scholarly journals Learning to discover: expressive Gaussian mixture models for multi-dimensional simulation and parameter inference in the physical sciences

2022 ◽  
Author(s):  
Stephen Burns Menary ◽  
Darren David Price
Keyword(s):  
Particle Physics ◽  
Scientific Discovery ◽  
Hadron Collider ◽  
Gaussian Mixture Models ◽  
Gaussian Mixture ◽  
High Energy ◽  
High Energy Particle ◽  
Calibration Data ◽  
Likelihood Ratios ◽  
Dimensional Simulation

Abstract We show that density models describing multiple observables with (i) hard boundaries and (ii) dependence on external parameters may be created using an auto-regressive Gaussian mixture model. The model is designed to capture how observable spectra are deformed by hypothesis variations, and is made more expressive by projecting data onto a configurable latent space. It may be used as a statistical model for scientific discovery in interpreting experimental observations, for example when constraining the parameters of a physical model or tuning simulation parameters according to calibration data. The model may also be sampled for use within a Monte Carlo simulation chain, or used to estimate likelihood ratios for event classification. The method is demonstrated on simulated high-energy particle physics data considering the anomalous electroweak production of a $Z$ boson in association with a dijet system at the Large Hadron Collider, and the accuracy of inference is tested using a realistic toy example. The developed methods are domain agnostic; they may be used within any field to perform simulation or inference where a dataset consisting of many real-valued observables has conditional dependence on external parameters.

Concepts of Elementary Particle Physics

2019 ◽  
Author(s):  
Michael E. Peskin
Keyword(s):  
Elementary Particle ◽  
Particle Physics ◽  
Hadron Collider ◽  
Z Bosons ◽  
High Energy ◽  
High Energy Particle ◽  
Particle Interactions ◽  
Elementary Particle Physics ◽  
The Standard Model ◽  
Precision Study

This is a textbook of elementary particle physics whose goal is to explain the Standard Model of particle interactions. Part I introduces the basic concepts governing high-energy particle physics: elements of relativity and quantum field theory, the quark model of hadrons, methods for detection and measurement of elementary particles, methods for calculating predictions for observable quantitites. Part II builds up our understanding of the strong interaction from the key experiments to the formulation of Quantum Chromodynamics and its application to the description of evetns at the CERN Large Hadron Collider. Part III build up our understanding of the weak interaction from the key experiments to the formulation of spontaneously broken gauge theories. It then describes the tests and extensions of this theory, including the precision study of the W and Z bosons, CP violation, neutrino mass, and the Higgs boson.

scholarly journals Charged Particle Tracking via Edge-Classifying Interaction Networks

2021 ◽  
Vol 5 (1) ◽  
Author(s):  
Gage DeZoort ◽  
Savannah Thais ◽  
Javier Duarte ◽  
Vesal Razavimaleki ◽  
Markus Atkinson ◽  
...  
Keyword(s):  
Particle Tracking ◽  
Particle Physics ◽  
Message Passing ◽  
Heterogeneous Computing ◽  
Hadron Collider ◽  
Interaction Network ◽  
High Energy ◽  
High Energy Particle ◽  
Particle Trajectories ◽  
Edge Classification

AbstractRecent work has demonstrated that geometric deep learning methods such as graph neural networks (GNNs) are well suited to address a variety of reconstruction problems in high-energy particle physics. In particular, particle tracking data are naturally represented as a graph by identifying silicon tracker hits as nodes and particle trajectories as edges, given a set of hypothesized edges, edge-classifying GNNs identify those corresponding to real particle trajectories. In this work, we adapt the physics-motivated interaction network (IN) GNN toward the problem of particle tracking in pileup conditions similar to those expected at the high-luminosity Large Hadron Collider. Assuming idealized hit filtering at various particle momenta thresholds, we demonstrate the IN’s excellent edge-classification accuracy and tracking efficiency through a suite of measurements at each stage of GNN-based tracking: graph construction, edge classification, and track building. The proposed IN architecture is substantially smaller than previously studied GNN tracking architectures; this is particularly promising as a reduction in size is critical for enabling GNN-based tracking in constrained computing environments. Furthermore, the IN may be represented as either a set of explicit matrix operations or a message passing GNN. Efforts are underway to accelerate each representation via heterogeneous computing resources towards both high-level and low-latency triggering applications.

Microfabrication research at the National Submicron Facility

1983 ◽  
Vol 41 ◽  
pp. 86-89
Author(s):  
E.D. Wolf
Keyword(s):  
Integrated Circuits ◽  
Particle Physics ◽  
High Speed ◽  
New Technology ◽  
High Energy ◽  
High Energy Particle ◽  
New Science ◽  
Wide Range ◽  
Intermediate Domain ◽  
Circuit Function

Most microelectronics devices and circuits operate faster, consume less power, execute more functions and cost less per circuit function when the feature-sizes internal to the devices and circuits are made smaller. This is part of the stimulus for the Very High-Speed Integrated Circuits (VHSIC) program. There is also a need for smaller, more sensitive sensors in a wide range of disciplines that includes electrochemistry, neurophysiology and ultra-high pressure solid state research. There is often fundamental new science (and sometimes new technology) to be revealed (and used) when a basic parameter such as size is extended to new dimensions, as is evident at the two extremes of smallness and largeness, high energy particle physics and cosmology, respectively. However, there is also a very important intermediate domain of size that spans from the diameter of a small cluster of atoms up to near one micrometer which may also have just as profound effects on society as “big” physics.

All-Union Conference on High Energy Particle Physics

Atomic Energy ◽  
1956 ◽  
Vol 1 (4) ◽  
pp. 621-632
Author(s):  
V. A. Biryukov ◽  
B. M. Golovin ◽  
L. I. Lapidus
Keyword(s):  
Particle Physics ◽  
High Energy ◽  
High Energy Particle ◽  
Energy Particle ◽  
Union Conference

scholarly journals Programmable combinational logic trigger system for high energy particle physics experiments

1977 ◽  
Vol 140 (3) ◽  
pp. 549-552 ◽  
Author(s):  
E.D. Platner ◽  
A. Etkin ◽  
K.J. Foley ◽  
J.H. Goldman ◽  
W.A. Love ◽  
...  
Keyword(s):  
Particle Physics ◽  
High Energy ◽  
High Energy Particle ◽  
Combinational Logic ◽  
Trigger System ◽  
Energy Particle ◽  
Physics Experiments

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.

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