DOCSLIB.ORG

HEP Analysis

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
HEP Analysis HEP Analysis John Morris Monte Carlo What is MC? Why use MC? MC Generation Detector Sim Reconstruction Reconstruction Event Properties HEP Analysis MC Corrections MC Corrections Re-Weighting Tag & Probe John Morris Smearing Summary Queen Mary University of London Measurements 2012 Cross section Luminosity Selection Backgrounds Efficiency Results Uncertainties Statistical Luminosity Experimental Modelling Searches Searches Higgs BSM Conclusion 1 / 105 HEP Analysis John Morris HEP Analysis Monte Carlo What is MC? Why use MC? MC Generation Outline Detector Sim Reconstruction • Monte Carlo (MC) Reconstruction • What is MC and why do we use it? Event Properties MC Corrections • MC event generation MC Corrections • MC detector simulation Re-Weighting Tag & Probe • What happens when MC does not agree with data? Smearing Summary • Event re-weighting Measurements • 4-vector smearing Cross section Luminosity • Cross Section measurement Selection Backgrounds Efficiency • Systematic uncertainties Results Uncertainties Statistical • These notes will focus on the ATLAS experiment, but also apply Luminosity Experimental to other experiments Modelling • Searches Borrowing slides, plots and ideas from E. Rizvi, T. Sjostrand Searches and ATLAS public results Higgs BSM Conclusion 2 / 105 HEP Analysis John Morris HEP Analysis Monte Carlo What is MC? This is not an in-depth MC course Why use MC? MC Generation Detector Sim • The subject of MC is vast Reconstruction Reconstruction • There are many different models, generators and techniques Event Properties • Could spend hours, weeks, months on each MC Corrections MC Corrections • These slides are just a brief outline Re-Weighting Tag & Probe • Focus on the generalities Smearing Summary • Explain the steps involved Measurements • Not going to get bogged down in details Cross section Luminosity Selection Aim of this course Backgrounds Efficiency • Aim is to provide a working knowledge of MC basics Results • Steps involved in generation Uncertainties Statistical • Introduction to various systematics Luminosity Experimental • Techniques for correcting MC to describe data Modelling Searches • Overview of a measurement Searches Higgs • Systematics BSM Conclusion 3 / 105 HEP Analysis John Morris Monte Carlo (MC) - What is MC? Monte Carlo What is MC? Why use MC? MC Generation Detector Sim • MC is a simulation of collisions between particles Reconstruction • Reconstruction For these notes, pp collisions in ATLAS at the LHC Event Properties • MC is used in all particle physics experiments MC Corrections MC Corrections • MC generation is broadly split into 2 parts: Re-Weighting 1 Generation of the physics process Tag & Probe Smearing • Matrix element calculation Summary • ...(lots of steps)... Measurements • 4-vectors of final state hadrons Cross section Luminosity 2 Simulation of the detector Selection Backgrounds • Trigger, tracking and calorimeter simulation Efficiency • ...(lots of steps)... Results • Analysis objects like electrons and jets Uncertainties Statistical • End result: Luminosity Experimental • MC Samples in the same format as the actual data Modelling • With additional information from Step 1, the truth record Searches Searches Higgs BSM Conclusion 4 / 105 HEP Analysis John Morris Monte Carlo (MC) - What is MC? Monte Carlo What is MC? Why use MC? MC Generation Detector Sim Reconstruction Reconstruction Event Properties MC Corrections MC Corrections Re-Weighting Tag & Probe Smearing Summary Measurements Cross section Luminosity Selection Backgrounds Efficiency Results Uncertainties Statistical Luminosity Experimental Modelling Searches • MC simulates what happens at the LHC and ATLAS Searches Higgs • Many different programmes can be used at each stage BSM Conclusion 5 / 105 HEP Analysis John Morris Monte Carlo (MC) - Why use MC? Monte Carlo What is MC? Why use generators? Why use MC? MC Generation Detector Sim • Allows studies of complex multi-particle physics Reconstruction Reconstruction • Allows studies of theoretical models Event Properties • ) What does a SUSY signal look like? MC Corrections MC Corrections Can be used to Re-Weighting Tag & Probe Smearing • Predict cross sections and topologies of various processes Summary • ) Feasibility study - Can we find the theoretical particle X? Measurements