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LHC computing!
HEP 101 Lecture #8 ayana arce
Outline • Major computing systems for LHC
experiments: – (ATLAS) Data Reduction – (ATLAS) Data Production – (ATLAS) Data Analysis
• End-user tools: – Exercise: plotting and fitting data with ROOT – homework: writing a toy Monte Carlo
DATA REDUCTION managing the data volume
overview: the data reduction chain
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Hardware Trigger (prefilter)
Event Filter (software event selection)
data reconstruction and distribution
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The TDAQ system • Trigger:
– (almost) real-time filtering of collision events – Events read every ~25ns:
• how long does the trigger take to decide? • DAQ:
– Sends event data through the trigger and readout systems
– Merges trigger and detector conditions data with event data
L1
• select 1/10,000 in 2.5 µs • hardware-‐based, 256 items
L2
• select 1/15 in 40 ms
L3
• read global detector data • select 1/15 in 4 seconds
Storage • similar triggers grouped: data streams
analysis • trigger data used to account for bias
local (event fragments)
~1700 nodes (8/12 core, 16/24 GB)
dedicated L3 ~10 Gb links
flexible L2/L3 processors 10 Gb links
ATLAS
full events
ATLAS trigger system
Example: electron trigger
is it an electron?
clustering tracking electron selecHon
is there a cluster of hot cells with straight tracks nearby?
clustering cluster selecHon tracking cluster/track
matching
are any EM calorimeter regions hot?
DATA PRODUCTION managing the data volume
Global data processing and storage • LHC data output estimate: 15 PB/year
(and we prefer multiple copies) – Stored and processed on WLCG:
shared by all CERN experiments – Your “local” Tier-1: BNL – Your local Tier-3: in your backpack!
• Every stored physics event is modeled by many simulated events – thus most resources are spent in
Monte Carlo simulation
note: ATLAS compuHng systems alone must handle MILLIONS of producHon/analysis jobs daily
analyze create MC
backup RAW reprocess (re-reconstruct)
store RAW calibrate reconstruct (6k cores)
Tier 0
Tier 1
Tier 2
Tier 1
Tier 1
Tier 2 Tier 2 Tier 2
Tier 2 Tier 2
Tier 2
Tier 2
Tier 2
físicos physicists
物理学者
38 T2 centers 120k cores total cernVM environment
ATLAS Tier computing: roles
Production: data ATLAS trigger convert
MERGE&
derive
bytestream
RECO
esd
aod
tag
D3PD
aod
RDO (raw)
pattern recognition
sorting
Production: Monte Carlo
MERGE &
derive
RECO
esd
aod
tag
D3PD
aod
MONTE CARLO PRODUCTION CHAIN RDO (raw)
pick random x, random y if y2 < 1-‐x2: increment area
What is Monte Carlo, really? • HEP predictions require a lot
of convolution integrals – one reason: QM!
Monte Carlo calculation of π
pick random x, random y if y2 < 1-‐x2: increment area
What is Monte Carlo? • HEP predictions require a lot
of convolution integrals – one reason: QM!
• The Monte Carlo Method: – use random numbers as
an integration tool
Monte Carlo calculation of π
this is probably the simplest way to use a computer
for a calculaHon… but it works!
What is Monte Carlo?
Z picks mass
and decay angles
electron ET
• The Monte Carlo Method: – use random numbers as an
integration tool • Very intuitive picture of convolution
integrals: – a series of choices from
probability distributions
What is Monte Carlo?
Z picks mass
and decay angles
electron ET
calorimeter (mis)measurement
observed electron ET
• The Monte Carlo Method: – use random numbers as an
integration tool • Very intuitive picture of convolution
integrals: – a series of choices from
probability distributions
Meet your (3-part) Monte Carlo
Slides: Sjöstrand
Meet your MC: #PYTHIA, HERWIG, MadGraph, MCFM, MC@NLO, BaurMC, POWHEG, &c.…
Meet your MC: #PYTHIA, HERWIG, MadGraph, MCFM, MC@NLO, BaurMC, POWHEG, &c.…
Meet your MC: #PYTHIA, HERWIG/JIMMY, Sherpa…
Meet your MC: #PYTHIA, HERWIG/JIMMY, Sherpa…
What’s the third part? • Detector simulation:
up to 5 minutes for a high-mass event (lots of particles, each individually tracked through hundreds of detector elements)
why is this essential?
