Particle Identification Algorithms for the Medium Energy ( GeV) MINERνA Test Beam Experiment

Transcription

1 Particle Identification Algorithms for the Medium Energy ( GeV) MINERνA Test Beam Experiment Tesista: Antonio Federico Zegarra Borrero Asesor: Dr. Carlos Javier Solano Salinas UNI, March 04,

2 Contents (1)The MINERνA Experiment at Fermilab (2)Medium Energy MINERvA Test Beam experiment. (3)Tools for Data Analysis & Particle ID (4)Results on the composition (% p ±, π ±, μ ±, e ±) of the secondary beam for different energies & polarities (5)Efficiency-Purity analysis to find the optimum cuts to separate different species for the 2GeV sample (6)Conclusions 2

3 (1)The MINERνA Experiment at Fermilab Neutrino-Nucleon interactions & Neutrino Oscillation Experiments. Many Interaction-channels with different Cross-Sections at different Energies. Particles in the final state have a Specific-Pattern of depositing Energy inside the MINERνA main detector. Their Identification is important for the Reconstruction of the specific Event. Calculation of Cross-Sections. 3

4 The NuMI Beam & the MINERvA Main Detector 4

5 Data Acquisition (DAQ) 5

6 Energy Dependence of Neutrino Interactions 6

7 Some examples of Neutrino Interactions CCQE: 7

8 Looking them in Arachne... 8

9 RES production of a single π: 9

10 (2) Medium Energy MINERvA Test Beam experiment. Main Goal of the Medium Energy Test Beam experiment. Why? ---> Test MC simulations How? ---> Constructing up and analyzing a Beam Beamline elements: 10

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12 Test Beam Detector Configurations Pion & Electron Data Samples (Folders) Advantage of the ECAL/HCAL configuration Different species ---> different behavior inside different Regions of the Detector (Energy deposition pattern, Number of Hits, etc...) 12

13 Time of Flight Device Elements making up the 2 Stations (Upstream & DOWNstream) How this device separates different species (different masses) Interpretation of the ToF measured-time histogram Limitations (Resolution: at E > 8GeV & particles inside the Pion-peak) 13

14 Early Result (ToF histogram) from its usage (Data from February 2015): 14

15 Limitations in the resolution of the ToF Resolution (ToF): ps Considering a momentum as low as 1 GeV/c: At E>= 8GeV other process (DIS) dominates neutrino-int. 15

16 (3) Tools for Data Analysis & Particle ID ROOT via C++ or python (concepts of DST, Chain, Tree, Branch) Ways to perform analysis: Monte Carlo simulations, Scatterplots, physical criteria Energy Deposition Patterns: Ionization (de/dx), Electromagnetic Showers & Hadronic Showers Visualization of Events (for eye-scanning) via Arachne (a software developed by MINERvA) 16

17 Interaction of particles with matter 17

18 Ionization 18

19 Electromagnetic Showers 19

20 Hadronic Showers 20

21 Radiation & Interaction lengths in our Detector. 21

22 Tracks in the TB Detector seen in Arachne for a given view (XZ) 22

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24 Mandatory Conditions to retain meaningful Events (==Particles) The Beam has to be ON: There is Activity in the Detector: All 6 PMTs of ToF Stations fire: The Veto does not fire: The Event occurs in the Triggered Slice: Relevant to know what Veto Branch to use (Veto Sanity Check) & an Analysis of Correlations between the ToF & the Veto -----> 1 Event == 1 Particle > Start Isolating Species! 24

25 What is a Slice? 25

26 Methodology for Particle Isolation In the ToF_measured_time histograms: 1)Isolate the protons 2) Eye-Scanning Events in Contamination-Intervals 3)Isolate Events in the ToF Pion-peak (containing pions, muons & some electrons). 4)To Isolate Species inside the ToF Pion-peak (definition of Detector-Variables) For E >= 4GeV a cut in the histogram of Total-Energy deposited For the 2GeV sample we need to cut on more than 1 Variable! 26

27 To separate Species (e, µ, π) inside the ToF π-peak: MC simulations of pure µ to find what Variable separates them better (µ are easier to locate). Definition of many Detector Variables to see which one works better (via python dictionaries of de/dx, PE & Hits per module). How to look at any electron. 27

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30 (4)Results on the composition (% p ±, π ±, μ ±, e ±) of the secondary beam for different energies & polarities Methodology used for the 8 GeV π + sample (the same for the 4 & 6 GeV for both + & - polarities ) 30

31 Separating species in the ToF Pion-peak 31

32 Patterns of Isolated species (from Data) & Pure species (Monte Carlo) 32

33 The Relevant-Intervals in all histograms used for the isolation (in the ToF & Total-E) & how e+ are located (and counted) are detailed. The same criteria was used for the 6 & 4 GeV samples. Here Results for the 8GeV π+: 33

