Results

In this section we will explain in detail the results of the hybrid-π⁰ samples comparison used to estimate systematic error of the NC 1π⁰ background. The method used is similar for all systematic error estimations that were made. Then the differences of the technique used for the estimation of the other systematic errors in relation to the estimation of the NC 1π⁰ sample will be presented.

4.4.1 NC 1π

⁰

background

After having constructed all hybrid-π⁰ samples we apply usual Super Kamiokande event reconstruction to these samples and compare the distributions used along the event selec-tion between the data hybrid-π⁰ sample and the corresponding Monte-Carlo sample. This allows to evaluate whether data and Monte-Carlo distributions are compatible and if their shape is similar to the expected shape for π⁰ events, which, for example, will typically present two rings classified as e-like. Differences between the shape of the hybrid-π⁰ data and Monte-Carlo samples indicate cases where it would be possible to improve either the reconstruction or the simulation to obtain better agreement between data and Monte-Carlo. This could result in the reduction of the estimated systematic errors or even of the contamination of the signal by the background.

In this section we will be presenting only the comparison of data and Monte-Carlo along event selection of the “primary hybrid-π⁰ sample”. The corresponding plots from the “secondary hybrid-π⁰sample” do not present different characteristics than those shown here and can be found at the end of this subsection.

Following the event selection, we compare the visible energy distribution of the hybrid-π⁰ samples shown in figure 4.23.

The next event selection is the number of rings. Figure 4.24 shows the ring counting likelihood of the hybrid-π⁰ samples. Even though there is statistical agreement between data and Monte-Carlo for every value of the ring counting there are some regions where there is a similar difference between hybrid-π⁰ data and Monte-Carlo, for example near ring-counting likelihood of -4. For the moment it is not possible to know if this disagree-ment is purely statistical or if there is a real reason for it. We can expect to decide in either way by improving the statistical significance of the hybrid-π⁰ sample in the future by using more electrons from atmospheric νe data when available.

The following event selection is the particle type. Figure 4.25 shows the PID likelihood of the hybrid-π⁰ samples. Even though there are some differences between hybrid-π⁰ data and Monte-Carlo, they are currently within the statistical error. We should also note that the probability that the first ring is identified as a µ ring in the hybrid-π⁰ sample is small (of about 2%) which means that any slight difference between hybrid-π⁰ data and Monte-Carlo will not strongly affect the efficiency of the event selection as most of the events are identified as electron rings.

The following event selection is the requirement that no decay electrons are observed.

Figure 4.26 shows the number of decay electrons of the hybrid-π⁰samples. By construction we expect that there are no decay-electrons observed in these samples, which is exactly what is observed.

4.4. Results

Visible energy (MeV)

0 500 1000 1500 2000 2500 3000

5000 10000 15000 20000 25000 30000 35000 40000

Data π0

Primary

h-0 MC π Primary

h-Normalized by number of events

Figure 4.23: Visible energy distribution of the primary hybrid-π⁰ data (black) and Monte-Carlo (blue) samples. The events selected by the T2K ν_e event selection have visible energy greater than 100 MeV. The error bars are calculated by taking the reuse into account.

Ring-Counting Likelihood

-15 -10 -5 0 5 10 15

0 1000 2000 3000 4000 5000 6000 7000

Data π0

Primary

h-0 MC π Primary

h-Normalized by number of events

Figure 4.24: Ring counting likelihood distribution of the primary hybrid-π⁰ data (black) and Monte-Carlo (blue) sample. The events selected by the T2K νe event selection have ring counting likelihood lower than 0 (one ring sample) and are indicated by the magenta arrow. The error bars are calculated by taking the reuse into account.

PID likelihood

-15 -10 -5 0 5 10 15

0 500 1000 1500 2000 2500 3000 3500 4000

Data π0

Primary

h-0 MC π Primary

h-Normalized by number of events

Figure 4.25: PID likelihood distribution of the primary hybrid-π⁰ data (black) and Monte-Carlo (blue) sample. The events selected by the T2K νe event selection have PID likelihood lower than 0 (e-like sample) and are indicated by the magenta arrow. The error bars are calculated by taking the reuse into account.

Number of Decay Electrons

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

0 5000 10000 15000 20000 25000 30000 35000

Data π0

Primary

h-0 MC π Primary

h-Normalized by number of events

Figure 4.26: Number of decay electrons distribution of the primary hybrid-π⁰ data (black) and Monte-Carlo (blue) sample. The events selected by the T2Kνeevent selection have 0 decay electrons. The error bars are calculated by taking the reuse into account.

