HEP 101 Lecture #8 ayana arce

• 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

managing the data volume
DATA REDUCTION

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11001000101000 11001000101000 11001000101000 11001000101000 11001000101000 11001000101000 11001000101000 11001000101000
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0100001100010
101001101001
0100001100010
101001101001
0100001100010
101001101001
0100001100010
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0100001100010
101001101001
Hardware Trigger (prefilter)
10010011000001
1011010010011100
100011011010001
11001010111110
01000011111010
101001101101
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Event Filter (software event selection) data reconstruction and distribution overview: the data reduction chain

• 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 local (event fragments)
• select 1/15 in 40 ms
L2
L3
• read global detector data
• select 1/15 in 4 seconds
ATLAS full events
Storage
• similar triggers grouped: data streams analysis
• trigger data used to account for bias
~1700 nodes
(8/12 core, 16/24 GB) dedicated L3
~10 Gb links flexible L2/L3 processors
10 Gb links

are any EM calorimeter regions hot?
is there a cluster of hot cells with straight tracks nearby?
clustering cluster selection tracking cluster/track matching clustering is it an electron?
tracking electron selection

managing the data volume
DATA PRODUCTION

• 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 computing systems alone must handle MILLIONS of production/analysis jobs daily

store RAW calibrate reconstruct (6k cores)
Tier 0
Tier 1
Tier 2
Tier 2
Tier 2
  backup RAW reprocess (re-reconstruct)
Tier 1 analyze create MC
Tier 2
Tier 2
Tier 2
Tier 1
Tier 2 físicos
Tier 2
Tier 2 physicists
38 T2 centers
120k cores total cernVM environment

ATLAS
bytestream trigger convert
RDO
(raw)
RECO pattern recognition esd aod tag
MERGE
& derive sorting
D3PD aod

MONTE CARLO PRODUCTION CHAIN
RDO
(raw)
RECO esd aod tag
MERGE
& derive
D3PD aod

Monte Carlo calculation of π
• HEP predictions require a lot of convolution integrals
– one reason: QM! pick random x, random y if y 2 < 1-x 2 : increment area

Monte Carlo calculation of π
• HEP predictions require a lot of convolution integrals
– one reason: QM! pick random x, random y if y 2 < 1-x 2 : increment area
• The Monte Carlo Method:
– use random numbers as an integration tool

Z picks mass and decay angles
• The Monte Carlo Method:
– use random numbers as an integration tool
• Very intuitive picture of convolution integrals:
– a series of choices from probability distributions electron
E
T

Z picks mass and decay angles
• The Monte Carlo Method:
– use random numbers as an integration tool
• Very intuitive picture of convolution integrals:
– a series of choices from probability distributions electron
E
T calorimeter
(mis)measurement observed electron E
T

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…

• 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?

measurements and discoveries!
DATA ANALYSIS

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

• 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 event track track track track trac k hit k hit
“n-tuple”  “tree”

1
2
3
Event number
0
2
1
5
nTracks
3 track pT
12.4
8.1
5.0
24.5
20.5
2.0
40.4
… track eta
0.9
1.9
0.1
…
0.3
1.1
-0.9
1.1
track phi
3.3
1.4
0.8
…
2.1
1.0
4.0
0.2
17
5
21
…
14
17
22 track layers…
30

User’s interface to nature: histograms
``Hello World’’ for HEP computing: making a histogram
TH1F(“name”, “title; x title; y title”, nBins, firstBinValue, LastBinValue)
: histo = makeHisto(nbins=50, firstbin=0*GeV, lastbin=200*GeV) for thisEvent in allEvents: if HasZBoson( thisEvent ): m = reconstructZBosonMass( thisEvent ) histo.FillWith( m )
TH1F::Fill(value,weight)

note: in code examples, your input is given in green
EXAMPLE!

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();

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

• 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)

• 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 atarce@phy.duke.edu
with solutions, questions, etc!!

• 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

>>> 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