DOCSLIB.ORG

Nuwro - Neutrino MC Event Generator

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Nuwro - Neutrino MC Event Generator NuWro - neutrino MC event generator Tomasz Golan 28.04.2017, UW HEP Seminar Outline Neutrino source νe Detector ′ α 1. Motivation να νµ ντ 2. NuWro event generator 3. Final State Interactions 3500 MB data Total 3000 EL on carbon np-nh 2500 EL on hydrogen Irreducible background 2000 Other backgrounds 1500 4. MB NCEL data analysis No. of events 1000 500 0 0 100 200 300 400 500 600 700 Reconstructed kinetic energy (MeV) Tomasz Golan NuWro @ HEP UW 2 / 61 Introduction Basic properties of neutrinos ■ 1 Three generations 2 -spin, no electric charge, small mass Introduction of fermions H Neutrino properties (extremely hard to detect) I II III PMNS matrix Probability of oscillation u c t γ Neutrino oscillation ■ Measurement idea Interactions with elementary particles Example: T2K BOSONS QUARKS s g Energy reconstruction → electroweak theory (Standard Model) d b MC generators ■ ν νµ ν 0 NuWro Interactions with nucleons e τ Z Final state interactions → form factors e µ τ ± MB NCEL analysis → parton distribution functions LEPTONS W Backup slides ■ Interactions with nuclei → nuclear effects ■ The neutrino flavor state is a superposition of the mass states: |ναi = i Uαi |νii νe The neutrino producedP in α state να νβ νµ ντ can be measured in β state. The phenomenon is called neutrino oscillation. Tomasz Golan NuWro @ HEP UW 4 / 61 Pontecorvo-Maki-Nakagawa-Sakata matrix The PMNS matrix defines the mass mixing in the lepton sector: −iδ 1 0 0 c13 0 s13e c12 s12 0 U = 0 c23 s23 0 1 0 −s12 c12 0 iδ 0 −s23 c23 −s13e 0 c13 0 0 1 cij = cos θij sij = sin θij θij - mixing angels δ - CP phase factor Measurements: 2 ■ Solar, reactor and accelerator: sin (2θ12) = 0.846 ± 0.021 2 ■ Atmospheric: sin (2θ23) > 0.92 2 ■ Reactor: sin (2θ13) = 0.093 ± 0.008 δ can be measured if and only if all mixing angles are nonzero. If δ 6= 0 CP is violated. Tomasz Golan NuWro @ HEP UW 5 / 61 Probability of oscillation Introduction 2 Neutrino properties P (να → νβ) = |hνβ(x)|να(y)i| = δαβ PMNS matrix 2 Probability of oscillation ∗ ∗ 2 ∆mij L Neutrino oscillation − 4 Re U UiβUα U sin Measurement idea αi j jβ 4E Example: T2K i>j Energy reconstruction 2 2 2 X ∆mij ≡ mi − mj 2 MC generators ∆m L + 2 Im U ∗ U U U ∗ sin2 ij NuWro αi iβ αj jβ 2E Final state interactions i>j X MB NCEL analysis Backup slides L - traveled distance (fixed), E - neutrino energy (will be discussed) Measurements: ■ 2 −5 2 Solar neutrinos: ∆m21 = (7.53 ± 0.18) · 10 eV ■ 2 −3 2 Atmospheric neutrinos: |∆m32| = (2.44 ± 0.06) · 10 eV Tomasz Golan NuWro @ HEP UW 6 / 61 Neutrino oscillation The probability of neutrino oscillation depends on: Normal hierarchy Inverted hierarchy 2 2 ■ m3 m2 What we want to measure: 2 ∆m21 2 2 ◆ three mixing angles - already measured ∆m32 m1 2 2 ◆ the difference between squared mass of the mass m2 ∆m31 2 2 states - the sign of ∆m is still unknown, ∆m21 32 2 2 what is the mass hierarchy? m1 m3 ? ? ◆ the CP phase factor - is the CP symmetry broken? 0 0 ? P (¯να → ν¯β) = P (νβ → να) = P (να → νβ) C + − ■ What we need to know: charge conjugation P ◆ the distance traveled by a neutrino + + parity transformation ◆ neutrino energy - this is a problem T + + time reversal Tomasz Golan NuWro @ HEP UW 7 / 61 How to measure neutrino oscillation? Neutrino source ν Detector Introduction e Neutrino properties ′ PMNS matrix Probability of oscillation Neutrino oscillation α/α Measurement idea να Example: T2K Energy reconstruction νµ ντ MC generators NuWro Final state interactions MB NCEL analysis Backup slides Disappearance method Number of α-flavor Number of α-flavor neutrinos charged leptons Appearance method Number of α-flavor Number of α′-flavor neutrinos charged leptons Tomasz Golan NuWro @ HEP UW 8 / 61 Example: T2K Target