scholarly journals Quantum pattern recognition algorithms for charged particle tracking

2021 ◽  
Vol 380 (2216) ◽  
Author(s):  
H. M. Gray
Keyword(s):  
Pattern Recognition ◽  
Charged Particle ◽  
Particle Physics ◽  
High Energy Physics ◽  
Rapid Development ◽  
Hadron Collider ◽  
Simulated Data ◽  
High Energy ◽  
Recognition Algorithms ◽  
Energy Physics

High-energy physics is facing a daunting computing challenge with the large datasets expected from the upcoming High-Luminosity Large Hadron Collider in the next decade and even more so at future colliders. A key challenge in the reconstruction of events of simulated data and collision data is the pattern recognition algorithms used to determine the trajectories of charged particles. The field of quantum computing shows promise for transformative capabilities and is going through a cycle of rapid development and hence might provide a solution to this challenge. This article reviews current studies of quantum computers for charged particle pattern recognition in high-energy physics. This article is part of the theme issue ‘Quantum technologies in particle physics’.

scholarly journals The Impact of Microelectronics on High Energy Physics Innovation: The Role of 65 nm CMOS Technology on New Generation Particle Detectors

2021 ◽  
Vol 9 ◽  
Author(s):  
N. Demaria
Keyword(s):  
Particle Physics ◽  
High Energy Physics ◽  
Hadron Collider ◽  
Cmos Technology ◽  
High Energy ◽  
Main Role ◽  
Noise Power ◽  
Recent Developments ◽  
The Impact ◽  
Energy Physics

The High Luminosity Large Hadron Collider (HL-LHC) at CERN will constitute a new frontier for the particle physics after the year 2027. Experiments will undertake a major upgrade in order to stand this challenge: the use of innovative sensors and electronics will have a main role in this. This paper describes the recent developments in 65 nm CMOS technology for readout ASIC chips in future High Energy Physics (HEP) experiments. These allow unprecedented performance in terms of speed, noise, power consumption and granularity of the tracking detectors.

scholarly journals Impact of detector simulation in particle physics collider experiments - highlights

2019 ◽  
Vol 214 ◽  
pp. 02019
Author(s):  
V. Daniel Elvira
Keyword(s):  
Particle Physics ◽  
High Energy Physics ◽  
Hadron Collider ◽  
High Energy ◽  
Design And Optimization ◽  
The Cost ◽  
Detector Simulation ◽  
The Impact ◽  
Journal Submission ◽  
Energy Physics

Detector simulation has become fundamental to the success of modern high-energy physics (HEP) experiments. For example, the Geant4-based simulation applications developed by the ATLAS and CMS experiments played a major role for them to produce physics measurements of unprecedented quality and precision with faster turnaround, from data taking to journal submission, than any previous hadron collider experiment. The material presented here contains highlights of a recent review on the impact of detector simulation in particle physics collider experiments published in Ref. [1]. It includes examples of applications to detector design and optimization, software development and testing of computing infrastructure, and modeling of physics objects and their kinematics. The cost and economic impact of simulation in the CMS experiment is also presented. A discussion on future detector simulation needs, challenges and potential solutions to address them is included at the end.

scholarly journals Quantum machine learning and its supremacy in high energy physics

2020 ◽  
pp. 2030024
Author(s):  
Kapil K. Sharma
Keyword(s):  
Machine Learning ◽  
Pattern Recognition ◽  
Particle Physics ◽  
High Energy Physics ◽  
Recognition Task ◽  
High Energy ◽  
Particle Identification ◽  
Quantum Machine Learning ◽  
The Future ◽  
Energy Physics

This paper reveals the future prospects of quantum algorithms in high energy physics (HEP). Particle identification, knowing their properties and characteristics is a challenging problem in experimental HEP. The key technique to solve these problems is pattern recognition, which is an important application of machine learning and unconditionally used for HEP problems. To execute pattern recognition task for track and vertex reconstruction, the particle physics community vastly use statistical machine learning methods. These methods vary from detector-to-detector geometry and magnetic field used in the experiment. Here, in this paper, we deliver the future possibilities for the lucid application of quantum computation and quantum machine learning in HEP, rather than focusing on deep mathematical structures of techniques arising in this domain.

