Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userA neural network classifier for electron identification on the DAMPE experiment
165
0
0.0
(
0
)
Added by
David Fran\\c{c}ois Droz
Publication date
2021
fields
Physics
and research's language is
English
Ask ChatGPT about the research
No Arabic abstract
The Dark Matter Particle Explorer (DAMPE) is a space-borne particle detector and cosmic ray observatory in operation since 2015, designed to probe electrons and gamma rays from a few GeV to 10 TeV energy, as well as cosmic protons and nuclei up to 100 TeV. Among the main scientific objectives is the precise measurement of the cosmic electron+positron flux, which due to the very large proton background in orbit requires a powerful particle identification method. In the past decade, the field of machine learning has provided us the needed tools. This paper presents a neural network based approach to cosmic electron identification and proton rejection and showcases its performances based on simulated Monte Carlo data. The neural network reaches significantly lower background than the classical, cut-based method for the same detection efficiency, especially at highest energies. A good matching between simulations and real data completes the picture.
rate research
Read More
A software system has been developed for the DArk Matter Particle Explorer (DAMPE) mission, a satellite-based experiment. The DAMPE software is mainly written in C++ and steered using Python script. This article presents an overview of the DAMPE offline software, including the major architecture design and specific implementation for simulation, calibration and reconstruction. The whole system has been successfully applied to DAMPE data analysis, based on which some results from simulation and beam test experiments are obtained and presented.
Common variable star classifiers are built only with the goal of producing the correct class labels, leaving much of the multi-task capability of deep neural networks unexplored. We present a periodic light curve classifier that combines a recurrent neural network autoencoder for unsupervised feature extraction and a dual-purpose estimation network for supervised classification and novelty detection. The estimation network optimizes a Gaussian mixture model in the reduced-dimension feature space, where each Gaussian component corresponds to a variable class. An estimation network with a basic structure of a single hidden layer attains a cross-validation classification accuracy of ~99%, on par with the conventional workhorses, random forest classifiers. With the addition of photometric features, the network is capable of detecting previously unseen types of variability with precision 0.90, recall 0.96, and an F1 score of 0.93. The simultaneous training of the autoencoder and estimation network is found to be mutually beneficial, resulting in faster autoencoder convergence, and superior classification and novelty detection performance. The estimation network also delivers adequate results even when optimized with pre-trained autoencoder features, suggesting that it can readily extend existing classifiers to provide added novelty detection capabilities.
An antenna array devoted to the autonomous radio-detection of high energy cosmic rays is being deployed on the site of the 21 cm array radio telescope in XinJiang, China. Thanks in particular to the very good electromagnetic environment of this remote experimental site, self-triggering on extensive air showers induced by cosmic rays has been achieved with a small scale prototype of the foreseen antenna array. We give here a detailed description of the detector and present the first detection of extensive air showers with this prototype.
The Radar Echo Telescope for Cosmic Rays (RET-CR) is a recently initiated experiment designed to detect the englacial cascade of a cosmic-ray initiated air shower via in-ice radar, toward the goal of a full-scale, next-generation experiment to detect ultra high energy neutrinos in polar ice. For cosmic rays with a primary energy greater than 10 PeV, roughly 10% of an air-showers energy reaches the surface of a high elevation ice-sheet ($gtrsim$2 km) concentrated into a radius of roughly 10 cm. This penetrating shower core creates an in-ice cascade many orders of magnitude more dense than the preceding in-air cascade. This dense cascade can be detected via the radar echo technique, where transmitted radio is reflected from the ionization deposit left in the wake of the cascade. RET-CR will test the radar echo method in nature, with the in-ice cascade of a cosmic-ray initiated air-shower serving as a test beam. We present the projected event rate and sensitivity based upon a three part simulation using CORSIKA, GEANT4, and RadioScatter. RET-CR expects $sim$1 radar echo event per day.
We utilize techniques from deep learning to identify signatures of stellar feedback in simulated molecular clouds. Specifically, we implement a deep neural network with an architecture similar to U-Net and apply it to the problem of identifying wind-driven shells and bubbles using data from magneto-hydrodynamic simulations of turbulent molecular clouds with embedded stellar sources. The network is applied to two tasks, dense regression and segmentation, on two varieties of data, simulated density and synthetic 12 CO observations. Our Convolutional Approach for Shell Identification (CASI) is able to obtain a true positive rate greater than 90%, while maintaining a false positive rate of 1%, on two segmentation tasks and also performs well on related regression tasks. The source code for CASI is available on GitLab.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
