We introduce the Morph approximation, a class of product approximations of probability densities that selects low-order disjoint parameter blocks by maximizing the sum of their total correlations. We use the posterior approximation via Morph as the importance distribution in optimal bridge sampling. We denote this procedure by MorphZ, which serves as a post-processing estimator of the marginal likelihood. The MorphZ estimator requires only posterior samples together with the prior and likelihood, and is fully agnostic to the choice of sampler. We evaluate MorphZ's performance across statistical benchmarks, pulsar timing array (PTA) models, compact binary coalescence (CBC) gravitational-wave (GW) simulations and the GW150914 event. Across these applications, spanning low to high dimensionalities, MorphZ yields accurate evidence at substantially reduced computational cost relative to standard approaches, and can improve these estimates even when posterior coverage is incomplete. Its bridge sampling relative error diagnostic provides conservative uncertainty estimates. Because MorphZ operates directly on posterior draws, it complements exploration-oriented samplers by enabling fast and reliable evidence estimation, while it can be seamlessly integrated into existing inference workflows.
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 Estimating the auto power spectrum of cosmological tracers from line-intensity mapping (LIM) data is often limited by instrumental noise, residual foregrounds, and systematics. Cross-power spectra between multiple lines offer a robust alternative, mitigating noise bias and systematics. However, inferring the auto spectrum from cross-correlations relies on two key assumptions: that all tracers are linearly biased with respect to the matter density field, and that they are strongly mutually correlated. In this work, we introduce a new diagnostic statistic, \(\mathcal{Q}\), which serves as a data-driven null test of these assumptions. Constructed from combinations of cross-spectra between four distinct spectral lines, \(\mathcal{Q}\) identifies regimes where cross-spectrum-based auto-spectrum reconstruction is unbiased. We validate its behavior using both analytic toy models and simulations of LIM observables, including star formation lines ([CII], [NII], [CI],[OIII]) and the 21-cm signal. We explore a range of redshifts and instrumental configurations, incorporating noise from representative surveys. Our results demonstrate that the criterion \( \mathcal{Q} \approx 1 \) reliably selects the modes where cross-spectrum estimators are valid, while significant deviations are an indicator that the key assumptions have been violated. The \( \mathcal{Q} \) diagnostic thus provides a simple yet powerful data-driven consistency check for multi-tracer LIM analyses. Semi-analytic models are a widely used approach to simulate galaxy properties within a cosmological framework, relying on simplified yet physically motivated prescriptions. They have also proven to be an efficient alternative for generating accurate galaxy catalogs, offering a faster and less computationally expensive option compared to full hydrodynamical simulations. In this paper, we demonstrate that using only galaxy $3$D positions and radial velocities, we can train a graph neural network coupled to a moment neural network to obtain a robust machine learning based model capable of estimating the matter density parameters, $Ω_{\rm m}$, with a precision of approximately 10%. The network is trained on ($25 h^{-1}$Mpc)$^3$ volumes of galaxy catalogs from L-Galaxies and can successfully extrapolate its predictions to other semi-analytic models (GAEA, SC-SAM, and Shark) and, more remarkably, to hydrodynamical simulations (Astrid, SIMBA, IllustrisTNG, and SWIFT-EAGLE). Our results show that the network is robust to variations in astrophysical and subgrid physics, cosmological and astrophysical parameters, and the different halo-profile treatments used across simulations. This suggests that the physical relationships encoded in the phase-space of semi-analytic models are largely independent of their specific physical prescriptions, reinforcing their potential as tools for the generation of realistic mock catalogs for cosmological parameter inference. 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. 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. 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.Clues from $\mathcal{Q}$--A null test designed for line intensity mapping cross-correlation studies
Authors: Debanjan Sarkar, Ella Iles, Adrian LiuComments: 27 pages, 16 figures, 5 tables. Comments are welcomePrimary Category: astro-ph.COAll Categories: astro-ph.CO, astro-ph.IM, physics.data-anGalaxy Phase-Space and Field-Level Cosmology: The Strength of Semi-Analytic Models
Authors: Natalí S. M. de Santi, Francisco Villaescusa-Navarro, Pablo Araya-Araya, Gabriella De Lucia, Fabio Fontanot, Lucia A. Perez, Manuel Arnés-Curto, Violeta Gonzalez-Perez, Ángel Chandro-Gómez, Rachel S. Somerville, Tiago CastroComments: 23 pages, 5 figuresPrimary Category: astro-ph.COAll Categories: astro-ph.CO, astro-ph.GA, cs.LGMagnetic activity of ultracool dwarfs in the LAMOST DR11
Authors: Yue Xiang, Shenghong Gu, Dongtao CaoComments: 13 pages, 10 figures, accepted for publication in ApJPrimary Category: astro-ph.SRAll Categories: astro-ph.SR, astro-ph.IM, cs.LGThe ONs and OFFs of Pulsar Radio Emission: Characterizing the Nulling Phenomenon
Authors: Garvit Grover, N. D. Ramesh Bhat, Samuel J. McSweeney, Christopher P. Lee, Chia Min Tan, Shih Ching Fu, Bradley W. MeyersComments: Accepted for publication in the Astrophysical Journal, 25 pages, 9 Figures, 4 TablesPrimary Category: astro-ph.HEAll Categories: astro-ph.HE, physics.data-anMasked Autoencoder Pretraining on Strong-Lensing Images for Joint Dark-Matter Model Classification and Super-Resolution
Authors: Achmad Ardani Prasha, Clavino Ourizqi Rachmadi, Muhamad Fauzan Ibnu Syahlan, Naufal Rahfi Anugerah, Nanda Garin Raditya, Putri Amelia, Sabrina Laila Mutiara, Hilman Syachr RamadhanComments: 21 pages, 7 figures, 3 tablePrimary Category: cs.CVAll Categories: cs.CV, astro-ph.CO, astro-ph.IM, cs.AI, cs.LG