165 papers
This paper introduces an efficient ANN-emulator framework for the SCRIPT semi-numerical model that reduces the computational cost of constraining the Epoch of Reionization by up to 70-fold while maintaining statistical fidelity, thereby enabling the analysis of high-dimensional models required for upcoming JWST and 21 cm datasets.
This paper demonstrates how large language models can automate the analysis of NASA's General Coordinates Network (GCN) Circulars by employing neural topic modeling for clustering, contrastive fine-tuning for classification, and retrieval-augmented generation to achieve high-accuracy extraction of gamma-ray burst redshifts, thereby significantly enhancing astronomical text mining capabilities.
This paper introduces pkdgrav3, a high-performance, fully parallel tree-SPH code optimized for hybrid CPU/GPU architectures that accurately simulates large-scale, self-gravitating hydrodynamic systems such as planetary impacts.
This paper presents an end-to-end deep learning pipeline that extends the AutoSourceID method to Cherenkov Telescope Array Observatory (CTAO) simulated data, offering a versatile framework for the detection, localization, and characterization of gamma-ray sources as a step toward a foundational model for astrophysical source detection.
This paper proposes SEMD, a deep learning model based on Vision Transformers that integrates Squeeze-and-Excitation mechanisms and multilayer perceptrons to efficiently and robustly identify strongly lensed gravitational wave events by analyzing morphological similarities in time-frequency spectrograms, thereby overcoming the computational limitations of traditional Bayesian inference methods.
This paper demonstrates that the orbital eccentricity of massive black hole binaries significantly enhances LISA's parameter estimation capabilities, leading to a substantial increase in the number of bright standard sirens and resulting in markedly tighter cosmological constraints on parameters such as the Hubble constant and dark energy.
This paper introduces SELDON, a continuous-time variational autoencoder that combines masked GRU-ODE encoders, neural ODE propagators, and interpretable Gaussian-basis decoders to efficiently model sparse, irregular astrophysical light curves and extract physically meaningful parameters for rapid supernova prioritization.
This paper presents an automated framework utilizing the ALeRCE broker to systematically search for optical counterparts to LIGO-Virgo-KAGRA gravitational wave superevents during the O4a and O4b runs, successfully identifying several plausible candidates including a transient consistent with a Bowen fluorescence flare in an AGN.
This study analyzes six years of cosmic muon rate data from a high-latitude station in Svalbard, revealing that seasonal atmospheric temperature variations drive a pronounced annual modulation and demonstrating the effectiveness of tailored temperature correction techniques compared to lower-latitude measurements.
This paper introduces a novel modified-gradient (MG) method that employs an implicit projection to reformulate magnetic field gradients, thereby achieving exact divergence-free results with round-off precision in meshless magnetohydrodynamics and demonstrating superior performance over constrained-gradient techniques and the GIZMO code across various test cases.
This paper outlines the scientific objectives and observational methodology of the OPTIMus survey, which utilizes multi-wavelength data from optical, infrared, and millimeter telescopes to comprehensively characterize the complex interstellar medium surrounding young massive stars.
This paper introduces the galmex Python package to generate a comprehensive catalog of non-parametric morphological indices for DECaLS galaxies, demonstrating their effectiveness—particularly when combined with LightGBM machine learning—in reliably classifying spiral and elliptical galaxies for future southern hemisphere surveys.
This paper presents an autoencoder-based machine learning framework that effectively identifies anomalous stellar spectra, such as instrumental errors and rare carbon or oxygen-rich stars, within the MaNGA Stellar Library by leveraging reconstruction errors as anomaly scores.
This paper presents a transformer-based foundation model that leverages Gaia XP spectra and APOGEE data to simultaneously predict atmospheric parameters, mass, and age for evolved stars with high accuracy, demonstrating that data-driven approaches can effectively internalize stellar physics to overcome spectral degeneracies in Galactic archaeology.
This study analyzes ACS/WFC bias frames to determine that read noise exhibits no column dependence but is influenced by row number due to accumulated dark current, while also finding that physical pre-scans have lower noise than science arrays, amplifiers A and C show an initial linear noise decrease, and masking hot pixels reduces column noise.
The STIS team has updated the on-orbit sensitivity curves for the instrument's echelle modes following Servicing Mission 4 by utilizing improved CALSPECv11 flux models for the G 191-B2B standard star and releasing new blaze shift coefficients and ripple tables.
This paper benchmarks pre-main sequence stellar evolutionary tracks by comparing HR diagram-derived masses with dynamical masses from ALMA disk observations of Upper Scorpius stars, finding that models with moderate spot coverage best match the data and that incorporating dynamical mass priors significantly reduces age scatter and improves model consistency.
This paper proposes a data-driven "structured generalized sliced Wasserstein distance" method using randomized neural networks to directly analyze two-dimensional polarized images from Gas Pixel Detectors, successfully determining X-ray polarization and incident angles while demonstrating high consistency with traditional statistical models.
This paper introduces STRIDE, a novel streaming-based dedispersion algorithm that generates per-pixel time series from interferometric images by partitioning dispersive sweeps across both time and frequency dimensions, thereby drastically reducing memory requirements and enabling efficient transient searches for low-frequency widefield interferometers like the MWA and SKA-Low.
This study presents new VLBA astrometry for 11 radio stars, achieving high-precision positions, parallaxes, and proper motions that significantly improve the alignment between the radio-based ICRF and the optical Gaia-CRF at the bright end.