Cross section • Simulate background processes to the signal of interest Luminosity Selection • ) Can devise analysis strategies Backgrounds Efficiency • Study detector response Results Uncertainties • ) Optimise trigger & detector selection cuts Statistical Luminosity • Study detector imperfections Experimental Modelling • ) Can evaluate acceptance corrections Searches • See next week for a discussion of acceptance Searches Higgs • Remove the effect of the apparatus from the measurement BSM • ) Unfold the data. Correcting the data for detector effects Conclusion 6 / 105 HEP Analysis John Morris MC Generation Monte Carlo What is MC? Why use MC? MC Generation Detector Sim Use random numbers for integration Reconstruction Reconstruction • Code uses random number generator to integrate Event Properties x2 MC Corrections Z MC Corrections f (x) :dx = (x2 − x1) hf (x)i Re-Weighting x1 Tag & Probe Smearing N Summary 1 X hf (x)i = f (x ) Measurements N i Cross section i=1 Luminosity Selection Backgrounds Efficiency • Cross section randomly sampled over phase space Results • Want to generate events that simulate nature Uncertainties Statistical • Get average and fluctuations right Luminosity Experimental • Need to make random choices, as in nature Modelling • An event with n particles involves O (10n) random choices Searches • LHC events involve 1000's of random choices Searches Higgs BSM Conclusion 7 / 105 HEP Analysis John Morris Monte Carlo Generation Monte Carlo What is MC? Why use MC? MC Generation Detector Sim Reconstruction Reconstruction Event Properties MC Corrections MC Corrections Re-Weighting Tag & Probe Smearing Summary Measurements Cross section Luminosity Selection Backgrounds Efficiency Results Uncertainties Statistical Luminosity Experimental Modelling Searches Searches Higgs BSM Conclusion 8 / 105 HEP Analysis John Morris Monte Carlo Generation Monte Carlo What is MC? Why use MC? MC Generation Detector Sim Reconstruction Reconstruction Event Properties MC Corrections MC Corrections Re-Weighting Tag & Probe Smearing Summary Measurements Cross section Luminosity Selection Backgrounds Efficiency Results Uncertainties Statistical Luminosity Experimental Modelling Searches Searches Higgs BSM Conclusion 9 / 105 HEP Analysis John Morris Monte Carlo Generation Monte Carlo What is MC? Why use MC? MC Generation Detector Sim Reconstruction Identifying theoretical modelling uncertainties Reconstruction Event Properties MC Corrections • Let's look at some of these steps in more detail MC Corrections Re-Weighting • There are several ways to perform each step Tag & Probe Smearing • This choice leads to theoretical modelling uncertainties Summary • These uncertainties enter into most LHC results Measurements Cross section • Want to understand where they come from Luminosity Selection • Theoretical modelling uncertainties include: Backgrounds Efficiency • Generator uncertainties Results • PDF uncertainties Uncertainties Statistical • Parton shower uncertainties Luminosity • Hadronisation uncertainties Experimental Modelling Searches Searches Higgs BSM Conclusion 10 / 105 HEP Analysis John Morris Matrix element calculation Monte Carlo What is MC? Why use MC? Normally calculated at LO or NLO MC Generation Detector Sim Reconstruction Reconstruction Event Properties MC Corrections MC Corrections Re-Weighting Tag & Probe Smearing Summary Measurements Cross section • Higher order corrections are important: Luminosity Selection • Normalisation and shape of kinematic distributions Backgrounds Efficiency • Multiplicity of objects like jets Results Uncertainties • Higher order corrections are hard to calculate and CPU intensive Statistical Luminosity • Several programs that will do the calculation Experimental Modelling • Different calculation techniques Searches • Different assumptions Searches • Different results Higgs BSM • ) Theoretical modelling uncertainty Conclusion 11 / 105 HEP Analysis John Morris PDF Monte Carlo What is MC? The ProtonPartonDensityFunction (PDF) Why use MC? MC Generation Detector Sim • Proton is not a point-like particle, it's full of partons Reconstruction Reconstruction • Need to calculate: Event Properties • Probability of propagator interacting with quarks/gluon MC Corrections 2 MC Corrections • Needed as a function of Q and x Re-Weighting Tag & Probe Smearing HERA I+II inclusive, jets, charm PDF Fit • Various groups provide PDFs Summary 1 xf Q2 = 10 GeV2 Measurements • Parametrised differently Cross section HERAPDF1.7 (prel.) 0.8 Luminosity exp. uncert. • LHC uses: Selection model uncert. Backgrounds parametrization uncert. • xuv CTEQ Efficiency 0.6 Results HERAPDF1.6 (prel.) • MSTW Uncertainties • NNPDF Statistical 0.4 × Luminosity xg ( 0.05) xdv • Get a different result using Experimental Modelling different PDFs Searches 0.2 • ) Theoretical modelling Searches xS (× 0.05) Higgs uncertainty BSM HERAPDF Structure Function Working Group June 2011 10-4 10-3 10-2 10-1 1 Conclusion x 12 / 105 HEP Analysis John Morris Parton Showering Monte Carlo What is MC? Why use MC? MC Generation Detector Sim Reconstruction Reconstruction Event Properties MC Corrections MC Corrections Re-Weighting Tag & Probe Smearing Summary Measurements Cross section Luminosity Selection Backgrounds • Efficiency Need to go from 2!2 scattering
Load more

Recommended publications
  • Search for Higgs Boson Decays to Beyond-The-Standard-Model Light Bosons in Four-Lepton Events√ with the ATLAS Detector at S = 13 Tev
    EUROPEAN ORGANISATION FOR NUCLEAR RESEARCH (CERN) JHEP 06 (2018) 166 CERN-EP-2017-293 DOI: 10.1007/JHEP06(2018)166 25th July 2018 Search for Higgs boson decays to beyond-the-Standard-Model light bosons in four-lepton eventsp with the ATLAS detector at s = 13 TeV The ATLAS Collaboration A search is conducted for a beyond-the-Standard-Model boson using events where a Higgs boson with mass 125 GeV decays to four leptons (` = e or µ). This decay is presumed to occur via an intermediate state which contains one or two on-shell, promptly decaying bosons: H ! ZX/XX ! 4`, where X is a new vector boson Zd or pseudoscalar a with mass between 1 and 60 GeV. The search uses pp collision data collected with the ATLAS detectorp at the LHC with an integrated luminosity of 36.1 fb−1 at a centre-of-mass energy s = 13 TeV. No significant excess of events above Standard Model background predictions is observed; therefore, upper limits at 95% confidence level are set on model-independent fiducial cross- sections, and on the Higgs boson decay branching ratios to vector and pseudoscalar bosons in two benchmark models. arXiv:1802.03388v2 [hep-ex] 24 Jul 2018 © 2018 CERN for the benefit of the ATLAS Collaboration. Reproduction of this article or parts of it is allowed as specified in the CC-BY-4.0 license. Contents 1 Introduction 2 2 Benchmark models3 2.1 Vector-boson model4 2.2 Pseudoscalar-boson model5 3 ATLAS detector 6 4 Event reconstruction7 4.1 Trigger and event preselection7 4.2 Lepton reconstruction7 4.3 Definition of invariant-mass kinematic variables8 4.4
    [Show full text]
  • The PYTHIA Event Generator: Past, Present and Future∗
    LU TP 19-31 MCnet-19-16 July 2019 The PYTHIA Event Generator: Past, Present and Future∗ Torbj¨ornSj¨ostrand Theoretical Particle Physics, Department of Astronomy and Theoretical Physics, Lund University, S¨olvegatan 14A, SE-223 62 Lund, Sweden Abstract The evolution of the widely-used Pythia particle physics event generator is outlined, from the early days to the current status and plans. The key decisions and the development of the major physics components are put in arXiv:1907.09874v1 [hep-ph] 23 Jul 2019 context. ∗submitted to 50 Years of Computer Physics Communications: a special issue focused on computational science software 1 Introduction The Pythia event generator is one of the most commonly used pieces of software in particle physics and related areas, either on its own or \under the hood" of a multitude of other programs. The program is designed to simulate the physics processes that can occur in collisions between high-energy particles, e.g. at the LHC collider at CERN. Monte Carlo methods are used to represent the quantum mechanical variability that can give rise to wildly different multiparticle final states under fixed simple initial conditions. A combi- nation of perturbative results and models for semihard and soft physics | many of them developed in the Pythia context | are combined to trace the evolution towards complex final states. The program roots stretch back over forty years to the Jetset program, with which it later was fused. Twelve manuals for the two programs have been published in Computer Physics Communications (CPC), Table 1, together collecting over 20 000 citations in the Inspire database.