Proton beam Decay volume π + α νµ beam 295 km να 41.4 m µ + 2.5o 39.3 m ■ Off-axis beam ■ Cherenkov detector (Super-Kamiokande) ■ 50 000 tons of ultra-pure water ■ Only charged leptons and final state charged hadrons are visible ■ The neutrino energy is unknown Tomasz Golan NuWro @ HEP UW 9 / 61 The problem with neutrino energy reconstruction ′ ′ Introduction l(k ) p(p ) Neutrino properties PMNS matrix Probability of oscillation In the case of neutrino scattering off Neutrino oscillation Measurement idea nucleon at rest neutrino energy can be Example: T2K W Energy reconstruction calculated from lepton kinematics: MC generators ν(k) n(p) NuWro Final state interactions 2 ′2 ′ 2 Mp = p =(p + k − k ) Quasi-elastic scattering MB NCEL analysis Backup slides 2 2 2 Mp = ml + Mn − 2MnEl k = (Eν, 0, 0,Eν) + 2Eν(Mn − El + |~pl| cos θl) ′ k = (El, ~pl) 2 2 2 p = (Mp, 0, 0, 0) Mp − Mn − ml + 2MnEl ′ Eν = p = p + q 2(Mn − El + |~pl| cos θl) q = k − k′ Tomasz Golan NuWro @ HEP UW 10 / 61 The problem with neutrino energy reconstruction Introduction ■ Usually, the energy reconstruction procedure is based on Neutrino properties PMNS matrix quasi-elastic neutrino-nucleon scattering: Probability of oscillation Neutrino oscillation Measurement idea Example: T2K Energy reconstruction 2 2 2 REC Mp − (Mn − EB) − ml + 2(Mn − EB)El MC generators Eν = 2(Mn − EB − El + |~pl| cos θl) NuWro Final state interactions MB NCEL analysis Backup slides Nucleus Impulse Approximation l ν θl No Fermi motion π nucleon(s) Binding Potential Tomasz Golan NuWro @ HEP UW 10 / 61 The problem with neutrino energy reconstruction Introduction ■ Usually, the energy reconstruction procedure is based on Neutrino properties PMNS matrix quasi-elastic neutrino-nucleon scattering: Probability of oscillation Neutrino oscillation Measurement idea Example: T2K Energy reconstruction 2 2 2 REC Mp − (Mn − EB) − ml + 2(Mn − EB)El MC generators Eν = 2(Mn − EB − El + |~pl| cos θl) NuWro Final state interactions MB NCEL analysis Backup slides Nucleus l Never judge an event ν θl by its final state particles! π nucleon(s) Tomasz Golan NuWro @ HEP UW 10 / 61 Monte Carlo generators Buffon’s needle problem Suppose we have a floor made of parallel strips of wood, each the same width, and we drop a needle onto the floor. What is the probability that the needle will lie across a line between two strips? Georges-Louis Leclerc, blue are good Comte de Buffon 18th century red are bad Monte Carlo without computers If needle length (l) < lines width (t): MC experiment was performed by Mario Lazzarini in 1901 by throwing 3408 2l P = needles: tπ which can be used to estimate π: 2l · 3408 355 π = = = 3.14159292 2l t · #red 113 π = tP Tomasz Golan NuWro @ HEP UW 12 / 61 From Solitaire to Monte Carlo method ■ Stanis law Ulam was a Polish mathematician ■ He invented the Monte Carlo method while playing solitaire ■ The method was used in Los Alamos, performed by ENIAC computer ■ What is a probability of success in solitaire? ◆ Too complex for an analytical calculations ◆ Lets try N = 100 times and count wins ◆ With N → ∞ we are getting closer to correct result Tomasz Golan NuWro @ HEP UW 13 / 61 MC integration (hit-or-miss method) Lets do the following integration using MC method: Introduction MC generators 1 1 2 1 Buffon’s needle problem 1 1 x 1 From Solitaire to MC f(x)dx = x dx = = Hit-or-miss method 2 2 2 4 Crude method Z0 Z0 0 Methods comparison Accept-reject y MC generators Why do we need them? ■ take a random point from The main problem Cooking generator the [0, 1] × [0, 1] square NuWro 1 1 Final state interactions ■ compare it to your f(x) f(x) = 2 x MB NCEL analysis Backup slides ■ repeat N times ■ count n points below the function ■ you results is given by 1 x 1 n n f(x)dx = P · = N N Z0 Tomasz Golan NuWro @ HEP UW 14 / 61 MC integration (crude method) Lets do the following