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

Direct detection of light dark matter (DM), below the GeV scale, through electron recoil can be efficient if DM has a velocity well above the virial value of $v\sim 10^{-3}$. We point out that if there is a long range attractive force…

Dual-phase xenon time projection chamber (TPC) detectors offer heightened sensitivities for dark matter detection across a spectrum of particle masses. To broaden their capability to low-mass dark matter interactions, we investigated the…

A deep learning approach to blind denoising of images without complete knowledge of the noise statistics is considered. We propose DN-ResNet, which is a deep convolutional neural network (CNN) consisting of several residual blocks…

In this research work, we have demonstrated the application of Mask-RCNN (Regional Convolutional Neural Network), a deep-learning algorithm for computer vision and specifically object detection, to semiconductor defect inspection domain.…

In recent years, deep learning techniques have been introduced into the field of trajectory optimization to improve convergence and speed. Training such models requires large trajectory datasets. However, the convergence of low thrust (LT)…

We present a three-dimensional graph convolutional network (3DGCN), which predicts molecular properties and biochemical activities, based on 3D molecular graph. In the 3DGCN, graph convolution is unified with learning operations on the…

Detecting the topology and direction of low-energy nuclear and electronic recoils is broadly desirable in nuclear and particle physics, with applications in coherent elastic neutrino-nucleus scattering, astrophysical neutrino measurements,…

This paper presents a wide-area event classification in transmission power grids. The deep neural network (DNN) based classifier is developed based on the availability of data from time-synchronized phasor measurement units (PMUs). The…

The aim of the CYGNO project is the construction and operation of a 1~m$^3$ gas TPC for directional dark matter searches and coherent neutrino scattering measurements, as a prototype toward the 100-1000~m$^3$ (0.15-1.5 tons) CYGNUS network…

Directional detection is the dedicated strategy to demonstrate that DM-like signals measured by direct detectors are indeed produced by DM particles from the galactic halo. The experimental challenge of measuring the direction of DM-induced…

Existing computer vision and object detection methods strongly rely on neural networks and deep learning. This active research area is used for applications such as autonomous driving, aerial photography, protection, and monitoring.…

Within the field of instance segmentation, most of the state-of-the-art deep learning networks rely nowadays on cascade architectures, where multiple object detectors are trained sequentially, re-sampling the ground truth at each step. This…

Measurement of the direction of the elastic nuclear recoil track and ionization charge distribution along it, gives unique possibility for unambiguous detection of the dark matter WIMP particle. Within current radiation detection…

A double-phase argon Time Projection Chamber (TPC), with an active mass of 185 g, has been designed and constructed for the Recoil Directionality (ReD) experiment. The aim of the ReD project is to investigate the directional sensitivity of…

Deep convolutional neural networks (DCNNs) are an influential tool for solving various problems in the machine learning and computer vision fields. In this paper, we introduce a new deep learning model called an Inception- Recurrent…

We introduce a model-based image reconstruction framework with a convolution neural network (CNN) based regularization prior. The proposed formulation provides a systematic approach for deriving deep architectures for inverse problems with…

MIMAC (MIcro-TPC MAtrix of Chambers) is a directional WIMP Dark Matter detector project. Direct dark matter experiments need a high level of electron/recoil discrimination to search for nuclear recoils produced by WIMP-nucleus elastic…

We seek to remove foreground contaminants from 21cm intensity mapping observations. We demonstrate that a deep convolutional neural network (CNN) with a UNet architecture and three-dimensional convolutions, trained on simulated…

Deep Neural Networks (DNN) have been successfully used to perform classification and regression tasks, particularly in computer vision based applications. Recently, owing to the widespread deployment of Internet of Things (IoT), we identify…

Deep learning networks have shown promising results in fast magnetic resonance imaging (MRI) reconstruction. In our work, we develop deep networks to further improve the quantitative and the perceptual quality of reconstruction. To begin…