DATA ANALYSIS measurements and discoveries!
ATLAS computing for users Programming languages • Main programming
languages: – FORTRAN (some generators) – C++ (main reconstruction
algorithms, analysis) – python (steering, analysis)
Interactive interfaces • Main interface: athena
– reads all data formats – C++ ; steered by python – this runsall simulation and
reconstruction – can run your analysis too…but
excecutable typically 4GB • Light interface: ROOT
Data representation • always organized by event • global quantities:
– metadata – missing energy…
• physics object lists: – muons – jets – tracks – “truth” particles…
• object properties: – hits on tracks – jet constituents
µ track track track track track track track
jet jet jet
track hit track hit
event
“n-‐tuple” “tree”
Data representation Event number
nTracks track pT track eta track phi track layers…
0 3 12.4 0.3 2.1 30
8.1 1.1 1.0 14
5.0 -‐0.9 4.0 17
1 2 24.5 1.1 0.2 22
20.5 0.9 3.3 17
2 1 2.0 1.9 1.4 5
3 5 40.4 0.1 0.8 21
… … … …
User’s interface to nature: histograms
: histo = makeHisto(nbins=50, firstbin=0*GeV, lastbin=200*GeV) for thisEvent in allEvents:
if HasZBoson( thisEvent ): m = reconstructZBosonMass( thisEvent )
histo.FillWith( m )
``Hello World’’ for HEP computing: making a histogram
TH1F::Fill(value,weight)
TH1F(“name”, “title; x title; y title”, nBins, firstBinValue, LastBinValue)
EXAMPLE! note: in code examples, your input is given in green
Let’s measure the kaon lifetime (again)!
• open the ROOT file: – you% root Hep101Data_2013.root
• How to see everything in the file: – root [1] new TBrowser();
Some ROOT features:
root [0] double x(3.0),y(4.0); sqrt(x*x+y*y) (const double)5.00000000000000000e+00 root [1] TLorentzVector pion(1500,0,0,1506.482); root [2] printf("The mass is %3.4g\n", pion.M( )); The mass is 139.6 root [3] TMath::C( <TAB> Double_t C() // m s^-1 root [4] TMath::C() (Double_t)2.99792458000000000e+08
Mathematical functions in ROOT
• Simple: FitPanel (under Tools)
• Also easy: root [9] KaonDecays->Fit(“expo”)
• More explicit: root [10] TF1 f("f","[0]*exp(-x/(100*[1]*TMath::C()))",0,60); //free
parameters specified in brackets root [11] KaonDecays->Fit(f);
• Complete program (from Dave)
Next steps • You can download ROOT:
– root.cern.ch • Homework: write your own Monte Carlo
generator to solve Problem 2 from lecture 5 a neutral pion beam with energy E decays to two
photons. What is the photon energy distribution in the laboratory frame?
• Feel free to contact [email protected] with solutions, questions, etc!!
homework hint: random numbers • Use the ROOT class TRandom3 for good
performance. • Example
– root [1] TRandom3 r; – root [2] float random1 = r.Gaus(0,35);
//generate a gaussian-distributed random number with mean 0 and width 35;
– root [3] float random2 = r.Flat(0,2*TMath::Pi());
//generate a scalar meson decay angle
Postscript: if you don’t like C++ >>> import ROOT #from ROOT import * also works >>> pion = ROOT.TLorentzVector(1500,0,0,1506.482); >>> print "The mass is", pion.M(), "MeV" The mass is 139.5994854 MeV
