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

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Presentation on theme: "Detection of electromagnetic showers along muon tracks Salvatore Mangano (IFIC)"— Presentation transcript:

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

2 Muon energy loss Energy 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? water total ionisation pair production bremsstrahlung photonuclear

3 Shower Identification Method Muon emits: continuously Cherenkov photons and sometimes discrete electromagnetic showers Project photons onto reconstructed muon track =>Search for clusters

4 Goals Study shower multiplicity Additional input for energy estimators Distinguish event topologies

5 Algorithm 1. Reconstruct muon track 2. Calculate photon emission positions Photons with early arrival times (|20 ns|): Calculate photon vertex assuming emission under Cherenkov angle Photons with late arrival times (20-250 ns): Calculate photon vertex assuming spherical emission 3. Search shower candidates with a peak finding algorithm

6 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

7 Selection Muon 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)

8 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

9 MC study: muon and shower energy Average muon energy: Average shower energy: All: 1.2 TeV 160 GeV Soft: 2.4 TeV 200 GeV Hard: 3.2 TeV 460 GeV

10 Shower efficiency and purity Algorithm starts to be efficient for showers with energies above 1 TeV with reasonable purity

11 Shower charateristics Light deposit of showers More light => higher shower energy Number of showers More showers=>higher muon energy

12 Shower multiplicity for different primary models Different models = Different energy spectrum All models normalized to one Challenging task to distinguish primary models ¨´ ¨

13 Shower multiplicity MC shows (Horandel): Shower energy 0.5TeV Muon energy with shower 3.7TeV Position resolution 5m Shower Efficiency 5% Shower Purity 70% No reconstruction efficiency used Tested for 2007 data (47 days of livetime) Main systematic errors: Water absorption length PMT acceptance

14 Conclusion Analysis 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

15 Back up slide

16 Position resolution

17 Hit efficiency and purity

18 Downgoing muon (5 lines) Detected photon Used in fit Result of muon reconstruction Flat distribution of photons on muon trajectory

19 Downgoing muon with shower Peak=Shower position on muon trajectory Result of the 3D shower reconstruction

20 Shape of number of showers 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)


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