4.4. Results

2) POLfit inv. mass (MeV/c

0 50 100 150 200 250 300

500 1000 1500 2000 2500 3000

Data π0

Primary

h-0 MC π Primary

h-Normalized by number of events

Figure 4.27: POLfit reconstructed π⁰ mass distribution of the primary hybrid-π⁰ data (black) and Monte-Carlo (blue) sample. The events selected by the T2Kνeevent selection have POLfit reconstructed π⁰ mass lower than 105 MeV/c² and are indicated by the magenta arrow. The error bars are calculated by taking the reuse into account.

The following event selection is the requirement that the reconstructed POLfit mass is smaller than 105 MeV/c². Figure 4.27 shows the POLfit mass distribution of the hybrid-π⁰ samples. The POLfit mass distribution has a peak at the value of theπ⁰ mass, which is to be expected from the cases where the POLfit algorithm correctly finds the lowest momentum γ ring. There is also a peak at small POLfit mass which is due to POLfit identifying in some cases the lowest momentum γ ring to be on top of the highest momentum γ ring, that is the POLfit algorithm could not find the lowestγ ring. On this plot it is also shown that there is visibly some slight difference between the behaviour of hybrid-π⁰ data and Monte-Carlo, but for further studies it is essential that we have a better statistical significance of the hybrid-π⁰samples as most differences shown could just be statistical fluctuations. Furthermore, currently theπ⁰ reconstruction is being improved by two independent groups : the LLR (France) and TRIUMF (Canada) groups. The LLR group works in the improvement of POLfit, while the TRIUMF group is recreating a π⁰ fitter. Both groups intend to improve the π⁰ reconstruction and e/π⁰ separation.

Finally, the last event selection is based on the reconstructed neutrino energy. Fig-ure 4.28 shows the reconstructed neutrino energy of the hybrid-π⁰ samples. There are some differences between hybrid-π⁰ data and Monte-Carlo, however they are concentrated at small reconstructed energy, thus do not affect the current value of the cut.

In order to understand better which event selections are responsible for the removal of π⁰ events the plot 4.29 summarizes the efficiency as function of the cut number for the primary hybrid-π⁰ samples. The efficiency of the event selection is defined by the ratio of the number of events remaining after a given cut and the original number of fully contained events that were generated in the fiducial volume. As expected from previous

plots, the two cuts that are more effective to reduce the number of π⁰ events on the final sample are the ring counting and POLfit mass cuts. This is also to be expected from the topology of the π⁰ events, given that the topological difference between a π⁰ event and an electron event is the number of rings which is studied first by ring counting and then by POLfit under the assumption that the two rings are supposed to reconstruct a π⁰. It also means that for these two cuts that we expect to have a larger disagreement between hybrid-π⁰ data and Monte-Carlo samples, as is shown in the figure 4.30 and 4.31 for the primary and secondary samples respectively.

In order to estimate the systematic error on the efficiency of the T2K event selection of π⁰ events we calculate the relative difference between hybrid-π⁰ data and Monte-Carlo efficiencies for both primary and secondary samples. Then we assume that these samples are independent from each other as we are using each sample to study a different part of the π⁰ decay kinematics and then combine these relatives differences in quadrature. Since we cannot compute simply the total error, given that the statistical error of the efficiency difference is not much smaller than the central value of the efficiency difference measured, we have propagated the errors using a toy Monte-Carlo where we add two Gaussian distributions with given values and errors. We have furthermore added in quadrature the central value of the measured difference between data and Monte-Carlo in order to obtain 11.8% systematic error of the efficiency of the T2K νe event selection for events with a π⁰ in the final state. The value of the efficiencies of the hybrid-π⁰ samples used to obtain the given systematic error are shown in table 4.2.

Table 4.2: Efficiency of T2Kνe event selection for all hybrid-π⁰ samples. The systematic error of the reconstruction efficiency of the NC 1π⁰ sample is defined by the quadratic sum of the relative difference between hybrid-π⁰ data and Monte-Carlo samples (shown in the rightmost column) and of their uncertainties. The uncertainties are calculated by taking the reuse into account. The combination of these uncertainties gives a 11.8% final uncertainty.

