Related papers: New Particle Identification Approach with Convolut…
GAPS is an international balloon-borne project that contributes to solving the dark-matter mystery through a highly sensitive survey of cosmic-ray antiparticles, especially undiscovered antideuterons. To achieve a sufficient sensitivity to…
The General AntiParticle Spectrometer (GAPS) is an Antarctic balloon-borne detector designed to measure low-energy cosmic antinuclei (< 0.25 GeV/n), with a specific focus on antideuterons, as a distinctive signal from dark matter…
The General Antiparticle Spectrometer (GAPS) is an Antarctic stratospheric balloon mission designed to provide unmatched sensitivity to low-energy (<0.25 GeV/n) cosmic-ray antiprotons, antideuterons, and antihelium nuclei as signatures of…
The General Antiparticle Spectrometer (GAPS) will carry out a sensitive dark matter search by measuring low-energy ($\mathrm{E} < 0.25 \mathrm{GeV/nucleon}$) cosmic ray antinuclei. The primary targets are low-energy antideuterons produced…
The General AntiParticle Spectrometer (GAPS) is a balloon-borne instrument designed to detect cosmic-ray antimatter using the novel exotic atom technique, obviating the strong magnetic fields required by experiments like AMS, PAMELA, or…
We propose a novel antiparticle detector. The gaseous antiparticle spectrometer (GAPS) effects particle identification through the characteristic X-rays emitted by antiparticles when they form exotic atoms in gases. GAPS obtains…
The General Antiparticle Spectrometer (GAPS) is an upcoming balloon mission to measure low-energy cosmic-ray antinuclei during at least three ~35-day Antarctic flights. With its large geometric acceptance and novel exotic atom-based…
The General Antiparticle Spectrometer (GAPS) is an Antarctic balloon experiment designed for low-energy (0.1$-$0.3 GeV/$n$) cosmic antinuclei as signatures of dark matter annihilation or decay. GAPS is optimized to detect low-energy…
A novel method images to estimate cosmological parameters based on images is presented. In this paper, we demonstrate the use of a convolutional neural network (CNN) for constraining the mass of dark matter particle. For this purpose, we…
The GAPS experiment is foreseen to carry out a dark matter search using a novel detection approach to detect low-energy cosmic-ray antideuterons. The theoretically predicted antideuteron flux resulting from secondary interactions of primary…
There has been considerable recent activity applying deep convolutional neural nets (CNNs) to data from particle physics experiments. Current approaches on ATLAS/CMS have largely focussed on a subset of the calorimeter, and for identifying…
We provide a bridge between generative modeling in the Machine Learning community and simulated physical processes in High Energy Particle Physics by applying a novel Generative Adversarial Network (GAN) architecture to the production of…
The General Antiparticle Spectrometer (GAPS) is an Antarctic balloon-borne mission to indirectly search for dark matter through sensitive observation of cosmic antiparticles. The first flight is planned for late 2021. GAPS is the first…
Pattern recognition problems in high energy physics are notably different from traditional machine learning applications in computer vision. Reconstruction algorithms identify and measure the kinematic properties of particles produced in…
The General Antiparticle Spectrometer (GAPS) is designed to carry out indirect dark matter search by measuring low-energy cosmic-ray antiparticles. Below a few GeVs the flux of antiparticles produced by cosmic-ray collisions with the…
The General AntiParticle Spectrometer (GAPS) is a novel approach for the indirect dark matter search that exploits cosmic antideuterons. GAPS utilizes a distinctive detection method using atomic X-rays and charged particles from the exotic…
Convolutional neural networks (CNNs) have been widely applied in the computer vision community to solve complex problems in image recognition and analysis. We describe an application of the CNN technology to the problem of identifying…
Accurate prediction of physical properties is critical for discovering and designing novel materials. Machine learning technologies have attracted significant attention in the materials science community for their potential for large-scale…
Experiments aiming to directly detect dark matter (DM) particles have yet to make robust detections, thus underscoring the need for complementary approaches such as searches for new particles at colliders, and indirect DM searches in…
The General Antiparticle Spectrometer (GAPS) is a novel approach for indirect dark matter searches that exploits cosmic antiparticles, especially antideuterons. The GAPS antideuteron measurement utilizes distinctive detection methods using…