Astrophysics · Physics & Astronomy
Instrumentation & Methods for Astrophysics
The newest papers in Instrumentation & Methods for Astrophysics that ReadSearch has read, newest first. Each links to its own page, with its authors, date, where it was published and the original.
202 papers, from 6 September 2026 to 5 November 2026: 202 arXiv preprints.
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Newest papers, page 10
EPIMETHEUS: Effective PSF inferred from mosaics
Accurate knowledge of the point spread function (PSF) is essential for a wide range of astronomical applications, from astrometry to precision photometry.From starlight to dark matter: a stochastic interpolation approach to map dark matter from stellar density
The dark matter halo profile in galaxies holds key information about the nature of dark matter and galaxy formation.Laboratory Evidence that Methanol-Rich Ice Mantles Lower Methyl Formate Binding Energies
Complex organic molecules are widely detected in star-forming regions and are important precursors to prebiotic species.Scale-Vector Alignment: A Scale-Aware Framework for Spatially Resolved Morphological Similarity in Astronomical Images
Astronomical maps made with different tracers are not expected to have identical morphology.LATED: Ly$α$-anchored photometric selection of candidate metal-free and extremely metal-poor star formation from the end of reionisation to cosmic noon
Cosmological simulations allow a low-level tail of Population III (Pop III) star formation to persist to $z=2-6$.Using Machine Learning to Model Stellar Collisions in our Galactic Center
Direct collisions in the inner pc of the Galactic center can alter the orbits and properties of stars.1I/'Oumuamua-like objects's SFDs with older, current and future surveys
Interstellar Objects (ISOs) may provide direct evidence of planetesimals ejected from early-stage solar systems.Bayesian classification of astronomical spectra with class uncertainties
We developed a probabilistic machine learning method with the aim of performing the O(10)-way classification of low- and high-resolution spectra of stellar and extragalactic targets for the upcoming…Physics-Informed Neural Networks for Fast Multilayer Spectral Inversion of Hα 6562.8 A and Ca II 8542.1 A Spectra
Strong chromospheric absorption lines such as H$α$ 6562.8 A and Ca II 8542.1 A provide vital diagnostics of plasma dynamics and thermal structure in the solar chromosphere.Deep Learning Detection of Beyond-General-Relativity Deviations in Gravitational-Wave Signals: A Detection-Threshold Study with Real LIGO Noise
We study machine-learning detection of controlled beyond-General-Relativity (beyond-GR) deviations in gravitational-wave signals, using both synthetic aLIGO-PSD noise and real LIGO H1 detector strain.Reinforcement learning for post-coronagraphic wavefront control
Direct imaging of exoplanets is limited by the extreme contrast between the star and the planets, which is mitigated using a coronagraph.Rethinking Domain Specialization for Open-Ended Scientific Reasoning in Astronomy Language Models
Domain-specialized language models are widely used for scientific question answering, but stronger general-purpose systems raise a sharper question: when does domain-specific fine-tuning remain…Graph neural networks for exoplanet atmospheres
Calculating disequilibrium chemistry in exoplanet atmospheres remains a significant computational bottleneck in atmospheric retrievals.Parameter Estimation of Ringdown Quasinormal Modes with Autoencoder
Ringdown gravitational waves from binary black hole mergers can be modeled as superpositions of quasinormal modes (QNMs), whose frequencies and excitation factors encode properties of the remnant…Operational Range Bounding in Spectroscopy: A Safety Cage Framework for Machine Learning Models
Ensuring the reliability of black-box machine learning models in safety-critical space missions remains a significant challenge, particularly when ground-truth is unavailable for validation.Continuous Learning of Gravity Field Irregularities Around Small Bodies via Neural Hamiltonian ODEs
We propose to learn the unknown dynamics in the proximity of a small body directly from tracking data, representing them as a feed-forward neural network embedded in the system Hamiltonian.More than half of recent astronomy papers are written with language-model assistance
Language models leave a distinctive vocabulary in the prose they help write, and we measure how much of the astronomy literature now carries it.Closed-Form of the Local Galactic Potential and Stellar Distribution Function from Gaia DR3
The local dark matter density determines the strength of the signal expected in direct-detection experiments, yet published estimates from stellar motions disagree by more than their errors, and the…AstroSpecLM: A Spectrum-Language Model for Evidence-Grounded Astronomical Spectral Analysis
Astronomical spectra encode rich physical information, but drawing scientific conclusions from spectral features typically requires expert interpretation.Neural Posterior Estimation for Tomographic Weak Lensing Mass Mapping
Weak gravitational lensing shear and convergence trace the distribution of baryonic and dark matter across space, making them a powerful probe of cosmic structure.