A NEURAL NETWORK CLASSIFIER IN EXPERIMENTAL PARTICLE PHYSICS

1993 ◽  
Vol 04 (02) ◽  
pp. 95-108 ◽  
Author(s):  
AMIR A. HANDZEL ◽  
T. GROSSMAN ◽  
E. DOMANY ◽  
S. TAREM ◽  
E. DUCHOVNI
Keyword(s):  
Neural Network ◽  
Multilayer Perceptron ◽  
Particle Physics ◽  
High Energy Physics ◽  
Simulated Data ◽  
Classification Problem ◽  
High Energy ◽  
Neural Network Classifier ◽  
Backpropagation Algorithm ◽  
Energy Physics

A classification problem in high energy physics has been solved on simulated data using a simple multilayer perceptron comprising binary units which was trained with the CHIR algorithm. The unstable training of such a network on a nonseparable set has been overcome by selecting those weight vectors with good performance while providing a flexible choice of the two types of classification errors. Specific features of the problem have been exploited in order to simplify and optimize the solution which has been compared to the popular backpropagation algorithm and found to perform on a similar level. Additional aspects of this work are the use of the CHIR algorithm on continuous input and incorporating the classic idea of a Φ-machine in a multilayer perceptron.

scholarly journals Graph Variational Autoencoder for Detector Reconstruction and Fast Simulation in High-Energy Physics

2021 ◽  
Vol 251 ◽  
pp. 03051
Author(s):  
Ali Hariri ◽  
Darya Dyachkova ◽  
Sergei Gleyzer
Keyword(s):  
Large Hadron Collider ◽  
Particle Physics ◽  
High Energy Physics ◽  
Hadron Collider ◽  
High Energy ◽  
Proton Collision ◽  
Learning Approaches ◽  
Fast Simulation ◽  
High Level ◽  
Energy Physics

Accurate and fast simulation of particle physics processes is crucial for the high-energy physics community. Simulating particle interactions with the detector is both time consuming and computationally expensive. With its proton-proton collision energy of 13 TeV, the Large Hadron Collider is uniquely positioned to detect and measure the rare phenomena that can shape our knowledge of new interactions. The High-Luminosity Large Hadron Collider (HLLHC) upgrade will put a significant strain on the computing infrastructure and budget due to increased event rate and levels of pile-up. Simulation of highenergy physics collisions needs to be significantly faster without sacrificing the physics accuracy. Machine learning approaches can offer faster solutions, while maintaining a high level of fidelity. We introduce a graph generative model that provides effiective reconstruction of LHC events on the level of calorimeter deposits and tracks, paving the way for full detector level fast simulation.

scholarly journals Automated Computation of One-Loop Amplitudes

2018 ◽  
Vol 68 (1) ◽  
pp. 291-312 ◽  
Author(s):  
Celine Degrande ◽  
Valentin Hirschi ◽  
Olivier Mattelaer
Keyword(s):  
High Energy Physics ◽  
Expert Knowledge ◽  
Hadron Collider ◽  
High Energy ◽  
Hadron Colliders ◽  
Collider Phenomenology ◽  
Physics Community ◽  
High Degree ◽  
Energy Physics ◽  
Loop Amplitudes

The automation of one-loop amplitudes plays a key role in addressing several computational challenges for hadron collider phenomenology: They are needed for simulations including next-to-leading-order corrections, which can be large at hadron colliders. They also allow the exact computation of loop-induced processes. A high degree of automation has now been achieved in public codes that do not require expert knowledge and can be widely used in the high-energy physics community. In this article, we review many of the methods and tools used for the different steps of automated one-loop amplitude calculations: renormalization of the Lagrangian, derivation and evaluation of the amplitude, its decomposition onto a basis of scalar integrals and their subsequent evaluation, as well as computation of the rational terms.