integration using MC method once again: Introduction MC generators 1 1 2 1 Buffon’s needle problem 1 1 x 1 From Solitaire to MC f(x)dx = x dx = = Hit-or-miss method 2 2 2 4 Crude method Z0 Z0 0 Methods comparison Accept-reject ■ One can approximate y MC generators Why do we need them? integral The main problem Cooking generator b N NuWro b − a f(x)dx ≈ f(xi) 1 Final state interactions N f(x) = x Za i=1 2 MB NCEL analysis X Backup slides where xi is a random number from [a, b] ■ It can be shown that crude method is more accurate x than hit-or-miss ■ We will skip the math and look at some comparisons Tomasz Golan NuWro @ HEP UW 15 / 61 N = 100 (hit-or-miss) N = 100 (crude) 0.5 0.5 0.4 0.4 0.3 0.3 0.2 0.2 MC result MC result 0.1 0.1 0 0 0 200 400 600 800 1000 0 200 400 600 800 1000 Run number Run number N = 1000 (hit-or-miss) N = 1000 (crude) 0.5 0.5 0.4 0.4 0.3 0.3 0.2 0.2 MC result MC result 0.1 0.1 0 0 0 200 400 600 800 1000 0 200 400 600 800 1000 Run number Run number Acceptance-rejection method fmax Introduction For generating events 0.6 MC generators according to a distribution. Buffon’s needle problem 2 From Solitaire to MC 3 −x y 0.4 Hit-or-miss method f(x) = x · e Crude method Methods comparison Accept-reject 0.2 MC generators ■ Evaluate fmax ≥ max(f) Why do we need them? The main problem Cooking generator 0 Note: fmax > max(f) will 0 0.5 1 NuWro affect performance, but the x Final state interactions result will be still correct MB NCEL analysis Backup slides ■ Generate random x 0.4 ■ Accept x with P = f(x) f y max 0.2 ◆ generate a random u from [0, fmax] ◆ 0 accept if u < f(x) 0 0.5 1 x Tomasz Golan NuWro @ HEP UW 17 / 61 Monte Carlo event generators ■ Monte Carlo generators Introduction basically do two things: MC generators Buffon’s needle problem From Solitaire to MC ◆ integrate cross Hit-or-miss method Crude method section formulas Methods comparison Accept-reject MC generators ◆ Why do we need them? generate events The main problem Cooking generator using
Load more

Recommended publications
  • Introduction to Parton-Shower Event Generators
    SLAC-PUB 16160 Introduction to parton-shower event generators Stefan H¨oche1 1 SLAC National Accelerator Laboratory, Menlo Park, CA 94025, USA Abstract: This lecture discusses the physics implemented by Monte Carlo event genera- tors for hadron colliders. It details the construction of parton showers and the matching of parton showers to fixed-order calculations at higher orders in per- turbative QCD. It also discusses approaches to merge calculations for a varying number of jets, the interface to the underlying event and hadronization. 1 Introduction Hadron colliders are discovery machines. The fact that proton beams can be accelerated to higher kinetic energy than electron beams favors hadron colliders over lepton colliders when it comes to setting the record collision energy. But it comes at a cost: Because of the composite nature of the beam particles, the event structure at hadron colliders is significantly more complex than at lepton colliders, and the description of full final states necessitates involved multi-particle calculations. The high-dimensional phase space leaves Monte-Carlo integration as the only viable option. Over the past three decades, this led to the introduction and development of multi-purpose Monte-Carlo event generators for hadron collider physics [1, 2]. This lecture series discusses some basic aspects in the construction of these computer programs. 1.1 Stating the problem The search for new theories of nature which explain the existence of dark matter and dark energy is the arXiv:1411.4085v2 [hep-ph] 18 Jun 2015 focus of interest of high-energy particle physics today. One possible discovery mode is the creation of dark matter candidates at ground-based collider experiments like the Large Hadron Collider (LHC).
