scholarly journals Towards a computer vision particle flow

2021 ◽  
Vol 81 (2) ◽  
Author(s):  
Francesco Armando Di Bello ◽  
Sanmay Ganguly ◽  
Eilam Gross ◽  
Marumi Kado ◽  
Michael Pitt ◽  
...  
Keyword(s):  
Computer Vision ◽  
High Energy Physics ◽  
Charged Particles ◽  
Super Resolution ◽  
High Energy ◽  
Particle Flow ◽  
Fundamental Aspect ◽  
Detector Acceptance ◽  
Charged Particle Tracking ◽  
Tracking Devices

AbstractIn High Energy Physics experiments Particle Flow (PFlow) algorithms are designed to provide an optimal reconstruction of the nature and kinematic properties of the particles produced within the detector acceptance during collisions. At the heart of PFlow algorithms is the ability to distinguish the calorimeter energy deposits of neutral particles from those of charged particles, using the complementary measurements of charged particle tracking devices, to provide a superior measurement of the particle content and kinematics. In this paper, a computer vision approach to this fundamental aspect of PFlow algorithms, based on calorimeter images, is proposed. A comparative study of the state of the art deep learning techniques is performed. A significantly improved reconstruction of the neutral particle calorimeter energy deposits is obtained in a context of large overlaps with the deposits from charged particles. Calorimeter images with augmented finer granularity are also obtained using super-resolution techniques.

Modeling charged-particle multiplicity distributions at LHC

2020 ◽  
Vol 35 (36) ◽  
pp. 2050302
Author(s):  
Amr Radi
Keyword(s):  
High Energy Physics ◽  
Hadron Collider ◽  
Center Of Mass ◽  
Charged Particles ◽  
High Energy ◽  
Particle Multiplicity ◽  
Charged Particle Multiplicity ◽  
Proton Proton ◽  
Center Of Mass Energy ◽  
8 Tev

With many applications in high-energy physics, Deep Learning or Deep Neural Network (DNN) has become noticeable and practical in recent years. In this article, a new technique is presented for modeling the charged particles multiplicity distribution [Formula: see text] of Proton-Proton [Formula: see text] collisions using an efficient DNN model. The charged particles multiplicity n, the total center of mass energy [Formula: see text], and the pseudorapidity [Formula: see text] used as input in DNN model and the desired output is [Formula: see text]. DNN was trained to build a function, which studies the relationship between [Formula: see text]. The DNN model showed a high degree of consistency in matching the data distributions. The DNN model is used to predict with [Formula: see text] not included in the training set. The expected [Formula: see text] had effectively merged the experimental data and the values expected indicate a strong agreement with Large Hadron Collider (LHC) for ATLAS measurement at [Formula: see text], 7 and 8 TeV.

scholarly journals Track Finding with Deep Neural Networks

2019 ◽  
Vol 20 (4) ◽  
Author(s):  
Marcin Kucharczyk ◽  
Marcin Wolter
Keyword(s):  
Deep Neural Networks ◽  
High Energy Physics ◽  
Charged Particles ◽  
High Energy ◽  
Two Dimensional ◽  
Dimensional Model ◽  
Data Dependencies ◽  
Speed Up ◽  
Physics Experiments ◽  
Energy Physics

High Energy Physics experiments require fast and efficient methods toreconstruct the tracks of charged particles. Commonly used algorithms aresequential and the CPU required increases rapidly with a number of tracks.Neural networks can speed up the process due to their capability to modelcomplex non-linear data dependencies and finding all tracks in parallel.In this paper we describe the application of the Deep Neural Networkto the reconstruction of straight tracks in a toy two-dimensional model. It isplanned to apply this method to the experimental data taken by the MUonEexperiment at CERN.

