相关论文: Deep learning for improved keV-scale recoil identi…

New techniques for the laboratory direct detection of dark matter weakly interacting massive particles (WIMPs) are sensitive to the recoil direction of the struck nuclei. We compute and compare the directional recoil rates…

The DEAP-1 low-background liquid argon detector was used to measure scintillation pulse shapes of electron and nuclear recoil events and to demonstrate the feasibility of pulse-shape discrimination (PSD) down to an electron-equivalent…

We investigate the sensitivity of a large, underground LArTPC-based neutrino detector to dark matter in the Galactic Center annihilating into neutrinos. Such a detector could have the ability to resolve the direction of the electron in a…

We investigate the feasibility of using deep learning techniques, in the form of a one-dimensional convolutional neural network (1D-CNN), for the extraction of signals from the raw waveforms produced by the individual channels of liquid…

Particle identification (PID) is essential for future particle physics experiments such as the Circular Electron-Positron Collider and the Future Circular Collider. A high-granularity Time Projection Chamber (TPC) not only provides precise…

The Dark Matter Time Projection Chamber (DMTPC) is a direction-sensitive detector designed to measure the direction of recoiling $^{19}$F and $^{12}$C nuclei in low-pressure CF$_4$ gas using optical and charge readout systems. In this…

We extend the study of the performance of a prototype two-phase liquid xenon WIMP dark matter detector to recoil energies below 20 keV. We demonstrate a new method for obtaining the best estimate of the energies of events using a calibrated…

We propose a novel weakly-supervised semantic segmentation algorithm based on Deep Convolutional Neural Network (DCNN). Contrary to existing weakly-supervised approaches, our algorithm exploits auxiliary segmentation annotations available…

Ultra-high-energy (UHE) neutrinos ($>10^{16}$ eV) can be measured cost-effectively using in-ice radio detection, which has been explored successfully in pilot arrays. A large radio detector is currently being constructed in Greenland with…

CYGNUS is a proposed global network of large-scale gas time projection chambers (TPCs) with the capability of directionally detecting nuclear and electron recoils at $\gtrsim$keV energies. The primary focus of CYGNUS so far has been the…

Forthcoming imaging surveys will potentially increase the number of known galaxy-scale strong lenses by several orders of magnitude. For this to happen, images of tens of millions of galaxies will have to be inspected to identify potential…

We report a measurement of the ionization efficiency of silicon nuclei recoiling with sub-keV kinetic energy in the bulk silicon of a charge-coupled device (CCD). Nuclear recoils are produced by low-energy neutrons ($<$24 keV) from a…

Accurate reconstruction of recoil-electron directions is critical for enhancing the point-spread function of electron-tracking Compton cameras (ETCCs) in gamma-ray imaging. Although full three-dimensional (3D) readout systems achieve…

Accurate classification of fine-grained images remains a challenge in backbones based on convolutional operations or self-attention mechanisms. This study proposes novel dual-current neural networks (DCNN), which combine the advantages of…

We present new results with a prototype detector that is being developed by the DMTPC collaboration for the measurement of the direction tag (head-tail) of dark matter wind. We use neutrons from a Cf-252 source to create low-momentum…

Due to cellular heterogeneity, cell nuclei classification, segmentation, and detection from pathological images are challenging tasks. In the last few years, Deep Convolutional Neural Networks (DCNN) approaches have been shown…

Directional detection can provide unambiguous observation of Dark Matter interactions even in presence of insidious backgrounds. The DM-TPC collaboration is developing a detector with the goal of measuring the direction and sense of nuclear…

This note presents revised detector parameters applicable to data from the First Science Run of the ZEPLIN-III dark matter experiment; these datasets were acquired in 2008 and reanalised in 2011. This run demonstrated electron recoil…

In this paper, we propose a robust and parsimonious approach using Deep Convolutional Neural Network (DCNN) to recognize and interpret interior space. DCNN has achieved incredible success in object and scene recognition. In this study we…

We show that the energy threshold for nuclear recoils in the XENON10 dark matter search data can be lowered to ~1 keV, by using only the ionization signal. In other words, we make no requirement that a valid event contain a primary…