search_query=cat:astro-ph.*+AND+lastUpdatedDate:[202512052000+TO+202512112000]&start=0&max_results=5000
<h1>New astro-ph.* submissions cross listed on cs.LG, physics.data-an, cs.AI, stat.* staritng 202512052000 and ending 202512112000</h1>Feed last updated: 2025-12-11T04:26:50Z<a href="https://arxiv.org/pdf/2512.09021v1"><h2>Interpretable machine learning of halo gas density profiles: a sensitivity analysis of cosmological hydrodynamical simulations</h2></a>Authors:  Daniele Sorini, Sownak Bose, Mathilda Denison, Romeel Davé</br>Comments: To be submitted to The Open Journal of Astrophysics</br>Primary Category: astro-ph.GA</br>All Categories: astro-ph.GA, astro-ph.CO, astro-ph.IM, cs.LG</br><p>Stellar and AGN-driven feedback processes affect the distribution of gas on a wide range of scales, from within galaxies well into the intergalactic medium. Yet, it remains unclear how feedback, through its connection to key galaxy properties, shapes the radial gas density profile in the host halo. We tackle this question using suites of the EAGLE, IllustrisTNG, and Simba cosmological hydrodynamical simulations, which span a variety of feedback models. We develop a random forest algorithm that predicts the radial gas density profile within haloes from the total halo mass and five global properties of the central galaxy: gas and stellar mass; star formation rate; mass and accretion rate of the central black hole (BH). The algorithm reproduces the simulated gas density profiles with an average accuracy of $\sim$80-90% over the halo mass range $10^{9.5} \, \mathrm{M}_{\odot} < M_{\rm 200c} < 10^{15} \, \mathrm{M}_{\odot}$ and redshift interval $0<z<4$. For the first time, we apply Sobol statistical sensitivity analysis to full cosmological hydrodynamical simulations, quantifying how each feature affects the gas density as a function of distance from the halo centre. Across all simulations and redshifts, the total halo mass and the gas mass of the central galaxy are the most strongly tied to the halo gas distribution, while stellar and BH properties are generally less informative. The exact relative importance of the different features depends on the feedback scenario and redshift. Our framework can be readily embedded in semi-analytic models of galaxy formation to incorporate halo gas density profiles consistent with different hydrodynamical simulations. Our work also provides a proof of concept for constraining feedback models with future observations of galaxy properties and of the surrounding gas distribution.</p></br><a href="https://arxiv.org/pdf/2512.08305v1"><h2>Magnetic activity of ultracool dwarfs in the LAMOST DR11</h2></a>Authors:  Yue Xiang, Shenghong Gu, Dongtao Cao</br>Comments: 13 pages, 10 figures, accepted for publication in ApJ</br>Primary Category: astro-ph.SR</br>All Categories: astro-ph.SR, astro-ph.IM, cs.LG</br><p>Ultracool dwarfs consist of lowest-mass stars and brown dwarfs. Their interior is fully convective, different from that of the partly-convective Sun-like stars. Magnetic field generation process beneath the surface of ultracool dwarfs is still poorly understood and controversial. To increase samples of active ultracool dwarfs significantly, we have identified 962 ultracool dwarfs in the latest LAMOST data release, DR11. We also simulate the Chinese Space Station Survey Telescope (CSST) low-resolution slitless spectra by degrading the LAMOST spectra. A semi-supervised machine learning approach with an autoencoder model is built to identify ultracool dwarfs with the simulated CSST spectra, which demonstrates the capability of the CSST all-sky slitless spectroscopic survey on the detection of ultracool dwarfs. Magnetic activity of the ultracool dwarfs is investigated by using the H$α$ line emission as a proxy. The rotational periods of 82 ultracool dwarfs are derived based on the Kepler/K2 light curves. We also derive the activity-rotation relation of the ultracool dwarfs, which is saturated around a Rossby number of 0.12.