    [Show full text]
  • Monte Carlo Event Generators for High Energy Particle Physics Event Simulation
    Monte Carlo event generators for high energy particle physics event simulation Edited by: 21 3 27 23 Andy Buckley ,​ Frank Krauss ,​ Simon Plätzer ,​ Michael Seymour ​ ​ ​ ​ on behalf of: 16 3 11 18 23 Simone Alioli ,​ Jeppe Andersen ,​ Johannes Bellm ,​ Jon Butterworth ,​ Mrinal Dasgupta ,​ ​ 1,18 ​ 6 ​ 9 1​ 8 18 ​ Claude Duhr ,​ Stefano Frixione ,​ Stefan Gieseke ,​ Keith Hamilton ,​ Gavin Hesketh ,​ ​ 14 2​ 2​ 6 1​ 1 18 ​ Stefan Hoeche ,​ Hannes Jung ,​ Wolfgang Kilian ,​ Leif Lönnblad ,​ Fabio Maltoni ,​ ​ 1 ​ 4 ​ 2 ​ 16 ​ 18 Michelangelo Mangano ,​ Stephen Mrenna ,​ Zoltán Nagy ,​ Paolo Nason ,​ Emily Nurse ,​ 28 ​ 16 ​ 13,19​ ​5 ​ 11 Thorsten Ohl ,​ Carlo Oleari ,​ Andreas Papaefstathiou ,​ Tilman Plehn ,​ Stefan Prestel ,​ ​ 1,10 ​ 2 1,3 ​ 25 ​ ​ 3 Emanuele Ré ,​ Juergen Reuter ,​ Peter Richardson ,​ Gavin Salam ,​ Marek Schoenherr ,​ ​ 22 ​ 15 ​ 7 ​ 11 ​ 11 Steffen Schumann ,​ Frank Siegert ,​ Andrzej Siódmok ,​ Malin Sjödahl ,​ Torbjörn Sjöstrand ,​ ​ 12 ​ 24 ​ 8 ​ 20 ​ Peter Skands ,​ Davison Soper ,​ Gregory Soyez ,​ Bryan Webber ​ ​ ​ ​ 1 2 3 C​ ERN, ​ DESY, Germany, ​ Durham University, UK, 4 ​ ​ 5 ​ Fermi National Accelerator Laboratory, USA, ​ Heidelberg University, Germany, 6 7 ​ ​ INFN, Sez. di Genova, Italy, ​ Institute of Nuclear Physics, Cracow, Poland, 8 9 ​ ​ IPhT Saclay, France, ​ Karlsruhe Institute of Technology, Germany, 10 11 ​ 12 ​ LAPTh Annecy, France, ​ Lund University, Sweden, ​ Monash University, Australia, 13 ​ 14 ​ ​ NIKHEF, Netherlands, ​ SLAC National Accelerator Laboratory, USA, 15 ​ 16 ​ Technical
    [Show full text]
  • Higgs Boson Reconstruction and Detection Algorithm Analysis of the H0 → Z0 + Z0 → 2Μ+ + 2Μ− Decay Chain Using CMSSW
    Higgs Boson Reconstruction and Detection Algorithm Analysis of the h0 ! Z0 + Z0 ! 2µ+ + 2µ− Decay Chain Using CMSSW Matthew Sanders∗ Washington and Lee University (Dated: August 20, 2008) Abstract The Compact Muon Solenoid is expected to become operational towards the end of 2008. When it does, our understanding of the world of elementary particles could change dramatically with the discovery of new particles such as the Higgs boson. The CMS collaboration had already developed a programming package called CMS Software (CMSSW) to aid in the discovery of the Higgs boson and other new physics. Using CMSSW researchers can develop and test algorithms designed to successfully detect the Higgs boson. In this project, I developed and tested one such algorithm to reconstruct the Higgs boson from four muons. I then compared the effectiveness of my algorithm to the effectiveness of a similar algorithm that also reconstructs the Higgs boson from four muons. Contents to satisfy three ends. They hope to probe physics at the TeV level, discover new physics beyond the standard I. Introdcution 1 model (i.e. supersymmetry and/or extra dimensions) and A. What is CMS? 1 discover the Higgs boson [1]. The latter of these goals has B. The Higgs Boson been the focus of my research. A Brief History and Itroduction 2 C. An Introduction to CMSSW 2 A. What is CMS? II. Higgs Boson Reconstruction Through Algorithm Development and Analysis 3 The CMS collaboration is a collection of researchers A. Summer Project from various educational and private research institu- h0 ! Z0 + Z0 ! 2µ+ + 2µ− 3 tions.
