0 50 100 150 200
Cell #
0 50 100 150 200 250 300 350 400 450 500
Muon Electron Run: 13913, Subrun: 31, Evt: 30
E(e) = 0.00 GeV ) = 0.89 θz cos(
0 50 100 150 200
Cell #
0 50 100 150 200 250 300 350 400 450 500
Plane#
0 50 100 150 200
Energy deposit
0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2
0 50 100 150 200
Cell #
0 50 100 150 200 250 300 350 400 450 500
Track ) = 0.89 θz
cos(
0 50 100 150 200
Cell #
0 50 100 150 200 250 300 350 400 450 500
Plane#
0 50 100 150 200
Energy deposit
0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2 0.22
0 50 100 150 200
Cell #
0 50 100 150 200 250 300 350 400 450 500
Slice
0 50 100 150 200
Cell #
0 50 100 150 200 250 300 350 400 450 500
Plane#
0 50 100 150 200
Energy deposit
0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 0.18 0.2 0.22 0.24
Figure 4.4: Extracted Brem hits in bluefrom muon track in Fig.4.2after shower finding and muon removal is applied. Track (middle) and slice (right) hits are also shown. Green
dash lines show the 1 MIP and 2 MIP mark in a plane.
of muon hits energy (σ =∼7%) which are left behind after the muon removal algorithm is applied. Fig.4.8 shows the same event as in Fig.4.3after muon hits are removed.
In Fig. [4.5, 4.6] are shown the vertex distributions Y vs X, Y vs Z, X vs Z, and X, Y, Z of Brem candidates in the detector.
4.4 Data/MC Comparison
4.4.1 EM Shower Reconstruction
The brem shower extracted hits are put through the standard reconstruction forνeanalysis as described in the previous chapter. After shower reconstruction, the data and MC comparison variables are plotted, as shown in following sections.
X (cm)
−500 0 500
Events
5000 10000 15000
Y (cm)
−500 0 500
Events
0 5000 10000 15000 20000
Z (cm)
0 1000 2000 3000 4000 5000
Events
6000 7000 8000 9000
Figure 4.5: X, Y and Z distribution of Brem candidates in the far detector.
200 400 600
X (cm)
−500 0 500
Y (cm)
−500 0 500
100 200 300
X (cm)
−500 0 500
Z (cm)
0 1000 2000 3000 4000 5000
200 300 400
Y (cm)
−500 0 500
Z (cm)
0 1000 2000 3000 4000 5000
Figure 4.6: Y vs X, Y vs Z and X vs Z distribution of Brem candidates in the far detector.
4.4. Data/MC Comparison 79
Figure 4.7: Fraction of muon hits left behind after muon removal. From [46].
Figure 4.8: Extracted bremsstrahlung EM shower hits of muon of Fig.4.3after shower finding and muon removal algorithms are applied.
Shower Energy (GeV)
0 2 4 6 8 10
Events
0 20000 40000 60000 80000
Data MC
Shower Energy (GeV)
0 2 4 6 8 10
DT/MC
0 0.5 1 1.5 2
Data/MC
Figure 4.9: Energy distribution of Brem (left) and ratio of Data/MC as a function of shower energy (right).
θbeam
0.5 0.6 cos0.7 0.8 0.9 1
Events
5000 10000 15000 20000 25000
Data MC
θbeam
0.5 0.6 cos0.7 0.8 0.9 1
DT/MC
0 0.5 1 1.5 2
Data/MC
Figure 4.10: Angular distribution of Brem (left) and ratio of Data/MC as a function of shower angle with respect to beam direction (right).
4.4.2 Data and MC Comparison
This section contains the data and MC comparison plots for the bremsstrahlung EM shower. Fig.4.9 shows the reconstructed energy distribution of Brem EM shower in data and MC. It is clearly seen that data and MC are in very good agreement in the energy range (1 - 3 GeV) relevant toνesignal energy. However, a difference was observed in the data and MC for the angular distribution as shown in Fig.4.10. Fig.[4.11 - 4.13] shows the data and MC distribution and comparison for various shower variables, for which a very good agreement between data and MC is observed.
4.4. Data/MC Comparison 81
Shower Width (cm)
0 2 4 6 8 10
Events
0 10000 20000 30000 40000
50000 DataMC
Shower Radius
0 2 4 6 8 10
DT/MC
0 0.5 1 1.5 2
Data/MC
Figure 4.11: Bremsstrahlung shower radius distribution (left) and its data/MC com- parison (right). The shower radius is defined as the average distance from shower cells
to shower core weighted by cell energy.
