Detection of electromagnetic showers along muon tracks Salvatore Mangano (IFIC)

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)