GRAPHICAL CONCEPTS FOR THE REPRESENTATION OF EVENTS IN HIGH ENERGY PHYSICS

1990 ◽  
Vol 01 (01) ◽  
pp. 147-163 ◽  
Author(s):  
H. DREVERMANN ◽  
C. GRAB
Keyword(s):  
Magnetic Field ◽  
High Energy Physics ◽  
Three Dimensional ◽  
Charged Particles ◽  
Homogeneous Magnetic Field ◽  
High Energy ◽  
Track Geometry ◽  
Aleph Detector ◽  
Complicated Event ◽  
Energy Physics

Different methods to graphically represent points and tracks of events, measured with the ALEPH-detector at LEP, are discussed. Special emphasis is put on projections, that are adapted to the cylindrical geometry of the detector, to the track geometry of charged particles moving in a homogeneous magnetic field and to the event topologies, encountered in Z0 physics. A new concept, the so-called "V-plot", is introduced, which incorporates the full three-dimensional information of spatial points in a single picture. It is ideally suited for the study of more complicated event topologies, such as e.g. decays of particles within jets, and of the correlation between tracks and calorimeter clusters. In addition, we propose ways of combining histograms and projections to incorporate the tracking and calorimetric information into a single picture. We describe methods of employing colour schemes to facilitate recognition of correlations between hits, tracks and/or subdetectors in different representations.

Graphical Concepts for the Representation of Events in High Energy Physics

1991 ◽  
Vol 02 (01) ◽  
pp. 328-330
Author(s):  
H. DREVERMANN ◽  
C. GRAB ◽  
B.S. NILSSON ◽  
R.K. VOGL
Keyword(s):  
Magnetic Field ◽  
High Energy Physics ◽  
Three Dimensional ◽  
Charged Particles ◽  
Homogeneous Magnetic Field ◽  
High Energy ◽  
Track Geometry ◽  
Aleph Detector ◽  
Complicated Event ◽  
Energy Physics

Different methods to graphically represent points and tracks of events, measured with the ALEPH-detector at LEP, are discussed. Special emphasis is put on projections, that are adapted to the cylindrical geometry of the detector, to the track geometry of charged particles moving in a homogeneous magnetic field and to the specific event topologies, encountered in Z0 physics. A new concept, the so-called “V-plot”, is introduced, which incorporates the full three dimensional information of spatial points in a single picture. It is ideally suited for the study of more complicated event topologies, such as e.g. decays of particles within jets, and of the correlation between information from tracking and calorimetric devices. In addition, we propose ways of combining histograms and projections in a single picture. We describe methods of employing colour schemes to facilitate recognition of correlations between hits, tracks and/or subdetectors in different representations.

DETECTOR R&D

2004 ◽  
Vol 19 (08) ◽  
pp. 1216-1228
Author(s):  
T. BEHNKE
Keyword(s):  
High Energy Physics ◽  
Linear Collider ◽  
High Energy ◽  
The State ◽  
Particle Flow ◽  
Development Projects ◽  
Precision Data ◽  
Novel Approach ◽  
Radiation Hard ◽  
Energy Physics

The next big project in high energy physics should be a high energy e+e- linear collider, operating at energies up to around 1 TeV. A vigorous R&D program has started to prepare the grounds for a detector at such a machine. The amounts of precision data expected at this machine make a novel approach to the reconstruction of events necessary; the particle flow ansatz. This in turn influences significantly the design of a detector for such an experiment. Apart from work ongoing for the linear collider detector, preparations are under way for an update of the LHC. This requires extremely radiation hard detectors. In this paper the state of the different detector development projects is reviewed.

scholarly journals ML Track Fitting in Nuclear Physics

2020 ◽  
Vol 245 ◽  
pp. 06015
Author(s):  
Thomas Britton ◽  
David Lawrence ◽  
Gagik Gavalian
Keyword(s):  
Charged Particle ◽  
Particle Tracking ◽  
Nuclear Physics ◽  
High Energy Physics ◽  
High Energy ◽  
High Data ◽  
The Status ◽  
Data Volume ◽  
Charged Particle Tracking ◽  
Fitting In

Charged particle tracking represents the largest consumer of CPU resources in high data volume Nuclear Physics (NP) experiments. An effort is underway to develop machine learning (ML) networks that will reduce the resources required for charged particle tracking. Tracking in NP experiments represent some unique challenges compared to high energy physics (HEP). In particular, track finding typically represents only a small fraction of the overall tracking problem in NP. This presentation will outline the differences and similarities between NP and HEP charged particle tracking and areas where ML learning may provide a benefit. The status of the specific effort taking place at Jefferson Lab will also be shown.

