DNN Based Prototype of the Track Reconstruction Algorithm for the MUonE Experiment

2021 ◽  
pp. 202-205
Author(s):  
Miłosz Zdybał ◽  
Marcin Kucharczyk ◽  
Marcin Wolter
Keyword(s):  
Reconstruction Algorithm ◽  
Track Reconstruction

Search by triplet: An efficient local track reconstruction algorithm for parallel architectures

2021 ◽  
pp. 101422
Author(s):  
Daniel Hugo Cámpora Pérez ◽  
Niko Neufeld ◽  
Agustín Riscos Núñez
Keyword(s):  
Reconstruction Algorithm ◽  
Parallel Architectures ◽  
Track Reconstruction

scholarly journals A fast and efficient SIMD track reconstruction algorithm for the LHCb upgrade 1 VELO-PIX detector

2020 ◽  
Vol 15 (06) ◽  
pp. P06018-P06018
Author(s):  
A. Hennequin ◽  
B. Couturier ◽  
V.V. Gligorov ◽  
S. Ponce ◽  
R. Quagliani ◽  
...  
Keyword(s):  
Reconstruction Algorithm ◽  
Track Reconstruction

The TOTEM T2 detector at LHC: Track reconstruction algorithm and preliminary measurements

2011 ◽  
Author(s):  
M. Berretti ◽  
V. Avati ◽  
E. Bossini ◽  
P. Brogi ◽  
E. Brucken ◽  
...  
Keyword(s):  
Reconstruction Algorithm ◽  
Track Reconstruction

scholarly journals Analysis of the experimental data measured by BM@N drift chambers

2019 ◽  
Vol 204 ◽  
pp. 07001
Author(s):  
Ján Fedorišin
Keyword(s):  
Experimental Data ◽  
Deuteron Beam ◽  
Reconstruction Algorithm ◽  
Current Status ◽  
Combinatorial Background ◽  
Track Reconstruction ◽  
Preliminary Results ◽  
Drift Chambers

The current status of the analysis of the experimental data measured by the drift chambers in the BM@N experiment is presented. Apart from the previously published analysis based on the deuteron beam data, this study shows preliminary results for the carbon data at energy 4:5 GeV per nucleon. Some practical issues, such as evaluation and possible removal of the combinatorial background, as well as reliability of the employed track reconstruction algorithm are discussed as well.

scholarly journals Precise 3D Track Reconstruction Algorithm for the ICARUS T600 Liquid Argon Time Projection Chamber Detector

2013 ◽  
Vol 2013 ◽  
pp. 1-16 ◽  
Author(s):  
M. Antonello ◽  
B. Baibussinov ◽  
P. Benetti ◽  
E. Calligarich ◽  
N. Canci ◽  
...  
Keyword(s):  
Time Projection Chamber ◽  
Reconstruction Algorithm ◽  
Liquid Argon ◽  
Neutrino Beam ◽  
Practical Application ◽  
Track Reconstruction ◽  
Imaging Capability ◽  
Event Reconstruction ◽  
Particle Imaging ◽  
Time Projection

Liquid Argon Time Projection Chamber (LAr TPC) detectors offer charged particle imaging capability with remarkable spatial resolution. Precise event reconstruction procedures are critical in order to fully exploit the potential of this technology. In this paper we present a new, general approach to 3D reconstruction for the LAr TPC with a practical application to the track reconstruction. The efficiency of the method is evaluated on a sample of simulated tracks. We present also the application of the method to the analysis of stopping particle tracks collected during the ICARUS T600 detector operation with the CNGS neutrino beam.

scholarly journals Tau reconstruction at CMS with a focus on high $ p_{T} $ taus

2019 ◽  
Author(s):  
Soham Bhattacharya
Keyword(s):  
Transverse Momentum ◽  
High Efficiency ◽  
Reconstruction Algorithm ◽  
High Transverse Momentum ◽  
Track Reconstruction ◽  
Work In Progress ◽  
Cms Experiment ◽  
Pros And Cons ◽  
And Performance

We present the algorithm and performance of tau reconstruction at the CMS experiment, while highlighting a dedicated reconstruction algorithm that uses calorimeter hits instead of tracks to reconstruct taus with high transverse momentum. Describing the standard Hadrons-Plus-Strips (HPS) algorithm and its dependence on track reconstruction and shower modelling, we present the calorimetric tau (calo-tau) reconstruction that uses minimal track information for high p_{T}pT taus. The pros and cons of these algorithms are discussed along with their performance and potential uses. It is found that the calo-tau algorithm outperforms the HPS algorithm in the high efficiency region. This study is work in progress, and is an attempt to tune the reconstruction for high p_{T}pT taus. The calo-tau algorithm is not yet an official tau reconstruction algorithm for CMS.

scholarly journals Improvements to ATLAS Inner Detector Track reconstruction for LHC Run-3

2021 ◽  
Vol 251 ◽  
pp. 03048
Author(s):  
Zachary Michael Schillaci
Keyword(s):  
High Activity ◽  
Reconstruction Algorithm ◽  
Track Reconstruction ◽  
Proton Proton ◽  
Previous Version ◽  
Pile Up ◽  
Reconstruction Software

This document summarises the main changes to the ATLAS experiment’s Inner Detector Track reconstruction software chain in preparation of LHC Run 3 (2022-2024). The work was carried out to ensure that the expected high-activity collisions with on average 50 simultaneous proton-proton interactions per bunch crossing (pile-up) can be reconstructed promptly using the available computing resources. Performance figures in terms of CPU consumption for the key components of the reconstruction algorithm chain and their dependence on the pile-up are shown. For the design pile-up value of 60 the updated track reconstruction is a factor of 2 faster than the previous version.

A new track reconstruction algorithm for the Mu3e experiment based on a fast multiple scattering fit

2014 ◽  
Vol 9 (12) ◽  
pp. C12012-C12012
Author(s):  
A. Kozlinskiy ◽  
A. Schöning ◽  
M. Kiehn ◽  
N. Berger ◽  
S. Schenk
Keyword(s):  
Multiple Scattering ◽  
Reconstruction Algorithm ◽  
Track Reconstruction

scholarly journals Towards a realistic track reconstruction algorithm based on graph neural networks for the HL-LHC

2021 ◽  
Vol 251 ◽  
pp. 03047
Author(s):  
Catherine Biscarat ◽  
Sylvain Caillou ◽  
Charline Rougier ◽  
Jan Stark ◽  
Jad Zahreddine
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Reconstruction Algorithm ◽  
Machine Learning Techniques ◽  
Track Reconstruction ◽  
Learning Techniques ◽  
Common Framework ◽  
Graph Neural Networks ◽  
Track Pattern ◽  
New Algorithms

The physics reach of the HL-LHC will be limited by how efficiently the experiments can use the available computing resources, i.e. affordable software and computing are essential. The development of novel methods for charged particle reconstruction at the HL-LHC incorporating machine learning techniques or based entirely on machine learning is a vibrant area of research. In the past two years, algorithms for track pattern recognition based on graph neural networks (GNNs) have emerged as a particularly promising approach. Previous work mainly aimed at establishing proof of principle. In the present document we describe new algorithms that can handle complex realistic detectors. The new algorithms are implemented in ACTS, a common framework for tracking software. This work aims at implementing a realistic GNN-based algorithm that can be deployed in an HL-LHC experiment.

Sign in / Sign up

Export Citation Format

Share Document