465 papers
This collection explores the fascinating world of instrumentation and detection within physics, focusing on the tools and sensors that allow scientists to measure the universe. From advanced particle trackers to sensitive gravitational wave detectors, these innovations form the backbone of modern discovery, turning abstract theories into observable data.
On Gist.Science, we process every new preprint in this field as it appears on arXiv, ensuring you stay ahead of the curve. Each paper is accompanied by a clear, plain-language explanation alongside a detailed technical summary, bridging the gap between complex research and accessible knowledge.
Below are the latest papers in physics instrumentation and detection, offering fresh insights into how we observe the fundamental nature of reality.
This paper demonstrates that nickel plating on Al/B₄C composite radial blades in Multi-Grid neutron detectors effectively suppresses alpha-induced background noise by a factor of approximately 1170, reducing the overall background counting rate to about 20% of that observed in detectors using uncoated radio-pure aluminum.
This paper details the energy calibration procedures and performance of HPGe detectors in the LEGEND-200 experiment, demonstrating a high-resolution energy reconstruction of ~keV at the value and exceptional long-term stability essential for the search for neutrinoless double beta decay.
This paper details the identification of a timing flaw in the ABCStar ASIC that threatened production yields, and the successful mitigation of this issue through a combination of increasing the core operating voltage and adjusting the clock duty cycle, thereby avoiding costly process changes or redesigns and enabling the continued production of ATLAS ITk detector modules.
This paper reports a ~5x improvement in the energy resolution of Kinetic Inductance Phonon-Mediated (KIPM) detectors by coupling them to a wideband Kinetic Inductance Travelling Wave Parametric Amplifier (KI-TWPA) operating near the Standard Quantum Limit, while also analyzing remaining noise sources such as passive component losses and two-level systems.
This paper introduces a deep neural network-based region of interest detection method (DNN ROI) for the Short-Baseline Neutrino Program's liquid argon time projection chambers, which outperforms traditional thresholding algorithms in identifying ionization charge and reconstructing neutrino interactions while demonstrating greater robustness against detector variations.
This paper describes the installation of a large, versatile vacuum chamber at the NIST Center for Neutron Research to stabilize perfect-crystal neutron interferometry against environmental fluctuations and enable cryogenic sample studies, demonstrated by the first successful measurement of a Ni60Cu40 sample cooled from 300 K to 4 K.
This paper reports on laboratory measurements of late-pulses and after-pulses in a large-area Hamamatsu R7081 photomultiplier tube with an improved quantum-efficiency photocathode, finding that while the late-pulse contribution is small, it remains non-negligible for accurate neutrino detection reconstruction.
This thesis presents the development of an improved RPC-based gaseous photodetector (GasPM) for the Belle II experiment, featuring a new algorithm to suppress photon feedback, single-electron discrimination capabilities, and the qualification of a radiation-resistant LaB photocathode to achieve picosecond time resolution.
This paper demonstrates through numerical simulations across multiple timescales that first-order SMC achieves slightly better time-domain accuracy than LQG while maintaining nearly identical stability, and substantially outperforms bang-bang (BB) steering in accuracy while avoiding BB's short-term instability.
The paper introduces the Spatially Aware Linear Transformer (SAL-T), a physics-inspired architecture that combines linear attention with spatially aware partitioning and convolutional layers to achieve transformer-level accuracy in particle jet tagging while significantly reducing computational complexity and inference latency.