    [Show full text]
  • Topics in Experimental Particle Physics S
    Topics in Experimental Particle Physics S. Xella credit J.Butterworth “Atomland” 1 Content Focus of these lectures is accelerator-based particle physics. • Lecture 1: Colliders. Focus: LHC. One experiment in details : ATLAS : detector+trigger+reconstruction of collisions. • Lecture 2: What have we learnt about the Higgs boson so far. • Lecture 3: New physics beyond the Standard Model: what can the LHC collider tell us (DM, neutrinos, matter-antimatter asymmetry). • Lecture 4: Possible future accelerator-based research at CERN: FCC, SHiP, Mathusla, FASER. What can they tell us? 2 Why do we need accelerators? particle physics Break it 3 the sky the lab 4 the sky the lab same physics 5 Why do we need accelerators • Precision tests of particle physics theory are best carried out at accelerator facilities. • one can reproduce the outcome of an experiment over and over in the lab and verify definitely that only one interpretation (= theory) is correct. • Discoveries of new particle phenomena are very trustworthy outputs of accelerator experiments : hadrons, J/psi, top quark, Higgs boson, CP violation in quarks processes, …. • hints from theory or from previous observations are usefull in the first use- case (test of theory) : choice of energy and type and luminosity best suited for testing. For the second case (discovery), simply try to reach a new frontier in energy or intensity (& be lucky?) is a worthwhile approach. 6 Accelerators are not enough • Neutrino or dark matter particle properties: questions not addressable with accelerators only. Need to exploit cosmic/atmospheric, nuclear reactors sources, etc… too. • In general, the current big questions in particle physics are really big ! They need different simultaneous experimental approaches, to find answers.
    [Show full text]
  • Quantum Field Theory Notes
    Quantum Field Theory Christoph Englert1 These notes are a write-up of lectures given at the RAL school for High Energy Physicists, which took place at Warwick in 2014. The aim is to introduce the canonical quantisation approach to QFT, and derive the Feynman rules for a scalar field. 1 Introduction Quantum Field Theory is a highly important cornerstone of modern physics. It underlies, for ex- ample, the description of elementary particles i.e. the Standard Model of particle physics is a QFT. There is currently no observational evidence to suggest that QFT is insufficient in describing particle behaviour, and indeed many theories for beyond the Standard Model physics (e.g. supersymmetry, extra dimensions) are QFTs. There are some theoretical reasons, however, for believing that QFT will not work at energies above the Planck scale, at which gravity becomes important. Aside from particle physics, QFT is also widely used in the description of condensed matter systems, and there has been a fruitful interplay between the fields of condensed matter and high energy physics. We will see that the need for QFT arises when one tries to unify special relativity and quan- tum mechanics, which explains why theories of use in high energy particle physics are quantum field theories. Historically, Quantum Electrodynamics (QED) emerged as the prototype of modern QFT's. It was developed in the late 1940s and early 1950s chiefly by Feynman, Schwinger and Tomonaga, and has the distinction of being the most accurately verified theory of all time: the anomalous magnetic dipole moment of the electron predicted by QED agrees with experiment with a stunning accuracy of one part in 1010! Since then, QED has been understood as forming part of a larger theory, the Standard Model of particle physics, which also describes the weak and strong nuclear forces.
    [Show full text]
  • The Higgs Boson Machine Learning Challenge
    JMLR: Workshop and Conference Proceedings 42:19-55, 2015 HEPML 2014 The Higgs boson machine learning challenge Claire Adam-Bourdarios [email protected] LAL, IN2P3/CNRS & University Paris-Sud, France Glen Cowan [email protected] Physics Department, Royal Holloway, University of London, UK C´ecileGermain [email protected] LRI, University Paris-Sud & CNRS & INRIA, France Isabelle Guyon [email protected] ChaLearn and ClopiNet, Berkeley, California, USA Bal´azsK´egl [email protected] LAL, IN2P3/CNRS & TAO team, INRIA & LRI, CNRS && University Paris-Sud, France David Rousseau [email protected] LAL, IN2P3/CNRS & University Paris-Sud, France Editor: Cowan, Germain, Guyon, K´egl,Rousseau Abstract The Higgs Boson Machine Learning Challenge (HiggsML or the Challenge for short) was organized to promote collaboration between high energy physicists and data scientists. The ATLAS experiment at CERN provided simulated data that has been used by physi- cists in a search for the Higgs boson. The Challenge was organized by a small group of ATLAS physicists and data scientists. It was hosted by Kaggle at https://www.kaggle. com/c/higgs-boson; the challenge data is now available on http://opendata.cern.ch/ collection/ATLAS-Higgs-Challenge-2014. This paper provides the physics background and explains the challenge setting, the challenge design, and analyzes its results. Keywords: high energy physics, Higgs boson, statistical tests, machine learning. c 2015 C. Adam-Bourdarios, G. Cowan, C. Germain, I. Guyon, B. K´egl& D. Rousseau. Adam-Bourdarios Cowan Germain Guyon Kegl´ Rousseau 1. Introduction This paper reports results of a unique experiment in high energy physics: using the power of the \crowd" to help solving difficult physics problems.
