896 papers
This paper validates the accuracy of prior-informed Fisher-matrix analyses by comparing them against real gravitational-wave data from GWTCs, demonstrating that while priors are crucial for handling parameter degeneracies, the Fisher approximation remains a reliable tool for future Einstein Telescope science-case studies.
This paper presents the successful prototyping and rigorous quantitative evaluation of novel 6.2-mm-pitch fiber positioner modules, demonstrating that both trillium-based and independently actuated robotic designs can meet the stringent mechanical and optical tolerances required for next-generation, highly multiplexed astronomical spectroscopic surveys.
The paper introduces AICE, a fast and accurate artificial neural network tool that predicts the fractional composition of interstellar ices (including H₂O, CO, CO₂, CH₃OH, NH₃, and CH₄) from infrared absorption spectra in under one second, enabling the systematic analysis of hundreds of JWST observations with typical errors of approximately 3%.
This paper proposes a graph-based method using pulsar timing residuals to detect the stochastic gravitational wave background by analyzing network structural characteristics, demonstrating its ability to identify signals with a strain amplitude of and providing weak evidence for an SGWB in the NANOGrav 15-year dataset.
This paper proposes a hybrid spectroscopic architecture that couples a medium-resolution Fourier transform spectrometer with a low-resolution on-chip filterbank spectrometer to significantly reduce photon noise and enable high-sensitivity, large-field line intensity mapping at resolutions of R~1000 for ground-based and balloon-borne astronomy.
This paper presents the results of the first 24,186 hours of the GREENBURST commensal survey using the Green Bank Telescope, which discovered a new 2.2-second pulsar (PSR J0039+5407) and monitored three known repeating FRBs, while also highlighting the challenges of its single-beam design in distinguishing genuine transients from radio frequency interference.
This study validates olivine as a promising candidate for the "paleo-detector" technique by using STEM to characterize heavy-ion tracks at various depths without etching, confirming that observed changes in track continuity align with SRIM simulation predictions regarding the transition between electronic and nuclear stopping power.
This paper proposes a deep learning framework that disentangles intrinsic physical signals from sensor-specific artifacts in multi-instrument data by leveraging overlapping observations and counterfactual generation, thereby enabling unconfounded parameter inference and instrument-independent analysis, as demonstrated on astrophysical galaxy images.
This paper introduces PEACC, a novel dual-source, GPS-synchronized digital calibration system deployed on a drone that enables high-fidelity, wideband coherent beam calibration for 21 cm intensity mapping experiments, significantly outperforming traditional auto-correlation methods in low-signal-to-noise regimes.
This paper proposes a multi-modal deep learning network combining ResNet-50 and BiLSTM to fuse image and catalog data from China Space Station Telescope (CSST) simulations, achieving over 99.6% recall for star-galaxy classification and demonstrating robust performance on faint and high-redshift objects.
This paper describes the design, commissioning, and initial on-sky validation of a dedicated solar seeing monitor developed for the Paranal site to characterize daytime atmospheric conditions and optimize the observing performance of the PoET solar telescope.
This paper presents a fast direct solver within a Method-of-Moments framework that exploits rotational symmetry and multipole decomposition to efficiently compute mutual coupling and embedded element patterns for large reflector arrays, successfully enabling the first full-wave analysis of the 320-element HERA core.
This paper introduces FIMER, an information-geometric framework that reconstructs effective measurement uncertainties in heterogeneous astrophysical datasets by combining weighted Fisher-information geometry with physically motivated priors, thereby enabling reliable statistical inference even when reported uncertainties are incomplete, underestimated, or lack cross-correlation data.
This study utilizes laboratory simulations of Pluto's N2/CH4/CO atmosphere to demonstrate that seasonal variations in methane concentration significantly alter haze formation pathways and nitrogen incorporation, thereby providing critical data to refine planetary haze models and interpret New Horizons observations.
This paper demonstrates that the CHRONOS torsion-bar detector, utilizing a differential gravitational calibrator to cancel Newtonian torque, can probe Yukawa-type deviations from Newtonian gravity with a sensitivity of at an 8-meter range, a performance ultimately limited by systematic uncertainties in source-mass geometry rather than statistical noise.
This paper numerically investigates and proposes the "Space-Clock Elevator," a modular orbital transport architecture that utilizes synchronized rotating tethers and intermediate elliptical nodes to enable propellant-free, multi-stage payload transfers between different orbital regimes while maintaining dynamic stability.
This paper introduces version 2.1 of the public Python code PRECESSION, which extends its capabilities to model the post-Newtonian dynamics of precessing black hole binaries on eccentric orbits through a semi-automatic adaptation method and new averaged evolutionary equations.
This study demonstrates that deep neural networks can successfully reconstruct the low-order geometric features, such as overall shape and orientation, of transiting objects from their light curves, while revealing inherent limitations in inferring higher-order details like eccentricity and non-convex features.
This paper demonstrates that incorporating photometric phase variations into an orbit ranking scheme significantly improves the deconfusion of directly imaged multi-planet systems, thereby aiding future missions like the Habitable Worlds Observatory in correctly identifying habitable zone planets.
This paper investigates and quantifies the morphological biases introduced by varying imaging quality in galaxy surveys, offering empirical corrections, new measurement metrics, and a dedicated Python package to ensure robust analysis of galaxy evolution for upcoming Rubin LSST and JWST data.