ترغب بنشر مسار تعليمي؟ اضغط هنا

Graph Neural Networks for Particle Reconstruction in High Energy Physics detectors

407   0   0.0 ( 0 )
 نشر من قبل Paolo Calafiura
 تاريخ النشر 2020
  مجال البحث فيزياء
والبحث باللغة English
 تأليف Xiangyang Ju




اسأل ChatGPT حول البحث

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 high energy collisions and recorded with complex detector systems. Two critical applications are the reconstruction of charged particle trajectories in tracking detectors and the reconstruction of particle showers in calorimeters. These two problems have unique challenges and characteristics, but both have high dimensionality, high degree of sparsity, and complex geometric layouts. Graph Neural Networks (GNNs) are a relatively new class of deep learning architectures which can deal with such data effectively, allowing scientists to incorporate domain knowledge in a graph structure and learn powerful representations leveraging that structure to identify patterns of interest. In this work we demonstrate the applicability of GNNs to these two diverse particle reconstruction problems.



قيم البحث

اقرأ أيضاً

We studied the performance of the Convolutional Neural Network (CNN) for energy regression in a finely 3D-segmented calorimeter simulated by GEANT4. A CNN trained solely on a pure sample of pions achieved substantial improvement in the energy resolut ion for both single pions and jets over the conventional approaches. It maintained good performance for electron and photon reconstruction. We also used the Graph Neural Network (GNN) with edge convolution to assess the importance of timing information in the shower development for improved energy reconstruction. In this paper, we present the comparison of several reconstruction techniques: a simple energy sum, a dual-readout analog, a CNN, and a GNN with timing information.
We apply deep neural networks (DNN) to data from the EXO-200 experiment. In the studied cases, the DNN is able to reconstruct the relevant parameters - total energy and position - directly from raw digitized waveforms, with minimal exceptions. For th e first time, the developed algorithms are evaluated on real detector calibration data. The accuracy of reconstruction either reaches or exceeds what was achieved by the conventional approaches developed by EXO-200 over the course of the experiment. Most existing DNN approaches to event reconstruction and classification in particle physics are trained on Monte Carlo simulated events. Such algorithms are inherently limited by the accuracy of the simulation. We describe a unique approach that, in an experiment such as EXO-200, allows to successfully perform certain reconstruction and analysis tasks by training the network on waveforms from experimental data, either reducing or eliminating the reliance on the Monte Carlo.
133 - E. Aprile , T. Doke 2009
This article reviews the progress made over the last 20 years in the development and applications of liquid xenon detectors in particle physics, astrophysics and medical imaging experiments. We begin with a summary of the fundamental properties of li quid xenon as radiation detection medium, in light of the most current theoretical and experimental information. After a brief introduction of the different type of liquid xenon detectors, we continue with a review of past, current and future experiments using liquid xenon to search for rare processes and to image radiation in space and in medicine. We will introduce each application with a brief survey of the underlying scientific motivation and experimental requirements, before reviewing the basic characteristics and expected performance of each experiment. Within this decade it appears likely that large volume liquid xenon detectors operated in different modes will contribute to answering some of the most fundamental questions in particle physics, astrophysics and cosmology, fulfilling the most demanding detection challenges. From experiments like MEG, currently the largest liquid xenon scintillation detector in operation, dedicated to the rare mu -> e + gamma decay, to the future XMASS which also exploits only liquid xenon scintillation to address an ambitious program of rare event searches, to the class of time projection chambers like XENON and EXO which exploit both scintillation and ionization of liquid xenon for dark matter and neutrinoless double beta decay, respectively, we anticipate unrivaled performance and important contributions to physics in the next few years.
Modern gas detectors for detection of particles require F-based gases for optimal performance. Recent regulations demand the use of environmentally unfriendly F-based gases to be limited or banned. This review studies properties of potential eco-friendly gas candidate replacements.
The liquid argon ionization current in a sampling calorimeter cell can be analyzed to determine the energy of detected particles. In practice, experimental artifacts such as pileup and electronic noise make the inference of energy from current a diff icult process. The beam intensity of the Large Hadron Collider will be significantly increased during the Phase-II long shut down of 2024-2026. Signal processing techniques that are used to extract the energy of detected particles in the ATLAS detector will suffer a significant loss in performance under these conditions. This paper compares the presently used optimal filter technique to convolutional neural networks for energy reconstruction in the ATLAS liquid argon hadronic end cap calorimeter. In particular, it is shown that convolutional neural networks trained with an appropriately tuned and novel loss function are able to outperform the optimal filter technique.
التعليقات
جاري جلب التعليقات جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
mircosoft-partner

هل ترغب بارسال اشعارات عن اخر التحديثات في شمرا-اكاديميا