Development of a Vector Finder Toolkit for Track Reconstruction in MPD ITS

2021 ◽  
Vol 52 (4) ◽  
pp. 788-792
Author(s):  
D. Zinchenko ◽  
A. Zinchenko ◽  
E. Nikonov
Keyword(s):  
Track Reconstruction

scholarly journals A Compact Muon Tracker for Dynamic Tomography of Density Based on a Thin Time Projection Chamber with Micromegas Readout

Particles ◽  
2021 ◽  
Vol 4 (3) ◽  
pp. 333-342
Author(s):  
Ignacio Lázaro Roche
Keyword(s):  
Time Projection Chamber ◽  
Wide Field ◽  
Track Reconstruction ◽  
Long Term Stability ◽  
Societal Issues ◽  
Primary Signal ◽  
And Performance ◽  
Time Projection

Tomography based on cosmic muon absorption is a rising technique because of its versatility and its consolidation as a geophysics tool over the past decade. It allows us to address major societal issues such as long-term stability of natural and man-made large infrastructures or sustainable underwater management. Traditionally, muon trackers consist of hodoscopes or multilayer detectors. For applications with challenging available volumes or the wide field of view required, a thin time projection chamber (TPC) associated with a Micromegas readout plane can provide a good tradeoff between compactness and performance. This paper details the design of such a TPC aiming at maximizing primary signal and minimizing track reconstruction artifacts. The results of the measurements performed during a case study addressing the aforementioned applications are discussed. The current works lines and perspectives of the project are also presented.

scholarly journals Studying the Potential of Graphcore® IPUs for Applications in Particle Physics

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Samuel Maddrell-Mander ◽  
Lakshan Ram Madhan Mohan ◽  
Alexander Marshall ◽  
Daniel O’Hanlon ◽  
Konstantinos Petridis ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Kalman Filter ◽  
Particle Physics ◽  
Processing Unit ◽  
Network Architectures ◽  
Track Reconstruction ◽  
Event Simulation ◽  
Scattering Correction ◽  
New Type

AbstractThis paper presents the first study of Graphcore’s Intelligence Processing Unit (IPU) in the context of particle physics applications. The IPU is a new type of processor optimised for machine learning. Comparisons are made for neural-network-based event simulation, multiple-scattering correction, and flavour tagging, implemented on IPUs, GPUs and CPUs, using a variety of neural network architectures and hyperparameters. Additionally, a Kálmán filter for track reconstruction is implemented on IPUs and GPUs. The results indicate that IPUs hold considerable promise in addressing the rapidly increasing compute needs in particle physics.

scholarly journals First Demonstration of a Pixelated Charge Readout for Single-Phase Liquid Argon Time Projection Chambers

Instruments ◽  
2020 ◽  
Vol 4 (1) ◽  
pp. 9 ◽  
Author(s):  
Jonathan Asaadi ◽  
Martin Auger ◽  
Antonio Ereditato ◽  
Damian Goeldi ◽  
Umut Kose ◽  
...  
Keyword(s):  
Single Phase ◽  
Signal To Noise Ratio ◽  
Liquid Argon ◽  
Track Reconstruction ◽  
3D Tracking ◽  
3D Space ◽  
Time Projection Chambers ◽  
Event Reconstruction ◽  
Phase Liquid ◽  
Time Projection

Traditional charge readout technologies of single-phase Liquid Argon Time projection Chambers (LArTPCs) based on projective wire readout introduce intrinsic ambiguities in event reconstruction. Combined with the slow response inherent in LArTPC detectors, reconstruction ambiguities have limited their performance, until now. Here, we present a proof of principle of a pixelated charge readout that enables the full 3D tracking capabilities of LArTPCs. We characterize the signal-to-noise ratio of charge readout chain to be about 14, and demonstrate track reconstruction on 3D space points produced by the pixel readout. This pixelated charge readout makes LArTPCs a viable option for high-multiplicity environments.

3D track reconstruction of neutron-induced recoil protons in fluorescent nuclear track detectors (FNTDs)

2020 ◽  
Vol 137 ◽  
pp. 106438
Author(s):  
A. Stabilini ◽  
M.S. Akselrod ◽  
V. Fomenko ◽  
S. Greilich ◽  
J. Harrison ◽  
...  
Keyword(s):  
Track Reconstruction ◽  
Nuclear Track Detectors

scholarly journals Gpu-Based Online Track Reconstruction for the Alice Tpc in Run 3 With Continuous Read-Out

2019 ◽  
Vol 214 ◽  
pp. 01050 ◽  
Author(s):  
David Rohr ◽  
Sergey Gorbunov ◽  
Schmidt Ole Marten ◽  
Ruben Shahoyan
Keyword(s):  
Data Compression ◽  
A Priori ◽  
Hardware Accelerators ◽  
Phase Identification ◽  
Track Reconstruction ◽  
Online Processing ◽  
The Status ◽  
Reconstruction Strategy ◽  
Time Projection ◽  
Online Reconstruction

In LHC Run 3, ALICE will increase the data taking rate significantly to 50 kHz continuous read-out of minimum bias Pb—Pb collisions. The reconstruction strategy of the online-offline computing upgrade foresees a first synchronous online reconstruction stage during data taking enabling detector calibration and data compression, and a posterior calibrated asynchronous reconstruction stage. Many new challenges arise, among them continuous TPC read-out, more overlapping collisions, no a priori knowledge of the primary vertex and of location-dependent calibration in the synchronous phase, identification of low-momentum looping tracks, and sophisticated raw data compression. The tracking algorithm for the Time Projection Chamber (TPC) will be based on a Cellular Automaton and the Kalman filter. The reconstruction shall run online, processing 50 times more collisions per second than today, while yielding results comparable to current offline reconstruction. Our TPC track finding leverages the potential of hardware accelerators via the OpenCL and CUDA APIs in a shared source code for CPUs and GPUs for both reconstruction stages. We give an overview of the status of Run 3 tracking including performance on processors and GPUs and achieved compression ratios.

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