    [Show full text]
  • Monte Carlo Event Generators
    Durham University Monte Carlo Event Generators Lecture 1: Basic Principles of Event Generation Peter Richardson IPPP, Durham University CTEQ School July 06 1 • So the title I was given for these lectures was ‘Event Generator Basics’ • However after talking to some of you yesterday evening I realised it should probably be called ‘Why I Shouldn’t Just Run PYTHIA’ CTEQ School July 06 2 Plan • Lecture 1: Introduction – Basic principles of event generation – Monte Carlo integration techniques – Matrix Elements • Lecture 2: Parton Showers – Parton Shower Approach – Recent advances, CKKW and MC@NLO • Lecture 3: Hadronization and Underlying Event – Hadronization Models – Underlying Event Modelling CTEQ School July 06 3 Plan • I will concentrate on hadron collisions. • There are many things I will not have time to cover – Heavy Ion physics – B Production – BSM Physics – Diffractive Physics • I will concentrate on the basic aspects of Monte Carlo simulations which are essential for the Tevatron and LHC. CTEQ School July 06 4 Plan • However I will try and present the recent progress in a number of areas which are important for the Tevatron and LHC – Matching of matrix elements and parton showers • Traditional Matching Techniques • CKKW approach • MC@NLO – Other recent progress • While I will talk about the physics modelling in the different programs I won’t talk about the technical details of running them. CTEQ School July 06 5 Other Lectures and Information • Unfortunately there are no good books or review papers on Event Generator physics. • However
    [Show full text]
  • Event Generators
    Event Generators XI SERC School on Experimental High-Energy Physics NISER Bhubaneswar, November 07-27, 2017 Event Generators Session 1 Introduction IntroductionIntroduction Typical high energy event (p+p collision) and possible processes: Long Island, New York, USA Typical highTypical energy high event energy ( eventp+p collision)(p+p collision) and and possible possible processes: processes: M. H. Seymour and M. Marx, arXiv:1304.6677 [hep-ph] 1. Hard process 1. Hard process 2. Parton shower 2. Parton shower 3. Hadronization 3. Hadronization 4. Underlying event 4. Underlying event 5. Unstable particle decays 5. Unstable particle decays If we could create a model that rightly incorporates most physics processes and rightly predicts the physics results… Helps Increase the physics understanding of the high energy collisions2 and data results obtained 2 2 Event Generators – In General Long Island, New York, USA ü Computer programs to generate physics events, as realistic as could be using a wide range of physics processes. ü Use Monte Carlo techniques to select all relevant variables according to the desired probability distributions and to ensure randomness in final events. ü Give access to various physics observables ü Different from theoretical calculations which mostly is restricted to one particular physics observable ü The output of event generators could be used to check the behavior of detectors – how particles traverse the detector and what physics processes they undergo -- simulated in programs such as GEANT. 3 Need of Event Generators Long Island, New York, USA ü Interpret the observed phenomena in data in terms of a more fundamental underlying theory; Give physics predictions for experimental data analysis.
