85 papers
This paper introduces the first end-to-end differentiable optical particle detector simulator that unifies simulation, calibration, and reconstruction into a single gradient-based framework, demonstrating improved accuracy, speed, and flexibility for analyzing large-scale neutrino detectors compared to traditional methods.
This study demonstrates that Analog Pixel Test Structures fabricated in 65 nm CMOS technology, utilizing both DC- and AC-coupled designs, maintain high detection efficiency and sub-70 ps time resolution even after exposure to radiation levels up to 10^15 NIEL, confirming their suitability for future high-energy physics tracking systems.
This paper presents the first experimental demonstration of a directional dark-field setup for nanoscale full-field transmission X-ray microscopy, enabling the orientation mapping of anisotropic nanostructures below the spatial resolution limit and facilitating quantitative structural characterization in fields ranging from biomineralization to advanced materials.
This paper addresses the confusion caused by non-SI quantities and misleading terminology in phase and amplitude noise measurements, advocating for the universal adoption of the International System of Units (SI) and clear, unambiguous language to standardize the field.
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 characterizes the high detective quantum efficiency of the Timepix4 hybrid pixel detector in event-driven mode at 100 kV and 200 kV and demonstrates its capability to capture weak diffraction signals from polycrystalline gold nanoparticles at 200 kV.
This paper demonstrates that robust defect detection in fluctuating magnetic systems, such as Ni80Fe20, can be achieved by training U-Net models on statistical descriptors like temporal mean, standard deviation, and latent entropy derived from micromagnetic simulations, provided the training data accurately reflects the expected noise statistics.
This paper introduces AIMD-L, an automated, high-throughput laboratory featuring custom instruments (HELIX and MAXIMA) and a robotic workflow designed to rapidly characterize the microstructure and properties of structural metals and ceramics for extreme environments, thereby closing the loop between AI-driven experimentation and data analysis.
This paper introduces a cryogenic widefield NV-diamond microscope that enables rapid, micrometer-scale imaging of magnetic flux trapping in superconducting devices, revealing critical vortex expulsion behaviors in Nb thin films and offering a high-throughput tool for improving the reliability of scalable superconducting electronics.
This paper identifies a superlinear response and a significant energy-dependent timing shift in sinusoidally-gated avalanche single-photon detectors, proposing two new attacks that exploit these energy-time effects to violate key security assumptions in Quantum Key Distribution.
This paper presents the design, test results, and Monte-Carlo simulations of a new heavy-ion detector utilizing a 10-picosecond resolution RF Timer to achieve precise background suppression and separation of prompt and delayed events for direct measurements of heavy hypernuclei lifetimes.
This paper introduces a novel methodology for analyzing neutron and x-ray scattering event data that bypasses traditional histogramming and least-squares fitting to achieve significantly higher accuracy and efficiency while reducing systematic errors, despite requiring increased computation time and offering a less intuitive approach.
The paper introduces Neural Field Thermal Tomography (NeFTY), a differentiable physics framework that parameterizes 3D material diffusivity as a continuous neural field optimized via a rigorous numerical solver to achieve high-resolution, quantitative reconstruction of subsurface defects from transient surface temperature measurements, overcoming the limitations of traditional 1D approximations and soft-constrained PINNs.
This white paper presents a community-driven vision to prioritize research and development in hardware-based machine learning systems—leveraging emerging technologies like AI, silicon microelectronics, and quantum algorithms—to address the unprecedented data acquisition challenges and enable real-time scientific discovery in next-generation particle physics experiments.
This paper demonstrates that integrating plastic scintillator detectors read out by the MuTRiG ASIC into the MuSiP spectrometer overcomes the timing limitations of silicon pixel detectors, enabling high-rate vertex-reconstructed muon-spin spectroscopy with sub-300 ps resolution and the ability to resolve precession frequencies exceeding 50 MHz.
This paper validates the chemical and mechanical robustness of the COSINE-100U NaI(Tl) encapsulation for low-temperature operation in liquid scintillator by demonstrating stable performance over approximately 150 days at -33°C following initial compatibility checks.
This paper presents a novel unsupervised deep learning method that utilizes a simplified physical model of optical photon transport to automatically extract photomultiplier tube calibration constants from radioactive background events in the large-scale SNO+ liquid scintillation detector.
This paper presents the design, construction, and successful operation of a dual-phase xenon time projection chamber prototype for the RELICS experiment, demonstrating its ability to achieve the required sub-keV energy threshold and validating the core technologies and methodologies essential for the full-scale coherent elastic neutrino-nucleus scattering detector.
This paper presents the successful development, installation, and commissioning of a modular and extensible control system for Fresnel zone plate zooming optics at KEK's AR-NE1A beamline, utilizing the STARS framework and the new CCDC command set to ensure reliable operation, enhanced flexibility, and interoperability for both routine and advanced experimental protocols.
This paper introduces the first application of deep neural networks to continuous-wave NMR polarization measurements, demonstrating that machine learning-based signal extraction significantly reduces noise and fitting uncertainties to improve the precision and reliability of target polarization monitoring in high-energy and nuclear physics experiments.