34 Methodology used for the 2 GeV π + sample -More than 1 Var to separate species in the Pion-peak 34

35 For separating µ from π in the ToF Pion-peak Not possible to rely on 1 single variable to separate µ from π 5 different kinds of cuts combining different variables The initial Logic was to look at π by knowing where µ are located These are the cuts that look at π (UNIONS) : 35

36 Let's see how was made CUT-1 (for example) with the aid of MC simulations of µ (union of different cuts were used to look at π): 36

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40 So the particular cut called CUT-1 is a UNION of cuts of the Energy deposited in different regions of the detector & looks at π. 40

41 Results for All Energies & Polarities These results are estimations because there will never be perfect PID algorithms. For the 2GeV samples the specific CUT-i used are shown. 41

42 (5)Efficiency-Purity analysis to find the optimum cuts to separate different species for the 2GeV sample The Cuts used for the 2GeV samples: composed of a UNION of cuts over different variables (5 per kind of Var). For Data Analysis: Reduce Number of Cuts -> Reduce Systematic Uncertainties. The IDEA: construct an OPTIMUM-CUT composed of only 2 cuts (among the 20 Variables) For this reason an Efficiency-Purity Analysis was reliable To analyze each of the cuts & the effect of one after another --> CHANGE IN THE LOGIC needed. This will look at µ instead of π 42

43 Change in the Logic to look at µ 43

44 For the MC an extra condition for the PE for the Hits for any Event was needed... 44

45 The Analysis began while looking for the Best Variable to separate µ from π 45

46 Concepts of Efficiency & Purity 46

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50 Best cuts to separate µ from π. Plot histograms of other Vars for events in the µ-interval (for the best cut) ---> To find the Best second cut. 50

51 Methodology followed Select the Best-Cut (let's call it in Variable Events in the µ-interval ( ). ) to retain Fill histograms of the other variables with the previous Events to see which variable (let's call it ) separates better the µ & π present there. Select remaining µ in the new µ-interval ( ) of this new histogram. 8 candidates were selected as the second cut (to add to the one cited above) and the most efficient (in selecting µ) among them was chosen. 51

52 New-Logic of the Cuts : Cut_µ =={ Cut_π == (*)=={ } } == ~ Cut_µ Applying these cuts to the MC samples of pure µ & π we can find the efficiencies: Fraction of µ looking as µ(pass the Cut_µ): Fraction of µ looking as π: The same for the case of π: The best cut was chosen as the one which maximizes 52

53 Candidate to be the Var 53

54 In next slides: histograms of the variable for events in which For each case: new µ-interval the four numbers: The 8 candidates for (8 candidates) is shown, together with are: Total_E Total_E_HCAL Total_PE Total_PE_HCAL <de/dx>_total <de/dx>_hcal Total_Hits_HCAL Total_Hits_L8P 54

55 Due to its highest efficiency, the 2nd variable was chosen to be Total_PE_HCAL 55

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57 Application (of this cut == composition of 2 cuts) to Data Relations : 57

58 A similar procedure for the Cut (only 1 cut in variables of type Var_i_ß is enough) that separate e from µ : (Here just results of the best cuts found) 58

59 Type of cut refers to any of the 20 sub-cuts to separate e from µ 59

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62 Some notes about e-µ separation Many good cuts to separate e from µ. Many cuts in LP-Vars are almost perfect. We expect that electrons will almost never arrive at the LP so this is physically expected. I believe that the best-cut (Hits_L8P) is enough for a very good separation. The best cuts to separate any e that may be in a µ sample would be the ones with highest values of Eff*Pur: 62

63 & for the Cut (again an intersection of 2 cuts ) to separate e from π 63

64 The Procedure for the PID would be Considering that: *Cut_i_j= Cut to separate species i from j *Cut_j_i = ~ Cut_i_j & that the cuts Cut_i_j are already known: ===> the way to do PID for π & e Folders of DR1 is: 64

65 The way to apply the Tool 65

66 (6)Conclusions Estimations on the Composition of the Secondary Beam as well as Efficient Tools for the Identification of specific kinds of particle species. Importance: For MINERvA (To PID particles in its ECAL/HCAL region in order to reconstruct Events), the Test Beam (to test the efficiency of its beamline elements), the Accelerator Division & for comparisons with a MC simulation of the secondary beam (in progress). The usage of the variables Var_i_ß have proven to be useful and agreed with the physical expectations. There will not be perfect PID algorithms The Efficiency-Purity analysis permits to look at any kind of particle species that we think may be present in the secondary beam The importance of my work is to show estimations and a specific way to proceed in order to make up the Tool for particle isolation, since Data is not full calibrated yet. 66