    [Show full text]
  • Isolated Electrons and Muons in Events with Missing Transverse Momentum at HERA
    Physics Letters B 561 (2003) 241–257 www.elsevier.com/locate/npe Isolated electrons and muons in events with missing transverse momentum at HERA H1 Collaboration V. Andreev x,B.Andrieuaa, T. Anthonis d,A.Astvatsatourovai,A.Babaevw,J.Bährai, P. Baranov x, E. Barrelet ab, W. Bartel j, S. Baumgartner aj, J. Becker ak, M. Beckingham u, A. Beglarian ah,O.Behnkem,A.Belousovx,Ch.Bergera,T.Berndtn,J.C.Bizotz, J. Böhme j, V. Boudry aa,J.Braciniky, W. Braunschweig a,V.Brissonz, H.-B. Bröker b, D.P. Brown j,D.Brunckop,F.W.Büsserk,A.Bunyatyanl,ah,A.Burrager, G. Buschhorn y, L. Bystritskaya w, A.J. Campbell j,S.Carona, F. Cassol-Brunner v, V. Chekelian y, D. Clarke e,C.Collardd,J.G.Contrerasg,1, Y.R. Coppens c, J.A. Coughlan e, M.-C. Cousinou v,B.E.Coxu,G.Cozzikai, J. Cvach ac, J.B. Dainton r, W.D. Dau o, K. Daum ag,2, M. Davidsson t, B. Delcourt z, N. Delerue v, R. Demirchyan ah, A. De Roeck j,3,E.A.DeWolfd,C.Diaconuv, J. Dingfelder m,P.Dixons, V. Dodonov l, J.D. Dowell c,A.Dubaky,C.Duprelb,G.Eckerlinj,D.Ecksteinai, V. Efremenko w, S. Egli af,R.Eichleraf,F.Eiselem,M.Ellerbrockm,E.Elsenj, M. Erdmann j,4,5, W. Erdmann aj, P.J.W. Faulkner c,L.Favartd,A.Fedotovw,R.Felstj, J. Ferencei j, S. Ferron aa,M.Fleischerj, P. Fleischmann j,Y.H.Flemingc,G.Fluckej, G. Flügge b, A. Fomenko x,I.Forestiak, J.
    [Show full text]
  • Introduction to Monte Carlo for Particle Physics Study
    Introduction to Monte Carlo for Particle Physics Study N. SRIMANOBHAS (Dept. of Physics, Faculty of Science, Chulalongkorn University) 1st CERN School Thailand October 4, 2010 1 วันจันทร์ที่ 4 ตุลาคม 2010 Credit where credit is due I collected (stole) most of my slide from Tomasz Wlodek (Monte Carlo methods in HEP) Concezio Bozzi (Monte Carlo simulation in Particle Physics) P. Richardson CERN-Fermilab school 2009 Geant4 school 2009 2 วันจันทร์ที่ 4 ตุลาคม 2010 Monte Carlo (MC) [From Tomasz Wlodek slides] General idea is “Instread of performing long complex calculations, perform large number of experiments using random number generation and see what happens” Problem: Calculate the area of this shape Any idea? 3 วันจันทร์ที่ 4 ตุลาคม 2010 10x10 40x40 Area = (# Hits)/(# Total) x total area 4 วันจันทร์ที่ 4 ตุลาคม 2010 History Method formally developed by John Neumann during the World War II, but already known before. It was used to study radiation shielding and distance that neutrons would likely travel through material. Von Neumann chose the codename "Monte Carlo". The name is a reference to the Monte Carlo Casino in Monaco where Ulam's uncle would borrow money to gamble. 5 วันจันทร์ที่ 4 ตุลาคม 2010 Why Monte Carlo? Monte Carlo assumes the system is described by probability density functions (PDF) which can be modeled. It does not need to write down and solve equation analytically/numerically. PDF comes from - Data driven - Theory driven - Data + Theory fitting 6 วันจันทร์ที่ 4 ตุลาคม 2010 Particle physics uses MC for (1) Detector design and optimization Complicate and huge detector Very expensive (2) Simulation of particle interactions with detector’s material (3) Physics analysis New predicted physics: SUSY, UED, ..
