168 papers
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.
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 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.
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 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 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 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 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 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 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 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.
The StarWhisper Telescope system is an AI agent framework that automates end-to-end astronomical observations by integrating large language models with specialized workflows to autonomously plan observations, analyze data, and trigger follow-ups, thereby reducing human intervention and demonstrating scalable potential for future large-scale telescope arrays.
This paper reviews the unique data analysis challenges of the Taiji space-based gravitational wave mission and introduces the Taiji Data Challenge II alongside the open-source Triangle toolkit, which together provide a high-fidelity simulation platform to bridge the gap between theoretical objectives and realistic detection pipelines.
This paper presents a deep learning pipeline based on a ConvLSTM2D architecture that effectively detects strongly lensed Type Ia supernovae in multi-band time-series imaging data, achieving high true-positive rates with low false-positive rates by the 7th to 9th observation to enable timely follow-up for upcoming surveys like LSST.
This paper presents an improved binary black hole search discriminator that utilizes singular value decomposition of real non-Gaussian noise transients to construct a statistic, demonstrating performance comparable to sine-Gaussian-based methods while offering a more direct approach for modeling complex glitches.
This paper introduces a novel time-series calibration method for global 21-cm cosmology experiments that utilizes noise wave parameters to model and correct for system drift and reference source impedance assumptions, thereby significantly reducing spectral residuals and parameter fit errors compared to previous techniques.
This paper introduces a flexible, parallel framework called Arepo-GW that integrates binary population synthesis with hydrodynamic simulations to generate gravitational wave event catalogs, demonstrating its application on the MillenniumTNG suite to reveal merger rates and progenitor properties consistent with current observations while highlighting specific discrepancies in binary black hole merger rates and redshift evolution.
The StarDICE IV paper presents a method that utilizes simultaneous thermal infrared observations to correct visible photometry for atmospheric gray extinction, enabling ground-based surveys to achieve sub-percent calibration precision even under non-photometric conditions.
This paper introduces ARTEMIDE, an open-source Mask R-CNN framework that successfully automates the detection and segmentation of strong gravitational lensing arcs in Euclid's Quick Data Release, achieving high precision and recall while demonstrating the viability of deep learning for analyzing massive wide-field survey datasets.
This paper demonstrates high-power RF pulse shaping experiments using a digital-only Next Generation LLRF (NG-LLRF) platform and a Cool Copper Collider prototype, achieving peak powers up to 5.4 MW and validating the system's capability to perform complex modulations like phase reversal and beam loading compensation without additional analog components.