    [Show full text]
  • Quantum Field Theory University of Cambridge Part III Mathematical Tripos
    Preprint typeset in JHEP style - HYPER VERSION Michaelmas Term, 2006 and 2007 Quantum Field Theory University of Cambridge Part III Mathematical Tripos Dr David Tong Department of Applied Mathematics and Theoretical Physics, Centre for Mathematical Sciences, Wilberforce Road, Cambridge, CB3 OWA, UK http://www.damtp.cam.ac.uk/user/tong/qft.html [email protected] { 1 { Recommended Books and Resources • M. Peskin and D. Schroeder, An Introduction to Quantum Field Theory This is a very clear and comprehensive book, covering everything in this course at the right level. It will also cover everything in the \Advanced Quantum Field Theory" course, much of the \Standard Model" course, and will serve you well if you go on to do research. To a large extent, our course will follow the first section of this book. There is a vast array of further Quantum Field Theory texts, many of them with redeeming features. Here I mention a few very different ones. • S. Weinberg, The Quantum Theory of Fields, Vol 1 This is the first in a three volume series by one of the masters of quantum field theory. It takes a unique route to through the subject, focussing initially on particles rather than fields. The second volume covers material lectured in \AQFT". • L. Ryder, Quantum Field Theory This elementary text has a nice discussion of much of the material in this course. • A. Zee, Quantum Field Theory in a Nutshell This is charming book, where emphasis is placed on physical understanding and the author isn't afraid to hide the ugly truth when necessary.
    [Show full text]
  • Particle Physicsphysics -- Measurementsmeasurements Andand Theorytheory
    ParticleParticle PhysicsPhysics -- MeasurementsMeasurements andand TheoryTheory OutlineOutline Natural Units Relativistic Kinematics Particle Physics Measurements Lifetimes Resonances and Widths Scattering Cross section Collider and Fixed Target Experiments Conservation Laws Charge, Lepton and Baryon number, Parity, Quark flavours Theoretical Concepts Quantum Field Theory Klein-Gordon Equation Anti-particles Yukawa Potential Scattering Amplitude - Fermi’s Golden Rule Matrix elements Nuclear and Particle Physics Franz Muheim 1 ParticleParticle PhysicsPhysics UnitsUnits Particle Physics is relativistic and quantum mechanical Î c = 299 792 458 m/s Î ħ = h/2π = 1.055·10-34 Js Length size of proton: 1 fm = 10-15 m Lifetimes as short as 10-23 s Charge 1 e = -1.60·10-19 C Energy Units: 1 GeV = 109 eV -- 1 eV = 1.60·10-19 J use also MeV, keV Mass in GeV/c2, rest mass is E = mc2 NaturalNaturalNatural UnitsUnitsUnits Set ħ = c = 1 Î Mass [GeV/c2], energy [GeV] and momentum [GeV/c] in GeV Î Time [(GeV/ħ)-1], Length [(GeV/ħc)-1] in 1/GeV area [(GeV/ħc)-2] Useful relations ħc = 197 MeV fm ħ = 6.582 ·10-22 MeV s Nuclear and Particle Physics Franz Muheim 2 ParticleParticle PhysicsPhysics MeasurementsMeasurements How do we measure particle properties and interaction strengths? Static properties Mass How do you weigh an electron? Magnetic moment couples to magnetic field Spin, Parity Particle decays Lifetimes Force Lifetimes Resonances & Widths Strong 10-23 -- 10-20 s Allowed/forbidden El.mag. 10-20 -- 10-16 s Decays Weak 10-13 -- 103 s Conservation laws Scattering Elastic scattering e- p → e- p Inelastic annihilation e+ e- → µ+ µ- Cross section total σ Force Cross sections Differential dσ/dΩ Strong O(10 mb) Luminosity L El.mag.
    [Show full text]