    [Show full text]
  • A New Monte Carlo Event Generator for High Energy Physics
    Computer Physics Communications 41 (1986) 127—153 127 North-Holland, Amsterdam A NEW MONTE CARLO EVENT GENERATOR FOR HIGH ENERGY PHYSICS S. KAWABATA National Laboratory for High Energy Physics, Tsukuba, Ibaraki, Japan Received 3 August 1985; in revised form 7 December 1985 PROGRAM SUMMARY Title of program: BASES/SPRING No. of lines in combined program and test deck: 2750 Catalogue number: AAFW Keywords: Multidimensional numerical integration, Monte Carlo simulation, event generation and four momentum vector Program obtainable from: CPC Program Library, Queen’s Uni- generation versity of Belfast, N. Ireland (see application form in this issue) Nature of physical problem In high energy physics experiments, it is necessary to generate Computer for which the program is designed and others on which events by the Monte Carlo method for the processes of interest it is operable: FACOM, HITAC, IBM and others with a in order to know the detection efficiencies. It is desirable to FORTRAN77 compiler have such a program that can generate events efficiently once we have the differential cross section of any process. It will be Computer: FACOM-M382, HITAC-M280; Installation: Na- a very powerful tool for the high energy physics experimenta- tional Laboratory for High Energy Physics (KEK), Tsukuba, list. Ibaraki, Japan Method of solution Operating system: OSIV/F4 MSP (FACOM), VOS3 (HITAC) A pair of program packages BASES and SPRING is developed for solving such a problem. By BASES, the differential cross Programming language used: FORTRAN77 section is integrated and a probability distribution is produced. By SPRING, four momentum vectors of events are generated High speed storage required: 145 Kwords each for BASES and according to the probability distribution mad~eby BASES.
    [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]
  • Event Generator Physics
    Event Generator Physics Peter Skands (CERN) Joliot Curie School 2013, Frejus, France In these two lectures, we will discuss the physics of Monte Carlo event generators and their mathematical foundations, at an introductory level. We shall attempt to convey the main ideas as clearly as possible without burying them in an avalanche of technical details. References to more detailed discussions are included where applicable. We assume a basic familiarity with QCD, which will also be covered at the school in the lectures by Y. Dokshitzer. The task of a Monte Carlo event generator is to calculate everything that happens in a high-energy collision, starting from the two initial beam particles at t ! −∞ and ending with a complicated multi-particle final state that hits an imagined dector at t ! +1 (which could, e.g., be represented by a detector simulation, such as FLUKA or GEANT, but that goes beyond the scope of these lectures). This requires some compromises to be made. General-purpose generators like Herwig, Pythia, and Sherpa, start from low-order (LO or NLO) matrix-element descriptions of the hard physics and then attempt to include the “most significant” corrections, such as higher- order matrix-element corrections and parton showers, resonance decays, underlying event (via multiple parton interactions), beam remnants, hadronization, etc. Each of the generators had slightly different origins, which carries through to the em- phasis placed on various physics aspects today: • Pythia. Successor to Jetset (begun in 1978). Originated in hadronization studies. Main feature: the Lund string fragmentation model. At the time of writing the most recent version is Pythia 8.175.
