overview : high - energy computing

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Overview: high-energy computing • How do the raw signals become analyzable data? • How do we “fake” the LHC (turn theoretical models into mock detector output)? • How do these analyzable events get to my workstation “so quickly?” • How are they archived? • How do I extract parameters from event records? How do I condense petabytes of data into a picture? production operatio ns analy sis

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Overview : high - energy computing. How do the raw signals become analyzable data? How do we “ fake ” the LHC ( turn theoretical models into mock detector output)? How do these analyzable events get to my workstation “so quickly ?” How are they archived ? - PowerPoint PPT Presentation

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Page 1: Overview :  high - energy computing

Overview: high-energy computing

• How do the raw signals become analyzable data?

• How do we “fake” the LHC (turn theoretical models into mock detector output)?

• How do these analyzable events get to my workstation “so quickly?”

• How are they archived?

• How do I extract parameters from event records? How do I condense petabytes of data into a picture?

production

operations

analysis

Page 2: Overview :  high - energy computing

Analysis with ROOT

• How do I extract parameters from event records? How do I condense petabytes of data into a picture?

analysis

Page 3: Overview :  high - energy computing

Data is stored in many formats…

trigger convert

MERGE&

derive

bytestream

reconstruction

esd

aod

tag

D3PD

aod

RDO(raw)

pattern recognition

fast pattern recognitionevent sorting

more sorting

note:you’ll see these acronyms often…

Page 4: Overview :  high - energy computing

Simulation: treated the same way!

MERGE&

deriveRECO

esd

aod

tag

D3PD

aod

MONTE CARLO PRODUCTION CHAINRDO(raw)

Page 5: Overview :  high - energy computing

The event format:

muon

calorimeterclusters

tracks

electron

metadata

Page 6: Overview :  high - energy computing

The event format in software

Organized by event(quadrillions witnessed in 2012!)

• event-wide information– Examples: metadata; sum

of measured transverse energy

• physics object lists:– muons, electrons, taus…– tracks of charged particles– “truth” particles…

• object properties: • e.g. “hits” on tracks

µ tracktracktracktracktracktracktrack

jetjettau

track hittrack

hit

event

2-tuple (a,b)n-tuple (a1,a2,…aN)TTree (evt, (el.px,el.py,el.pz)[el_n],UnitVector, …)

Page 7: Overview :  high - energy computing

Condensing the information

• how can we make that “jungle” look more like a Higgs boson?

Page 8: Overview :  high - energy computing

Histograms!

Pseudocode:histo = makeHisto(nbins=50, firstbin=0*GeV, lastbin=200*GeV)for thisEvent in allEvents:

if HasZ( thisEvent ):m = recoZMass( 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)

Page 9: Overview :  high - energy computing

getting to the fun part:Are you into installing/basic histogramming?• ROOT installation

• The DUKE ROOT tutorial:– exercises by Al Goshaw– help from Neil, others…

• The ATLAS ROOT tutorial:– also a nice way to try

out your new ATLAS computing account!

Are you ready for processing/understanding ATLAS data?

• TTrees, tree loops, TSelectors, python tree access

• Common homework: the Duke D3PD Dictionary