176 papers
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.
This paper introduces Synference, a fast and flexible Python framework that utilizes simulation-based inference to efficiently infer galaxy physical properties and photometric redshifts from multi-band photometry, achieving a ~1700-fold speedup over traditional methods while maintaining high accuracy and calibration.
This paper presents a new single-pulse search pipeline and associated software toolkits (FRBfaker and RFIbye) for the northern High Time Resolution Universe survey, demonstrating its readiness for full data processing through FRB-mimicking simulations and the detection of known pulsars, a RRAT, and numerous new single-pulse candidates.
This paper proposes and experimentally validates a novel "Phase-Modulated-sideband × Phase-Modulated-sideband Wave Front Sensing" (PMPMWFS) technique for gravitational-wave detectors, which demodulates signals at the difference frequency between two anti-resonant sidebands to effectively decouple Power Recycling Cavity and incident beam alignment from arm cavity signals, thereby enabling stable, multi-degree-of-freedom control.
Using a comprehensive dataset of 897 pulsars and a robust Bayesian framework, this study overturns the long-held consensus that pulsar radio spectra are predominantly simple power laws, revealing instead that complex curved or broken spectral shapes are the norm and that previous findings were largely statistical artifacts.
This paper proposes a deep neural network-based method that overcomes the limitations of conventional techniques by achieving both high computational efficiency and identification precision (97.26–99.91%) for weak stability boundary structures, thereby facilitating their application in ballistic capture analysis and low-energy transfer construction.
This paper proposes and validates a double-aperture photometry strategy as a computationally efficient alternative to on-board centroid shifts for detecting false positives in the PLATO mission, demonstrating that secondary flux measurements achieve the highest detection efficiency (92%) for rejecting eclipsing binary contaminants among Sun-like stars.
The paper introduces -EFTCAMB, a publicly available, Cobaya-integrated Python extension of EFTCAMB that enables robust cosmological testing of arbitrary covariant Horndeski gravity theories through both EFT mapping and direct scalar field equation solving, the latter of which remains valid even in regimes where the EFT approach fails.
This paper introduces and validates "Nemesis," a flexible and scalable multi-scale, multi-physics algorithm built on AMUSE that accurately simulates complex astrophysical systems like star clusters with planetary binaries, matching the precision of direct N-body codes while maintaining computational efficiency through effective time synchronization and parallelization.