A Quantum Graph Network Approach to Particle Track Reconstruction

Download

10.5281zenodo4088474.pdf

Date

2020-04-20

Author

Tüysüz, Cenk
Carminati, Federico
Demirköz, Melahat Bilge
Dobos, Daniel
Fracas, Fabio
Novotny, Kristiane
Potamianos, Karolos
Vallecorsa, Sofia
Vlimant, JeanRoch

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

191
views

129
downloads

The unprecedented increase of complexity and scale of data is expected in the necessary computation for tracking detectors of the High Luminosity Large Hadron Collider (HL-LHC) experiments. While currently used Kalman filter based algorithms are reaching their limits in terms of ambiguities from increasing number of simultaneous collisions, occupancy, and scalability (worse than quadratic), a variety of machine learning approaches to particle track reconstruction are explored. It has been demonstrated previously by HEP.TrkX using TrackML datasets, that graph neural networks, processing events as a graph connecting track measurements, are a promising solution and can reduce the combinatorial background to a manageable amount and are scaling to a computationally reasonable size. In previous work, we have shown a first attempt of Quantum Computing to Graph Neural Networks for track reconstruction of particles. We aim to leverage the capability of quantum computing to evaluate a very large number of states simultaneously and thus to effectively search in a large parameter space. As the next step in this paper, we present an improved model with an iterative approach to overcome the low accuracy convergence of the initial oversimplified Tree Tensor Network (TTN) model.

URI

https://hdl.handle.net/11511/70223

DOI

https://doi.org/10.5281/zenodo.4088474

Collections

Department of Physics, Conference / Seminar

Suggestions

OpenMETU
Core

CTD2020: A Quantum Graph Network Approach to Particle Track Reconstruction Tüysüz, Cenk; Demirköz, Melahat Bilge; Dobos, Daniel; Fracas, Fabio; Carminati, Federico; Vlimant, Jean-Roch; Potamianos, Karolos; Novotny, Kristiane; Vallecorsa, Sofia (2020-4-20) The unprecedented increase of complexity and scale of data is expected in the necessary computation for tracking detectors of the High Luminosity Large Hadron Collider (HL-LHC) experiments. While currently used Kalman filter based algorithms are reaching their limits in terms of ambiguities from increasing number of simultaneous collisions, occupancy, and scalability (worse than quadratic), a variety of machine learning approaches to particle track reconstruction are explored. It has been demonstrated previ...
A Novel Neural Network Method for Direction of Arrival Estimation with Uniform Cylindrical 12-Element Microstrip Patch Array Caylar, Selcuk; Dural, Guelbin; Leblebicioğlu, Mehmet Kemal (2008-01-01) In this study a new neural network algorithm is proposed for real time multiple source tracking problem with cylindrical patch antenna array based on a previous v reported Modified Neural Multiple Source Tracking Algorithm(MN-MUST). The proposed algorithm, namely Cylindrical Microstrip Patch Array Modified Neural Multiple Source Tracking Algorithm (CMN-MUST) implements W-MUST algorithm on a cylindrical microsttip patch array structure. CMN-MUST algorithm uses the advantage of directive pattern of microstrip...
A new approach to shutterless operation of microbolometer based infrared cameras Çavdar, Semih; Akın, Tayfun; Department of Electrical and Electronics Engineering (2021-2-03) This thesis presents an algorithm for the shutterless operation of microbolometer based infrared imaging systems. The active material of the microbolometer used in the thesis is vanadium oxide and it is produced in METU MEMS Center. The array size of the microbolometer is 480x640 and pixel pitch is 17 μm. The microbolometer has a CTIA type readout circuit which consists of detector and reference resistance. Voltage values are optimized at steady-state considering interactions between pixels in the focal pla...
A deep learning methodology for the flow field prediction around airfoils Duru, Cihat; Baran, Özgür Uğraş; Alemdar, Hande; Department of Mechanical Engineering (2021-9-07) This study aims to predict flow fields around airfoils using a deep learning methodology based on an encoder-decoder convolutional neural network. Neural network training and evaluation are performed from a set of computational fluid dynamics (CFD) solutions of the 2-D flow field around a group of known airfoils at a wide range of angles of attack. Reynolds averaged Navier-Stokes (RANS)-based CFD simulations are performed at a selected Mach number on the transonic regime on high-quality structured computati...
A Distributed Fault-Tolerant Topology Control Algorithm for Heterogeneous Wireless Sensor Networks Bagci, Hakki; KÖRPEOĞLU, İBRAHİM; Yazıcı, Adnan (Institute of Electrical and Electronics Engineers (IEEE), 2015-04-01) This paper introduces a distributed fault-tolerant topology control algorithm, called the Disjoint Path Vector (DPV), for heterogeneous wireless sensor networks composed of a large number of sensor nodes with limited energy and computing capability and several supernodes with unlimited energy resources. The DPV algorithm addresses the k-degree Anycast Topology Control problem where the main objective is to assign each sensor's transmission range such that each has at least k-vertex-disjoint paths to superno...

Citation Formats

C. Tüysüz et al., “A Quantum Graph Network Approach to Particle Track Reconstruction,” 2020, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/70223.