Shower Length (cm)
0 200 400 600 800 1000
Events
10000 20000 30000
40000 Data
MC
Shower Length
0 100 200 300 400 500 600
DT/MC
0 0.5 1 1.5 2
Data/MC
Figure 4.12: Length distribution of Brem (left) and and its data/MC comparison (right).
Number of Planes
0 20 40 60 80 100
Events
0 10000 20000 30000
Data MC
Plane
0 20 40 60 80
DT/MC
0 0.5 1 1.5 2
Data/MC
Figure 4.13: Planes distribution of Brem (left) and and its data/MC comparison (right).
γ Longitudinal Likelihood e -
−1 −0.5 0 0.5 1
Events
0 10000 20000 30000
Data MC
γ Transverse Likelihood e -
−4 −2 0 2 4
Events
0 5000 10000 15000 20000 25000
Data MC
Figure 4.14: Longitudinal (left) and transverse (right) likelihood differences between electron andγparticle hypothesis using Brem sample. Brem shower is clearly identified
as electron type thanγ shower in transverse and in longitudinal direction.
4.4.3 Likelihood Differences for Brem EM Shower
The likelihood based particle identification (LID) is an artificial neural based algorithm in which various likelihoods are fed as an input to selectνe-CC signal. In this section, likeli- hood differences are shown for various particle hypothesis. Likelihoods are constructed in two directions: longitudinal likelihood which is along the direction of shower and trans- verse likelihood which is along the transverse direction of the shower. After that, likelihood differences are plotted. For an event, if likelihood difference (say between two particlesa and b) is toward the positive axis, then the event is more likely to bebtype or vice versa.
Fig.4.14 shows the plot of likelihood difference between eand γ and we can see that the peak in the plot is toward the positive side, which indicates that Brem events are more likely to be electron type than γ type. This is the first indication of similarity between Brem EM shower and νe-CC electron EM shower.
Fig.4.15 shows the plot of likelihood difference between eand µ and we can see that the peak in the plot is clearly toward the positive side, which indicates that Brem events are more likely to be electron type than µ type. This indicates that the Brem sample, extracted fromµ, has clear distinction from its source, i.e., muon.
Similarly Fig.4.16 shows the plot of likelihood difference between e and π0 and we can see that the peak in the plot is toward the positive side in transverse likelihood than longitudinal likelihood. This is obvious due to the fact that π0 → γγ and it has two
4.4. Data/MC Comparison 83
µ Longitudinal Likelihood e -
−4 −2 0 2 4
Events
0 10000 20000 30000 40000
Data MC
µ Transverse Likelihood e -
−4 −2 0 2 4
Events
0 20000 40000 60000
Data MC
Figure 4.15: Longitudinal (left) and transverse (right) likelihood differences between electron and µ particle hypothesis. Brem shower is clearly identified as electron type
thanµin transverse and in longitudinal direction .
π0
Longitudinal Likelihood e -
−1 −0.5 0 0.5 1
Events
0 10000 20000 30000 40000
50000 Data
MC
π0
Transverse Likelihood e -
−2 −1 0 1 2
Events
0 5000 10000 15000 20000
Data MC
Figure 4.16: Longitudinal (left) and transverse (right) likelihood difference between electron and π0 particle hypothesis. Brem shower is more identified as electron type thanπ0shower in transverse direction. However in longitudinal direction, the separation
is less.
shower prongs and because of two shower prongs, the shower is wider (νe-CC electron has single shower and so is Brem) in transverse direction and thereby the distinction is more in comparison with single shower produced in νe-CC interaction.
4.4.4 Particle-ID Classification for Brem EM Shower
As mentioned earlier, the particle-ID LID, takes the input of aforesaid likelihood difference to classify a particle. Fig.4.17 shows the likelihood based identifier (LID) output using Brem sample. As can be seen very clearly that it peaks toward the high signal region (around LID value 1) which implies that our sample is e-type.
LID
0 0.5 1
Events
0 20000 40000 60000
Data MC
Figure 4.17: LID output. Most of the brem showers are identified as νe showers.
We also process our sample through another particle-ID, LEM and CVN and Fig.4.18 and Fig.4.19show the output of the LEM and CVN respectively. Since these particle IDs are made for beam events, unlike LID, LEM and CVN use the harder cuts on directionality and energy of νe events. Due to this reason, the Brem sample events are classified more as background type by LEM and CVN.
To address the above issue, we have developed a reweighing method to make the Brem shower sample equivalent to νe shower by constructing matrix, from a bin by bin comparison of brem and νe-CC energy and angle distribution (reweighing discussed in sections ahead). We will also see how after applying reweighing method, LEM starts to classify more Brem events (Fig.4.18) as e- type.