detection of electromagnetic showers along muon tracks salvatore mangano (ific)
TRANSCRIPT
Detection of electromagnetic showers along muon tracks
Salvatore Mangano (IFIC)
Muon energy lossEnergy loss ~ a + bE
Below 1 TeV: Continuous energy loss
Above 1 TeV: Discrete energy loss
Large energy fluctuation Electromagnetic showers
1. Do we see showers?2. Is number of showers correlated with energy?
waterwater
total
ionisation
pair production bremsstrahlungphotonuclear
Shower Identification Method
Muon emits: continuously Cherenkov photons and sometimes discrete electromagnetic showers
Project photons onto reconstructed muon track=>Search for clusters
Goals
• Study shower multiplicity
• Additional input for energy estimators
• Distinguish event topologies
Algorithm1. Reconstruct muon track
2. Calculate photon emission positionsPhotons with early arrival times (|20 ns|):
Calculate photon vertex assuming emission underCherenkov angle
Photons with late arrival times (20-250 ns):
Calculate photon vertex assuming spherical emission
3. Search shower candidates with a peak finding algorithm
MC simulation
Full detector simulation including realistic optical background
• Primary energy range 1 to 10^5 TeV (Corsika)
• Down going (between vertical and 85 degrees)
• Horandel model
• Hadronic interaction model QGSJET
• At detector: Resulting muon energy range 1 to 10^5 GeV
SelectionMuon selection
Muon track length L>125m
Shower selection
Hard cuts (high purity):
10 hits in 10m distance interval along track
Soft cuts (high efficiency):
5 hits in 20m distance interval along track
at least 5 hits from different floors (reduce fake showers)
Photon emission along MC muon track
all reconstructed emission points of the photons on muon trajectory
hits selected by the algorithm
positions of generated showers along the muon direction
Use MC to quantify performance of shower reconstruction
MC study: muon and shower energy
Average muon energy: Average shower energy: All: 1.2 TeV 160 GeVSoft: 2.4 TeV 200 GeVHard: 3.2 TeV 460 GeV
Shower efficiency and purity
Algorithm starts to be efficient for showers with energies above 1 TeV with reasonable purity
Shower charateristics
Light deposit of showersMore light => higher shower energy
Number of showersMore showers=>higher muon energy
Shower multiplicity for different primary models
Different models =Different energy spectrum
All models normalized to one
Challenging task to distinguish primary models
¨´¨
Shower multiplicity
MC shows (Horandel):
Shower energy 0.5TeVMuon energy with shower 3.7TeVPosition resolution 5mShower Efficiency 5%Shower Purity 70%
No reconstruction efficiency used
Tested for 2007 data (47 days of livetime)
Main systematic errors:Water absorption lengthPMT acceptance
ConclusionAnalysis idea:
project photons onto reconstructed muon track
search for clusters
=> identification of showers along muon track
Goals of ongoing analysis:
• shower multiplicity to distinguish different primary models
• input to energy estimator
Back up slide
Position resolution
Hit efficiency and purity
Downgoing muon (5 lines)Detected photon Used in fit
Result of muon reconstruction
Flat distribution of photons on muon trajectory
Downgoing muon with shower
Peak=Shower position on muon trajectory
Result of the 3D shower reconstruction
Shape of number of showers
20, 0, 1, 2,
21, 1, 2, 3,
(1 ) (1 ) ....
(1 ) (1 ) ....
rec gen gen gen
rec gen gen gen
n n n n
n n n n
entries with 0 rec. shower
entries with 0 gen. shower
entries with 1 gen shower times efficiency not to detect a shower
entries with 2 gen. showers times (efficiency not to detect a shower)
Driven by Binomial formula!
2
(Works only for high purity)