Sample Efficiency (%) (data-MC)/data (%)

Primary hybrid-π⁰ data 6.27±0.31 7.8±6.7 Primary hybrid-π⁰ Monte-Carlo 5.78±0.31

Secondary hybrid-π⁰ data 6.42±0.17 4.3±3.3 Secondary hybrid-π⁰ Monte-Carlo 6.14±0.14

4.4.2 other NC events with π

⁰

background

For the study of the systematic error on the selection efficiency of the “other NC with π⁰” background sample we needs to alter slightly the hybrid-π⁰ sample by adding other par-ticles to the π⁰ to have the same visible final state as the studied sample. In fact, since there is not only one particle that composes the “other NC with π⁰” background sam-ple, we have constructed a hybrid-π⁰ sample for each “final state” present on this sample for which we could construct a statistics significant sample, while we assume an ad-hoc 100% error for the remaining “final states”. In table 4.3 is shown the breakdown in “final state” of this sample where we have neglected γ and electrons with low momentum and decay-electrons as they are not visible in the events that pass the SK CCQE νe event selection.

4.4. Results

energy (MeV) ν

Reconst.

500 1000 1500 2000 2500 3000 3500 4000

h-Normalized by number of events

Figure 4.28: Reconstructedνeenergy distribution of the primary hybrid-π⁰ data (black) and Monte-Carlo (blue) sample. The events selected by the T2K νe event selection have reconstructed ν_e energy lower than 1250 MeV and are indicated by the magenta arrow.

The error bars are calculated by taking the reuse into account.

cut number νe

0 1 2 3 4 5 6 7 8

remaining efficiency0π

h-10-1 1 : In reconst. fiducial

cut

Figure 4.29: Efficiency of the T2K νe event selection for the primary hybrid-π⁰ data (black) and Monte-Carlo (blue) sample in function of the cut number. The error bars are calculated by taking the reuse into account.

cut number 1 : In reconst. fiducial

cut

h-Figure 4.30: Ratio between remaining efficiency after T2K νe event selection of the primary hybrid-π⁰ data and Monte-Carlo sample. The error bars are calculated by taking the reuse into account. 1 : In reconst. fiducial

cut

h-Figure 4.31: Ratio between remaining efficiency after T2K νe event selection of the secondary hybrid-π⁰ data and Monte-Carlo sample. The error bars are calculated by taking the reuse into account.

4.4. Results

Visible energy (MeV)

0 500 1000 1500 2000 2500 3000

5000 10000 15000 20000 25000 30000 35000

Data π0

Secondary

h-0 MC π Secondary

h-Normalized by number of events

Figure 4.32: Visible energy distribution of the secondary hybrid-π⁰ data (black) and Monte-Carlo (magenta) samples. The events selected by the T2K event selection have visible energy greater than 100 MeV. The error bars are calculated by taking the reuse into account.

Ring-Counting Likelihood

-15 -10 -5 0 5 10 15

0 1000 2000 3000 4000 5000 6000 7000

Data π0

Secondary

h-0 MC π Secondary

h-Normalized by number of events

Figure 4.33: Ring counting likelihood distribution of the secondary hybrid-π⁰ data (black) and Monte-Carlo (magenta) sample. The events selected by the T2K νe event selection have ring counting likelihood lower than 0 (one ring sample) and are indicated by the blue arrow. The error bars are calculated by taking the reuse into account.

PID likelihood

-15 -10 -5 0 5 10 15

0 500 1000 1500 2000 2500 3000

Data π0

Secondary

h-0 MC π Secondary

h-Normalized by number of events

Figure 4.34: PID likelihood distribution of the secondary hybrid-π⁰ data (black) and Monte-Carlo (magenta) sample. The events selected by the T2K ν_e event selection have PID likelihood lower than 0 (e-like sample) and are indicated by the blue arrow. The error bars are calculated by taking the reuse into account.

Number of Decay Electrons

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

0 5000 10000 15000 20000 25000 30000

Data π0

Secondary

h-0 MC π Secondary

h-Normalized by number of events

Figure 4.35: Number of decay electrons distribution of the secondary hybrid-π⁰ data (black) and Monte-Carlo (magenta) sample. The events selected by the T2K νe event selection have 0 decay electrons. The error bars are calculated by taking the reuse into account.

4.4. Results

2) POLfit inv. mass (MeV/c

0 50 100 150 200 250 300

500 1000 1500 2000 2500

Data π0

Secondary

h-0 MC π Secondary

h-Normalized by number of events

Figure 4.36: POLfit reconstructedπ⁰ mass distribution of the secondary hybrid-π⁰ data (black) and Monte-Carlo (magenta) sample. The events selected by the T2K ν_e event selection have POLfit reconstructed π⁰ mass lower than 105 MeV/c² and are indicated by the blue arrow. The error bars are calculated by taking the reuse into account.

energy (MeV) ν

Reconst.

