156 papers
This paper presents a multi-level parallel software pipeline that achieves linear scaling for SPHERE-3 detector simulations by leveraging the natural atomicity of independent event processing across CORSIKA, Geant4, and Python-based image approximation stages without requiring locks on hot paths.
This paper demonstrates that while traditional global VTEC maps significantly overestimate Ionospheric Faraday Rotation for the VLA and MeerKAT, utilizing the ALBUS software with local GNSS station data yields highly accurate corrections, while also establishing the intrinsic electric vector position angles of standard calibrators 3C286 and 3C138 across a broad frequency range.
This paper demonstrates that the commonly used frequency range of for predicting solar-like oscillation detectability is suboptimal, and recommends adopting a narrower range of to maximize detection probabilities and yields for asteroseismic surveys.
This paper introduces a new method using Gaussian process regression to define curved STIS secondary echelle mode traces, which improves flux throughput by approximately 4% and has been used to update reference files for nine different modes across pre- and post-Servicing Mission 4 observations.
This paper presents a straightforward scaling method, derived from updated CALSPEC stellar models, to recalibrate pre-SM4 STIS echelle throughputs for eight modes, achieving typical flux accuracy improvements of 0.5–2.4% in the FUV and NUV.
By applying self-supervised contrastive learning and spectral energy distribution fitting to DESI Legacy Survey images, researchers overcame the challenge of foreground contamination to identify and spectroscopically confirm 16 new high-redshift quasars (–6.45), including three missed by traditional methods, thereby demonstrating a scalable framework for uncovering rare early-universe sources.
The paper introduces **unxt**, a Python package built on the **quax** framework that integrates **astropy.units** with **JAX** to enable seamless, high-performance, unit-aware scientific computing.
This paper proposes enhancing low-energy neutrino reconstruction and classification in the KM3NeT/ORCA telescope by utilizing transformer models with physics-informed attention masks to incorporate explicit detector and physical knowledge, thereby improving performance and cross-configuration fine-tuning capabilities.
This paper proposes a novel Local Maxima Particle Swarm Optimization (LMPSO) algorithm to simultaneously detect thousands of low signal-to-noise Galactic binary gravitational wave signals, effectively overcoming signal subtraction contamination and achieving a lower false alarm rate than existing iterative methods.
This paper presents the description and characterization of the FoReRo2 instrument's polarimetric and spectropolarimetric capabilities at the Rozhen Observatory, including a statistical analysis of Serkowski's law parameters, an evaluation of standard star stability, and demonstration of its performance through observations of RS Oph, comet C/2019 Y4, and the symbiotic star Z And.
By analyzing Gaia astrometry in 98 transition disks, this study finds that while companions are detected in 32% of the sample, most are stellar-mass objects that cannot explain the inner dust cavities, suggesting that if companions are responsible for cavity formation, they are likely planetary-mass bodies residing at wider separations.
The paper proposes CHRONOS, a next-generation ground-based gravitational-wave detector utilizing a ring-cavity Sagnac interferometer with torsion-bar test masses and quantum nondemolition speed-meter readout to achieve unprecedented sensitivity in the unexplored 0.1–10 Hz band, thereby enabling the detection of intermediate-mass black hole binaries, probing the stochastic gravitational-wave background, and performing quantum-limited geophysical observations.
This paper presents and validates models for atmospheric and thermal systematics affecting the GroundBIRD telescope's MKID arrays, identifying atmospheric loading as the primary cause of resonance frequency shifts under typical observation conditions.
This paper proposes a modularized, fully differentiable deep learning architecture that generates physically consistent radio pulse signals from in-ice neutrino interactions, enabling gradient-based optimization of the future IceCube-Gen2 detector design.
This paper proposes a novel, computationally transparent Independent Component Analysis (ICA) framework capable of estimating and subtracting non-linearly coupled, non-stationary noise from gravitational wave data, as demonstrated through successful applications on both simulated datasets and real KAGRA detector observations.
This paper introduces a deep learning framework utilizing a convolutional encoder-decoder architecture and transfer learning to rapidly and accurately generate Teukolsky amplitudes for generic Extreme Mass Ratio Inspirals (EMRIs), overcoming the computational limitations of traditional methods for space-borne gravitational wave detection.
This paper presents a GPU-accelerated transient detection pipeline for DECam time-domain surveys that integrates image differencing and CNN-based classification to achieve real-time processing, high completeness in identifying real transients, and efficient artifact rejection for rapid scientific discovery.
This paper introduces CODES, a principled framework for systematically optimizing and benchmarking neural surrogate models for nonequilibrium chemistry, revealing distinct accuracy-efficiency trade-offs across architectures while providing public tools for reproducible astrochemical simulations.
This paper presents end-to-end numerical simulations demonstrating that while non-common path aberration compensation significantly reduces residual starlight in the SAXO+ system, a dark hole loop achieves a superior factor of 200 reduction, with the study also establishing that calibrating pyramid optical gains is beneficial for single-loop systems but unnecessary for the dual-loop SAXO+ architecture.
One hundred years after Bernard Lyot's pioneering work, the PICSARR project presents new high-precision polarimetric observations of Venus that confirm historical particle size models while revealing significant short-term variability and distinct ultraviolet polarization anomalies in the polar regions, suggesting lower cloud tops and a larger Rayleigh scattering component compared to equatorial areas.