    [Show full text]
  • Leptoninjector and Leptonweighter: a Neutrino Event Generator and Weighter for Neutrino Observatories
    LeptonInjector and LeptonWeighter: A neutrino event generator and weighter for neutrino observatories R. Abbasiq, M. Ackermannbe, J. Adamsr, J. A. Aguilarl, M. Ahlersv, M. Ahrensav, C. Alispachab, A. A. Alves Jr.ae, N. M. Aminao, R. Ann, K. Andeenam, T. Andersonbb, I. Ansseaul, G. Antonz, C. Argüellesn, S. Axanio, X. Baias, A. Balagopal V.ak, A. Barbanoab, S. W. Barwickad, B. Bastianbe, V. Basuak, V. Baumal, S. Baurl, R. Bayh, J. J. Beattyt,u, K.-H. Beckerbd, J. Becker Tjusk, C. Bellenghiaa, S. BenZviau, D. Berleys, E. Bernardinibe,1, D. Z. Bessonaf,2, G. Binderh,i, D. Bindigbd, E. Blaufusss, S. Blotbe, S. Böseral, O. Botnerbc, J. Böttchera, E. Bourbeauv, J. Bourbeauak, F. Bradasciobe, J. Braunak, S. Bronab, J. Brostean-Kaiserbe, A. Burgmanbc, R. S. Bussean, M. A. Campanaar, C. Chenf, D. Chirkinak, S. Choiax, B. A. Clarkx, K. Clarkag, L. Classenan, A. Colemanao, G. H. Collino, J. M. Conrado, P. Coppinm, P. Corream, D. F. Cowenba,bb, R. Crossau, P. Davef, C. De Clercqm, J. J. DeLaunaybb, H. Dembinskiao, K. Deoskarav, S. De Ridderac, A. Desaiak, P. Desiatiak, K. D. de Vriesm, G. de Wasseigem, M. de Withj, T. DeYoungx, S. Dharania, A. Diazo, J. C. Díaz-Vélezak, H. Dujmovicae, M. Dunkmanbb, M. A. DuVernoisak, E. Dvorakas, T. Ehrhardtal, P. Elleraa, R. Engelae, J. Evanss, P. A. Evensonao, S. Faheyak, A. R. Fazelyg, S. Fiedlschusterz, A.T. Fienbergbb, K. Filimonovh, C. Finleyav, L. Fischerbe, D. Foxba, A. Franckowiakk,be, E. Friedmans, A. Fritzal, P. Fürsta, T. K. Gaisserao, J. Gallagheraj, E. Ganstera, S. Garrappabe, L. Gerhardti, A. Ghadimiaz, C.
    [Show full text]
  • CERN–2005–014 DESY–PROC–2005–001 14 December 2005 Geneva 2005
    CERN–2005–014 DESY–PROC–2005–001 14 December 2005 Geneva 2005 Organizing Committee: G. Altarelli (CERN), J. Blumlein¨ (DESY), M. Botje (NIKHEF), J. Butterworth (UCL), A. De Roeck (CERN) (chair), K. Eggert (CERN), H. Jung (DESY) (chair), M. Mangano (CERN), A. Morsch (CERN), P. Newman (Birmingham), G. Polesello (INFN), O. Schneider (EPFL), R. Yoshida (ANL) Advisory Committee: J. Bartels (Hamburg), M. Della Negra (CERN), J. Ellis (CERN), J. Engelen (CERN), G. Gustafson (Lund), G. Ingelman (Uppsala), P. Jenni (CERN), R. Klanner (DESY), M. Klein (DESY), L. McLerran (BNL), T. Nakada (CERN), D. Schlatter (CERN), F. Schrempp (DESY), J. Schukraft (CERN), J. Stirling (Durham), W.K. Tung (Michigan State), A. Wagner (DESY), R. Yoshida (ANL) Contents VI Working Group 5: Monte Carlo Tools 565 Introduction to Monte Carlo tools 567 V. Lendermann, A. Nikitenko, E. Richter-Was, P. Robbe, M. H. Seymour The Les Houches Accord PDFs (LHAPDF) and LHAGLUE 575 M.R. Whalley, D. Bourilkov, R.C. Group THEPEG: Toolkit for High Energy Physics Event Generation 582 L. Lonnblad¨ PYTHIA 585 T. Sjostrand¨ HERWIG 586 M.H. Seymour Herwig++ 588 S. Gieseke The event generator SHERPA 590 T. Gleisberg, S. Hoche,¨ F. Krauss, A. Schalicke,¨ S. Schumann, J. Winter ARIADNE at HERA and at the LHC 592 L. Lonnblad¨ The Monte Carlo event generator AcerMC and package AcerDET 596 B. Kersevan, E. Richter-Was RAPGAP 601 H. Jung CASCADE 603 H. Jung Leading proton production in ep and pp experiments: how well do high-energy physics Monte Carlo generators reproduce the data? 605 G. Bruni, G. Iacobucci, L. Rinaldi, M.