Use of Plastic Optical Fibers for Charged Particle Tracking in High Energy Physics

1994 ◽  
Vol 348 ◽  
Author(s):  
Mitchell R. Wayne
Keyword(s):  
Optical Fibers ◽  
High Energy Physics ◽  
High Energy ◽  
Current Status ◽  
Development Effort ◽  
Detector Performance ◽  
Tracking Detector ◽  
Plastic Optical Fibers ◽  
Charged Particle Tracking ◽  
Energy Physics

ABSTRACTA large tracking detector consisting of scintillating plastic optical fibers has been chosen by the D0 collaboration as a part of a planned upgrade at the Fermilab Tevatron. The tracker will utilize a state of the art photodetector known as the Visible Light Photon Counter. The benefits of fiber tracking in high energy physics will be presented along with recent progress in several key areas, including: optimization of scintillating dyes and light yields, fiber construction, fiber ribbon manufacture and placement, optical transmission and photodetection. The current status of the D0 development effort will be outlined, including results from the characterization of 5000 channels of VLPC. Finally, results from simulations of expected detector performance will be shown and discussed.

scholarly journals DRIFT CHAMBER TRACKING WITH A VLSI NEURAL NETWORK

1992 ◽  
Vol 03 (supp01) ◽  
pp. 285-295
Author(s):  
Clark S. Lindsey ◽  
Bruce Denby ◽  
Herman Haggerty ◽  
Ken Johns
Keyword(s):  
Neural Network ◽  
Real Time ◽  
High Energy Physics ◽  
Charged Particles ◽  
Drift Chamber ◽  
High Energy ◽  
Analog Vlsi ◽  
Test Setup ◽  
Energy Physics

We have tested a commercial analog VLSI neural network chip for finding in real time the intercept and slope of charged particles traversing a drift chamber. Voltages proportional to the drift times were input to the Intel ETANN chip and the outputs were recorded and later compared off line to conventional track fits. We will discuss the chamber and test setup, the chip specifications, and results of recent tests. We’ll briefly discuss possible applications in high energy physics detector triggers.

scholarly journals GRAAL: A novel package to reconstruct data of triple-GEM detectors

2020 ◽  
Vol 245 ◽  
pp. 02021
Author(s):  
Riccardo Farinelli
Keyword(s):  
High Energy Physics ◽  
High Energy ◽  
Reconstruction Method ◽  
Detector Performance ◽  
Electron Multiplier ◽  
Innovative Cluster ◽  
Alignment Algorithms ◽  
Micro Pattern ◽  
Tracking Devices ◽  
Gem Detectors

Micro-Pattern Gas Detectors (MPGDs) are the new frontier among the gas tracking systems. Among them, the triple Gas Electron Multiplier (triple-GEM) detectors are widely used. In particular, cylindrical triple-GEM (CGEM) detectors can be used as inner tracking devices in high energy physics experiments. In this contribution, a new offline software called GRAAL (Gem Reconstruction And Analysis Library) is presented: digitization, reconstruction, alignment algorithms and analysis of the data collected with APV-25 and TIGER ASICs are reported. An innovative cluster reconstruction method based on charge centroid, micro-TPC and their merge is discussed, and the detector performance evaluated experimentally for both planar triple-GEM and CGEM prototypes.

Applications

2014 ◽  
pp. 228-256
Keyword(s):  
Homeland Security ◽  
High Energy Physics ◽  
Charged Particles ◽  
High Energy ◽  
Short Description ◽  
Dark Matter Search ◽  
Time Projection Chambers ◽  
Time Projection ◽  
New Applications ◽  
Energy Physics

In this chapter, some applications of micropattern detectors are described. Their main application is tracking of charged particles in high-energy physics. However, currently there are a lot of research and developments going on, which may open new exciting fields of applications, for example in dark matter search, medical applications, homeland security, etc. The authors start with the traditional applications, which are in high-energy physics and astrophysics. Later, the focus shifts to promising developments oriented towards new applications. These innovative applications include: imaging of charged particles and energetic photons with unprecedented high 2-D spatial resolution (e.g. in mammography), time projection chambers capable operating in a high flux of particles (e.g. ALICE upgraded TPC), and visualization of ultraviolet and visible photons. Finally, a short description of the international collaboration RD51 established at CERN is given in order to promote the development of micropattern detectors and their applications.

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