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CARF
an Analysis&Reconstruction
Framework for CMS
Vincenzo Innocente
CERN/EP/CMC
CARF Development Philosophy
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Tailored to CMS analysis and reconstruction
Serves present ORCA applications
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priorities driven development
backward compatibility, legacy code and data!
Bottom-up (concreteabstract) development in
synergy with other sub-systems
It is also a prototype of 2005 software
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offers more than one solution to a single problem
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Vincenzo Innocente CARF
Fundamentals
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CARF layered Structure
Core mechanisms
and “data structures”
G3
TestBeam
H2
Raw
Data
Raw
Data
Generic
Application
T9/X5
Raw
Data
Generic Clients
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Framework Basic Dynamics
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No central ordering of actions, no explicit
control of data flow: only implicit
dependencies
External dependencies managed through an
Event Driven Notification to “subscribers”
Internal dependencies through an Action on
Demand mechanism
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Vincenzo Innocente CARF
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Framework Main Services
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Define the events to be dispatched and
links them to the their actual source
Allow the selection among available
resources (user plug-in’s)
Manage the “not yet removed” sequential
components
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Framework Ancillary Services
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User Interface
Error Report (Exception management)
Logging facilities
Timing facility (statistics gathering)
Utility library
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Notably Objy utilities, wrappers and generic
persistent capable classes
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Framework middle layer
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A CARF Application is characterized by the
events it dispatches
Implementation of generic clients to specific
services (events)
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simplified API
uniform detailed design
uniform use of ancillary services
Requires synergy with detectors’ sub-systems
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Fundamentals
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Use Cases
L1 Trigger Simulation
Track Reconstruction
“Physics” reconstruction
L1 Trigger Simulation
detectors
Front-end
trigger logic
Local trigger
Global trigger
Final trigger decision
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Vincenzo Innocente CARF
Fundamentals
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L1 Trigger Simulation
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Accurate simulation of real electronics
In the real experiment only “final decision data”
are propagated forward
Also required
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Monitoring of single trigger units
Comparison of L1 trigger w.r.t. full reconstruction
ability to simulate just a part of the system
“save” computing intensive intermediate results
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Vincenzo Innocente CARF
Fundamentals
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Track Reconstruction
For each “detector element”
there are local measurements
of trajectory state-vector
(just position or more complex)
Local measurements are affected
by the detector element state
(calibrations, alignments)
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Pattern recognition “navigates”
in the detector to associate
local measurements into a track
Vincenzo Innocente CARF
Fundamentals
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“Physics” reconstruction
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4-vector-like objects are built out of
trajectories and localized energy deposits
A wide range of PID, jet, vertex etc
algorithms can be applied to produce
others 4-vector-like objects
Access to the “original” detector data
maybe required
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Vincenzo Innocente CARF
Fundamentals
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Reconstruction Sources
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Vincenzo Innocente CARF
Fundamentals
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Reconstruction Scenario
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Reproduce “Detector Status” at the moment of
the interaction:
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front-end electronics signals (digis)
calibrations
alignments
Perform local reconstruction as a continuation of
the front-end data reduction until objects
“detachable” from the detectors are not obtained
Use these objects to perform physics
reconstruction and analysis
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Fundamentals
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Components
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Reconstruction Algorithms
Event Objects
Other services (detector objects,
parameters, etc)
Legacy not-OO data (GEANT3)
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Vincenzo Innocente CARF
Fundamentals
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CARF Fundamentals
(inside the black box)
Detector Components
Event Driven Notification
Action on Demand
Detector Components
Load simulated
hits from MC
Sim Hit
Loader
Generates Digis
from SimHits
(or loads them Digitizer
from db)
Reconstruct
measured trajectory
state-vector from Digis
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Local
Geometry
Detector
Element
Reconstructor
Vincenzo Innocente CARF
Fundamentals
Global
Geometry
Time
Dependent
Parameters
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Event Driven Notification
Dispatcher
Obs1
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Obs2
Obs3
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Fundamentals
Obs4
Observers
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Active and Lazy Observers
Dispatcher
Lazy
Obs1
uptodate
obsolete
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Lazy
Obs2
uptodate
obsolete
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Fundamentals
Obs
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Action “on Demand”
Detector
Element
Rec Hits
Hits
Event
Rec T1
T1
T2
Rec T2
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Analysis
Vincenzo Innocente CARF
Fundamentals
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“Events” currently dispatched
G3
Geom
Start Xing
Ready to build new
G3 simulated event
SimPileUp
SetUp
New trigger event
SimTrigger ready in Zebra memory
SetUp Observers are Objects
which depend on geometry
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New pile-up event
ready in Zebra memory
G3Event
Vincenzo Innocente CARF
Fundamentals
New event “ready”
to be analyzed
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“RecObj” Object Model
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Object identification
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A Detector object collection is identified by:
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object type (or super-type)
detector element it belongs to
implicitly belongs to the “current crossing”
Required the detector to be “in place” and
“operational”
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Fundamentals
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Object identification
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A RecObj collection is identified by:
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object type (or super-type)
name of the Reconstructor (same as RecUnit)
event it belongs to
Does not require the RecUnit to be in
place or operational
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