meisner, Yuanyuan Zhang, Sebastián Vicencio, Destry Saul

“…Those candidates will be sent to brokers for further classification and alert distribution. We validate our pipeline using archival exposures that cover various types of objects, and the tested targets include a kilonova (GW170817), supernovae, stellar flares, variable stars (in a resolved galaxy or the Milky Way Bulge), and serendipitous objects. …”
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Population of X-Ray Sources in the Intermediate-age Cluster NGC 3532: a Test Bed for Machine-learning Classification by Steven Chen, Oleg Kargaltsev, Hui Yang, Jeremy Hare, Igor Volkov, Blagoy Rangelov, John Tomsick

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The Classification of Galaxy Morphology in the H Band of the COSMOS-DASH Field: A Combination-based Machine-learning Clustering Model by Yao Dai, Jun Xu, Jie Song, Guanwen Fang, Chichun Zhou, Shuo Ba, Yizhou Gu, Zesen Lin, Xu Kong

“…By applying our previously developed two-step scheme for galaxy morphology classification, we present a catalog of galaxy morphology for H -band-selected massive galaxies in the COSMOS-DASH field, which includes 17,292 galaxies with stellar mass M _⋆ > 10 ^10 M _⊙ at 0.5 < z < 2.5. …”
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A Novel Approach to Classify Telescopic Sensors Data Using Bidirectional-Gated Recurrent Neural Networks by Ali Raza, Kashif Munir, Mubarak Almutairi, Faizan Younas, Mian Muhammad Sadiq Fareed, Gulnaz Ahmed

“…The physical processes in stars and oscillations are similar to the Sun, which is more evolved to the red-giant branch (RGB), representing the Sun’s future. In stellar astrophysics, the RGB is a crucial problem to determine. …”
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Morphology of Galaxies in JWST Fields: Initial Distribution and Evolution of Galaxy Morphology by Jeong Hwan Lee, Changbom Park, Ho Seong Hwang, Minseong Kwon

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Detecting and Classifying Flares in High-resolution Solar Spectra with Supervised Machine Learning by Nicole Hao, Laura Flagg, Ray Jayawardhana

“…Detecting and classifying solar flares can inform the analysis of contamination caused by stellar flares in exoplanet transmission spectra. …”
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Disk Dissipation, Giant Planet Formation, and Star Formation Rate Fluctuations in the 3 Myr History of Gould’s Belt by Mingchao Liu, Jinhua He, Jixing Ge, Tie Liu, Yuping Tang, Xuzhi Li

“…By uniformly constraining the slope α of infrared spectral energy distributions (SEDs) of young stellar objects (YSOs) in the 13 largest Gould’s Belt (GB) protoclusters surveyed by the Spitzer Space Telescope, we have constructed a cluster-averaged histogram of α representing the YSO evolution lifetime as a function of the α value. …”
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