    [Show full text]
  • Summary of Workshop on Common Neutrino Event Generator Tools
    Summary of Workshop on Common Neutrino Event Generator Tools Josh Barrow1, Minerba Betancourt2, Linda Cremonesi3, Steve Dytman4, Laura Fields2, Hugh Gallagher5, Steven Gardiner2, Walter Giele2, Robert Hatcher2, Joshua Isaacson2, Teppei Katori6, Pedro Machado2, Kendall Mahn7, Kevin McFarland8, Vishvas Pandey9, Afroditi Papadopoulou10, Cheryl Patrick11, Gil Paz12, Luke Pickering7, Noemi Rocco2,13, Jan Sobczyk14, Jeremy Wolcott5, and Clarence Wret8 1University of Tennessee, Knoxville, TN 37996, USA 2Fermi National Accelerator Laboratory, Batavia, Illinois 60510, USA 3Queen Mary University of London, London E1 4NS, United Kingdom 4 University of Pittsburgh, Pittsburgh, Pennsylvania 15260, USA 5Tufts University, Medford, Massachusetts 02155, USA 6King's College London, London WC2R 2LS, UK 7Michigan State University, East Lansing, Michigan 48824, USA 8University of Rochester, Rochester, New York 14627 USA 9 University of Florida, Gainesville, FL 32611, USA 10Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA 11University College London, Gower Street, London WC1E 6BT, United Kingdom 12Wayne State University, Detroit, Michigan 48201, USA 13Argonne National Lab, Argonne IL 60439, USA 14Wroclaw University, 50-204 Wroclaw, Poland August 18, 2020 arXiv:2008.06566v1 [hep-ex] 14 Aug 2020 1 Introduction Neutrino event generators are a critical component of the simulation chains of many current and planned neutrino experiments, including the Deep Underground Neutrino Experiment (DUNE) [1] and Hyper-Kamiokande (HK) [2]. Simulating neutrino interactions with nuclei is particularly complex for accelerator-based neutrino experiments, which typically use neutrino beams with energy ranging between hundreds of MeV to tens of GeV. The dominant neutrino- nucleus interaction changes across this energy range, from relatively simple quasi-elastic scattering at the lowest energies, to deep inelastic scattering at the highest energies.
    [Show full text]
  • Event Generator Physics
    Event Generator Physics Bryan Webber University of Cambridge 1st MCnet School, IPPP Durham 18th – 20th April 2007 Event Generator Physics 3 Bryan Webber Structure of LHC Events 1. Hard process 2. Parton shower 3. Hadronization 4. Underlying event Event Generator Physics 3 Bryan Webber Lecture 3: Hadronization Partons are not physical 1. Phenomenological particles: they cannot models. freely propagate. 2. Confinement. Hadrons are. 3. The string model. 4. Preconfinement. Need a model of partons' 5. The cluster model. confinement into 6. Underlying event hadrons: hadronization. models. Event Generator Physics 3 Bryan Webber Phenomenological Models Experimentally, two jets: Flat rapidity plateau and limited Event Generator Physics 3 Bryan Webber Estimate of Hadronization Effects Using this model, can estimate hadronization correction to perturbative quantities. Jet energy and momentum: with mean transverse momentum. Estimate from Fermi motion Jet acquires non-perturbative mass: Large: ~ 10 GeV for 100 GeV jets. Event Generator Physics 3 Bryan Webber Independent Fragmentation Model (“Feynman—Field”) Direct implementation of the above. Longitudinal momentum distribution = arbitrary fragmentation function: parameterization of data. Transverse momentum distribution = Gaussian. Recursively apply Hook up remaining soft and Strongly frame dependent. No obvious relation with perturbative emission. Not infrared safe. Not a model of confinement. Event Generator Physics 3 Bryan Webber Confinement Asymptotic freedom: becomes increasingly QED-like at short distances. QED: + – but at long distances, gluon self-interaction makes field lines attract each other: QCD: linear potential confinement Event Generator Physics 3 Bryan Webber Interquark potential Can measure from or from lattice QCD: quarkonia spectra: String tension Event Generator Physics 3 Bryan Webber String Model of Mesons Light quarks connected by string.
    [Show full text]