156 papers
This paper presents the design and multi-dimensional performance analysis of a lightweight, off-axis four-mirror laser transmitting telescope for space gravitational wave detection, demonstrating that the optimized structure achieves high optical efficiency, nanometer-level surface stability, and robust thermal and dynamic performance under harsh space conditions.
This paper presents a methodology for searching for unintended electromagnetic radiation from Starlink satellites using the 21 Centimeter Array, which, despite limited sensitivity preventing the detection of Starlink emissions, successfully validated its transit prediction framework by identifying ORBCOMM signals and attributed observed broadband bursts to local power line arcing rather than satellite interference.
This paper estimates the detection rates of TeV-emitting Gamma-Ray Bursts similar to GRB 221009A for current and upcoming ground-based observatories (LHAASO, LACT, and SWGO) under two emission models, predicting annual detection frequencies ranging from 0.03 to 0.4 events depending on the instrument and model assumptions.
This paper proposes a new, more efficient broadband SETI strategy that leverages existing non-SETI astronomical survey data to constrain the prevalence of nearby communicative civilizations and alien probes, arguing that the lack of detected signals or probes suggests no such civilization has been within 100 light-years of Earth in the past few billion years.
This paper introduces a differentiable and optimizable 3D geometrical model that successfully reproduces observed molecular spectral cubes of the prestellar core L1544, revealing that an asymmetric density and velocity structure is required to explain the velocity differences between p-NH2D and N2D+.
This paper demonstrates that while advanced quantum sensors cannot directly detect gravitational waves via internal atomic or center-of-mass coupling due to negligible signal gains, they can still enhance existing laser and atom interferometers by factors of 1.04 to 2.4 by exploiting the light-propagation coupling mechanism and addressing specific noise limitations.
This paper introduces a multi-modal deep learning framework utilizing Vision Transformers and cross-attention mechanisms to accurately infer protostellar mass, inclination, and position angle from CO observations, effectively overcoming projection biases and demonstrating superior robustness compared to traditional convolutional networks.
This paper introduces BLISS, a simulation-based inference framework that utilizes convolutional neural networks and denoising autoencoders to probabilistically detect, deblend, and measure galaxy properties in crowded astronomical images, demonstrating significant improvements in flux accuracy for blended and faint objects compared to deterministic methods.
This paper demonstrates that adopting standardized computer vision architectures pre-trained on astronomical data, particularly from Galaxy Zoo, significantly improves the performance and efficiency of alert classification in wide-field time-domain surveys compared to traditional custom CNNs trained from scratch.
This paper demonstrates that Variational Autoencoders can effectively compress and analyze approximately 200,000 DESI galaxy spectra to identify both instrumental artifacts and unique astrophysical objects, while also revealing interpretable latent structures that separate object classes and track physical characteristics like star formation and emission lines.
This study presents a modified commercial laser mass spectrometer that serves as a validated, high-fidelity analog for the Mars Organic Molecule Analyzer (MOMA), enabling rapid testing, structural identification of organics in mineral matrices, and the generation of pre-flight reference data to support the upcoming Rosalind Franklin rover mission.
This paper demonstrates that the previously reported gravitational anomaly in wide binaries, which supported Modified Newtonian Dynamics, is likely an artifact of orbital modeling choices, as a hierarchical Bayesian analysis accounting for three-dimensional orbital elements yields results consistent with standard Newtonian gravity.
This paper presents a proof-of-concept workflow using the Hough Transform and SatChecker to detect, identify, and measure the brightness of satellite streaks in archival DECam images, demonstrating the feasibility of characterizing orbital debris impacts on astronomical surveys while highlighting challenges in detecting faint and transient glints.
This paper presents a novel, automated methodology using the HEALPix framework and a comprehensive data processing pipeline to debias meteor observations from the Desert Fireball Network, successfully quantifying clear-sky coverage and measuring the flux of the 2015 Southern Taurid meteor shower to address challenges in estimating meteoroid flux density.
This paper presents a Python-based, CASA-compatible framework for direction-dependent calibration and peeling that effectively suppresses artifacts from bright sources in uGMRT 650 MHz data, thereby significantly improving image fidelity and faint-source detectability through an optimized model-restoration strategy.
This paper presents the design, optimization, and performance analysis of the coded mask for the Lunar Electromagnetic Monitor in X-rays (LEM-X), a proposed wide-field lunar observatory utilizing Silicon Drift Detectors to achieve high-precision imaging and rapid localization of transient X-ray sources for multi-messenger astrophysics.
This paper presents the technical specifications, calibration status, and planned operational strategies of the Asteroid Framing Cameras on ESA's Hera mission, which are designed to support navigation and scientific investigations—including hazard detection, shape reconstruction, and surface mapping—of the Didymos binary asteroid system and the DART impact site on Dimorphos.
By applying a novel data-driven spectral factorization to JWST observations of Europa's leading hemisphere, this study reveals that CO2 enrichment extends beyond Tara Regio in a lens-like pattern across chaos units, suggesting that volatile retention is driven by near-surface microphysics and offering new insights into surface-interior exchange and the moon's habitability.
This paper introduces CSST-PSFNet, a deep learning model combining residual networks, lightweight Transformers, and variational latent representations to achieve high-fidelity point spread function reconstruction for the Chinese Space Station Survey Telescope, demonstrating superior accuracy in size and ellipticity recovery compared to PSFEx and robustness in weak-label adaptation scenarios.
This paper proposes and validates a computational method that directly infers intra-pixel sensitivity variations (IPSVs) from stellar images to reconstruct the instrumental point spread function, thereby reducing astrometric centroiding errors by nearly 30 times and enabling continuous detector calibration for future space-based surveys.