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Particle Identification Algorithms for the Medium Energy ( 1.5-8 - - PowerPoint PPT Presentation

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Particle Identification Algorithms for the Medium Energy ( 1.5-8 GeV) MINERA Test Beam Experiment Tesista: Antonio Federico Zegarra Borrero Asesor: Dr. Carlos Javier Solano Salinas UNI, March 04, 2016 1 Contents (1)The

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Tesista: Antonio Federico Zegarra Borrero Asesor: Dr. Carlos Javier Solano Salinas

“Particle Identification Algorithms for the Medium Energy ( 1.5-8 GeV) ∼ MINERνA Test Beam Experiment”

UNI, March 04, 2016

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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
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(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.
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The NuMI Beam & the MINERvA Main Detector

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Data Acquisition (DAQ)

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Energy Dependence of Neutrino Interactions

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  • CCQE:

Some examples of Neutrino Interactions

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Looking them in Arachne ...

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  • RES production of a single π:
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(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:
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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

  • f Hits, etc...)
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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)
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  • Early Result (ToF histogram) from its usage (Data from February 2015):
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Limitations in the resolution of the ToF

  • Resolution (ToF): 100-200 ps
  • Considering a momentum as low as 1 GeV/c:
  • At E>= 8GeV other process (DIS) dominates

neutrino-Int.

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(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)

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Interaction of particles with matter

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Ionization

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Electromagnetic Showers

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Hadronic Showers

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Radiation & Interaction lengths in our Detector.

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

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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 !
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What is a Slice ?

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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

  • f 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 !

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  • 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.

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(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 )

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  • Separating species in the ToF Pion-peak
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  • Patterns of Isolated species (from Data) & Pure species

(Monte Carlo)

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  • 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+:

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

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  • 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):

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  • So the particular cut called CUT-1 is a UNION of cuts
  • f the Energy deposited in different regions of the

detector & looks at π.

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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.
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(5)Efficiency-Purity analysis to find the

  • ptimum 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

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Change in the Logic to look at µ

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

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

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Concepts of Efficiency & Purity

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  • 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.

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Methodology followed

  • Select the Best-Cut (let's call it in Variable ) to retain

Events in the µ-Interval ( ).

  • 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
  • ne cited above) and the most efficient (in selecting µ)

among them was chosen.

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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
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Candidate to be the Var

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  • In next slides: histograms of the variable (8 candidates)

for events in which

  • For each case: new µ-interval is shown, together with

the four numbers:

  • The 8 candidates for are:

– Total_E – Total_E_HCAL – Total_PE – Total_PE_HCAL – <dE/dx>_Total – <dE/dx>_HCAL – Total_Hits_HCAL – Total_Hits_L8P

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

Total_PE_HCAL

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

  • Relations :
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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)

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

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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:

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

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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:
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The way to apply the Tool

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(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

  • f 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.