scholarly journals Design and Performance Evaluation of Multi-Gb/s Silicon Photonics Transmitters for High Energy Physics

Energies ◽  
2020 ◽  
Vol 13 (14) ◽  
pp. 3569
Author(s):  
Simone Cammarata ◽  
Gabriele Ciarpi ◽  
Stefano Faralli ◽  
Philippe Velha ◽  
Guido Magazzù ◽  
...  
Keyword(s):  
Silicon Photonics ◽  
Particle Physics ◽  
High Energy Physics ◽  
Current Mode ◽  
High Energy ◽  
Silicon On Insulator ◽  
Output Stage ◽  
Optical Links ◽  
And Performance ◽  
Energy Physics

Optical links are rapidly becoming pervasive in the readout chains of particle physics detector systems. Silicon photonics (SiPh) stands as an attractive candidate to sustain the radiation levels foreseen in the next-generation experiments, while guaranteeing, at the same time, multi-Gb/s and energy-efficient data transmission. Integrated electronic drivers are needed to enable SiPh modulators’ deployment in compact on-detector front-end modules. A current-mode logic-based driver harnessing a pseudo-differential output stage is proposed in this work to drive different types of SiPh devices by means of the same circuit topology. The proposed driver, realized in a 65 nm bulk technology and already tested to behave properly up to an 8 MGy total ionizing dose, is hybridly integrated in this work with a lumped-element Mach–Zehnder modulator (MZM) and a ring modulator (RM), both fabricated in a 130 nm silicon-on-insulator (SOI) process. Bit-error-rate (BER) performances confirm the applicability of the selected architecture to either differential and single-ended loads. A 5 Gb/s data rate, in line with the current high energy physics requirements, is achieved in the RM case, while a packaging-related performance degradation is captured in the MZM-based system, confirming the importance of interconnection modeling.

scholarly journals Yifang Wang: high energy physics in China

2016 ◽  
Vol 3 (2) ◽  
pp. 252-256 ◽  
Author(s):  
Ling Wang ◽  
Mu-ming Poo
Keyword(s):  
Neutrino Oscillation ◽  
Particle Physics ◽  
Quantum Interference ◽  
High Energy Physics ◽  
High Energy ◽  
Electron Neutrino ◽  
Daya Bay ◽  
Mass Hierarchy ◽  
Neutrino Mixing ◽  
Energy Physics

Abstract On 8 March 2012, Yifang Wang, co-spokesperson of the Daya Bay Experiment and Director of Institute of High Energy Physics, Chinese Academy of Sciences, announced the discovery of a new type of neutrino oscillation with a surprisingly large mixing angle (θ13), signifying ‘a milestone in neutrino research’. Now his team is heading for a new goal—to determine the neutrino mass hierarchy and to precisely measure oscillation parameters using the Jiangmen Underground Neutrino Observatory, which is due for completion in 2020. Neutrinos are fundamental particles that play important roles in both microscopic particle physics and macroscopic universe evolution. The studies on neutrinos, for example, may answer the question why our universe consists of much more matter than antimatter. But this is not an easy task. Though they are one of the most numerous particles in the universe and zip through our planet and bodies all the time, these tiny particles are like ‘ghost’, difficult to be captured. There are three flavors of neutrinos, known as electron neutrino (νe), muon neutrino (νμ), and tau neutrino (ντ), and their flavors could change as they travel through space via quantum interference. This phenomenon is known as neutrino oscillation or neutrino mixing. To determine the absolute mass of each type of neutrino and find out how they mix is very challenging. In a recent interview with NSR in Beijing, Yifang Wang explained how the Daya Bay Experiment on neutrino oscillation not only addressed the frontier problem in particle physics, but also harnessed the talents and existing technology in Chinese physics community. This achievement, Wang reckons, will not be an exception in Chinese high energy physics, when appropriate funding and organization for big science projects could be efficiently realized in the future.

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