Related papers: Deep Learning for direct Dark Matter search with n…
In this study, we have developed a new sub-MeV neutron detector that has a high position resolution, energy resolution, directional sensitivity, and low background. The detector is based on a super-fine-grained nuclear emulsion, called the…
Object detection systems based on the deep convolutional neural network (CNN) have recently made ground- breaking advances on several object detection benchmarks. While the features learned by these high-capacity neural networks are…
Generative deep learning methods built upon Convolutional Neural Networks (CNNs) provide a great tool for predicting non-linear structure in cosmology. In this work we predict high resolution dark matter halos from large scale, low…
Directional detection is the only strategy for the unambiguous identification of galactic Dark Matter (DM) even in the presence of an irreducible background such as beyond the neutrino floor. This approach requires measuring the direction…
In this article, we use artificial intelligence algorithms to show how to enhance the resolution of the elementary particle track fitting in inhomogeneous dense detectors, such as plastic scintillators. We use deep learning to replace more…
Directional detection is a promising direct Dark Matter (DM) search strategy. The angular distribution of the nuclear recoil tracks from WIMP events should present an anisotropy in galactic coordinates. This strategy requires both a…
Recoil imaging entails the detection of spatially resolved ionization tracks generated by particle interactions. This is a highly sought-after capability in many classes of detector, with broad applications across particle and astroparticle…
We propose a local modelling approach using deep convolutional neural networks (CNNs) for fine-grained image classification. Recently, deep CNNs trained from large datasets have considerably improved the performance of object recognition.…
We propose a new wide-field imaging method that exploits the Localized Surface Plasmon Resonance phenomenon to produce super-resolution images with an optical microscope equipped with a custom design polarization analyzer module. In this…
Based on the DUSTGRAIN-pathfinder suite of simulations, we investigate observational degeneracies between nine models of modified gravity and massive neutrinos. Three types of machine learning techniques are tested for their ability to…
Dark matter signal and its annual modulation of event number are observed by some direct searches in small mass region. However, the regions have been excluded by others. The isospin-violating dark matter is a hopeful candidate to explain…
Directional detection is a promising search strategy to discover galactic Dark Matter. We present a Bayesian analysis framework dedicated to data from upcoming directional detectors. The interest of directional detection as a powerful tool…
We propose a new approach to search for light dark matter (DM), with keV-GeV mass, via inelastic nucleus scattering at large-volume neutrino detectors such as Borexino, DUNE, Super-K, Hyper-K, and JUNO. The approach uses inelastic nuclear…
The reconstruction of charged particle trajectories in tracking detectors is a key problem in the analysis of experimental data for high-energy and nuclear physics. The amount of data in modern experiments is so large that classical…
To have a superior generalization, a deep learning neural network often involves a large size of training sample. With increase of hidden layers in order to increase learning ability, neural network has potential degradation in accuracy.…
Pulmonary nodule detection plays an important role in lung cancer screening with low-dose computed tomography (CT) scans. It remains challenging to build nodule detection deep learning models with good generalization performance due to…
Deep-learning algorithms enable precise image recognition based on high-dimensional hierarchical image features. Here, we report the development and implementation of a deep-learning-based image segmentation algorithm in an autonomous…
Directional detection of Dark Matter is a promising search strategy. However, to perform such detection, a given set of parameters has to be retrieved from the recoiling tracks : direction, sense and position in the detector volume. In…
Colloidoscope is a deep learning pipeline employing a 3D residual Unet architecture, designed to enhance the tracking of dense colloidal suspensions through confocal microscopy. This methodology uses a simulated training dataset that…
Despite their unprecedented performance in various domains, utilization of Deep Neural Networks (DNNs) in safety-critical environments is severely limited in the presence of even small adversarial perturbations. The present work develops a…