</p></br><a href="https://arxiv.org/pdf/2512.09274v1"><h2>The ONs and OFFs of Pulsar Radio Emission: Characterizing the Nulling Phenomenon</h2></a>Authors:  Garvit Grover, N. D. Ramesh Bhat, Samuel J. McSweeney, Christopher P. Lee, Chia Min Tan, Shih Ching Fu, Bradley W. Meyers</br>Comments: Accepted for publication in the Astrophysical Journal, 25 pages, 9 Figures, 4 Tables</br>Primary Category: astro-ph.HE</br>All Categories: astro-ph.HE, physics.data-an</br><p>Radio emission from pulsars is known to exhibit a diverse range of emission phenomena, among which nulling, where the emission becomes temporarily undetectable, is an intriguing one. Observations suggest nulling is prevalent in many long-period pulsars and must be understood to obtain a more comprehensive picture of pulsar emission and its evolution. One of the limitations in observational characterisation of nulling is the limited signal-to-noise, making individual pulses often not easily distinguishable from noise or any putative faint emission. Although some of the approaches in the published literature attempt to address this, they lose efficacy when individual pulses appear indistinguishable from the noise, and as a result, can lead to less accurate measurements. Here we develop a new method (the $\mathbb{N}$sum algorithm) that uses sums of pulses for better distinguishability from noise and thus measures the nulling fraction more robustly. It can be employed for measuring nulling fractions in weaker pulsars and observations with a limited number of observed pulses. We compare our algorithm with the recently developed Gaussian Mixture Modelling approach, using both simulated and real data, and find that our approach yields consistent results for generic and weaker pulsars. We also explore quasi-periodicity in nulling and measure the related parameters for five pulsars, including PSRs~J1453$-$6413, J0950$+$0755 and J0026$-$1955, for which these are also the first such measurements. We compare and contrast our analysis of quasi-periodic nulling with previously published work and explore the use of spin-down energy loss ($\dot E$) to distinguish between different types of modulation behaviour.</p></br><a href="https://arxiv.org/pdf/2512.06642v1"><h2>Masked Autoencoder Pretraining on Strong-Lensing Images for Joint Dark-Matter Model Classification and Super-Resolution</h2></a>Authors:  Achmad Ardani Prasha, Clavino Ourizqi Rachmadi, Muhamad Fauzan Ibnu Syahlan, Naufal Rahfi Anugerah, Nanda Garin Raditya, Putri Amelia, Sabrina Laila Mutiara, Hilman Syachr Ramadhan</br>Comments: 21 pages, 7 figures, 3 table</br>Primary Category: cs.CV</br>All Categories: cs.CV, astro-ph.CO, astro-ph.IM, cs.AI, cs.LG</br><p>Strong gravitational lensing can reveal the influence of dark-matter substructure in galaxies, but analyzing these effects from noisy, low-resolution images poses a significant challenge. In this work, we propose a masked autoencoder (MAE) pretraining strategy on simulated strong-lensing images from the DeepLense ML4SCI benchmark to learn generalizable representations for two downstream tasks: (i) classifying the underlying dark matter model (cold dark matter, axion-like, or no substructure) and (ii) enhancing low-resolution lensed images via super-resolution. We pretrain a Vision Transformer encoder using a masked image modeling objective, then fine-tune the encoder separately for each task. Our results show that MAE pretraining, when combined with appropriate mask ratio tuning, yields a shared encoder that matches or exceeds a ViT trained from scratch. Specifically, at a 90% mask ratio, the fine-tuned classifier achieves macro AUC of 0.968 and accuracy of 88.65%, compared to the scratch baseline (AUC 0.957, accuracy 82.46%). For super-resolution (16x16 to 64x64), the MAE-pretrained model reconstructs images with PSNR ~33 dB and SSIM 0.961, modestly improving over scratch training. We ablate the MAE mask ratio, revealing a consistent trade-off: higher mask ratios improve classification but slightly degrade reconstruction fidelity. Our findings demonstrate that MAE pretraining on physics-rich simulations provides a flexible, reusable encoder for multiple strong-lensing analysis tasks.</p></br>