500 1000 1500 2000 2500 3000 3500 4000

500 1000 1500 2000

2500 Secondary h-π⁰ Data

0 MC π Secondary

h-Normalized by number of events

Figure 4.37: Reconstructed νe energy distribution of the secondary hybrid-π⁰ data (black) and Monte-Carlo (magenta) sample. The events selected by the T2K νe event selection have reconstructedνe energy lower than 1250 MeV and are indicated by the blue arrow. The error bars are calculated by taking the reuse into account.

cut number νe

0 1 2 3 4 5 6 7 8

remaining efficiency0π

h-10-1

1

Data π0

Secondary

h-0 MC π Secondary

h-Cut number 1 : In reconst. fiducial

cut 2 : Evis

3 : Ring-counting 4 : Electron PID 5 : No decay-e 6 : POLfit mass 7 : Reconst. energy

Figure 4.38: Efficiency of the T2K νe event selection for the secondary hybrid-π⁰ data (black) and Monte-Carlo (magenta) sample in function of the cut number. The error bars are calculated by taking the reuse into account.

Table 4.3: Breakdown of events in the “other NC withπ⁰” sample with particles in the Monte-Carlo above Cerenkov threshold. For each final state, there is defined the number of events expected in run I+run II (and its fraction in the “other NC with π⁰” sample).

In the last column it is indicated how the error is estimated, which can be either by constructing a hybrid-π⁰ sample (in which case the name of the sample is given) either by assuming some ad-hoc 100% error.

Final state expected number of events in T2K run I+run II

systematic error

NC π⁰+π^± 0.0235 (∼ 48%) use “hybrid-π⁰+π^±” sample NC π⁰ +p 0.0171 (∼ 35%) use “hybrid-π⁰+p” sample NC π⁰ +p+π^± 0.0029 (∼ 6%) use “hybrid-π⁰+p+π^±” sample NC π⁰ + 2π^± 0.0039 (∼ 8%) assume 100% error

NC π⁰ +π⁰ 0.0005 (∼ 1%) assume 100% error NC π⁰ +π⁰+p 0.0005 (∼ 1%) assume 100% error NC π⁰ + 3π^± 0.0005 (∼ 1%) assume 100% error

4.4. Results

For each of the sub-samples we have applied the same verifications applied to the usual hybrid-π⁰ samples discussed previously, that is we have compared the shape of data and Monte-Carlo distributions along the event selection. These comparisons show compatible shapes for data and Monte-Carlo, as it was the case for the hybrid-π⁰ samples, though with poorer statistics. Furthermore, we have calculated the systematic error related to each of these sub-samples with the same method described previously and the results are shown in table 4.4. In this table is also shown the result (σ) from combining these estimated systematic errors (σi) for each sub-sample with their weight from the fraction (fi) of the sample (i) using the equation (4.1), leading to the overall estimated systematic error for the “other NC with π⁰” background sample.

σ = sX

i

f_i²σ_i² (4.1)

Table 4.4: Systematic error on the efficiency of the T2Kνe event selection for each final state defined on table 4.3. For the constructed hybrid sample, this error was estimated with the hybrid sample, while for the final states with no hybrid sample, the systematic error was assumed 100% and merged in only one line. Finally in the last line is shown the final value of the systematic error estimated for the “other NC with π⁰” sample.

Final state fraction systematic error

NC π⁰+π^± 48% 31.8%

NC π⁰+p 35% 30.7%

NC π⁰+p+π^± 6% 64.1%

remaining final states 11% ad-hoc 100%

other NC with π⁰ 100% 22.0%

4.4.3 ν

µ

CC with π

⁰

background

For the same reasons the “other NC with π⁰” background sample needed to be divided so did the “νµ CC with π⁰” background sample. The “final state” decomposition of this sample is shown in table 4.5 and the results from estimating the systematic error from adapting the hybrid-π⁰ sample is shown in table 4.6. This error estimation is done in the same way as it was explained for the “other NC events with π⁰” background.

We should note that one of the possible final states in the “νµ CC with π⁰” sample shown in table 4.5 is theνµCC π⁰, for which we had not created a specific hybrid sample.

The νµ CC π⁰ sample is composed from events where theνµ CC interaction produced aµ and aπ⁰ and for which the µmomentum was below the Cerenkov threshold and therefore did not emit light and was not added to the final state. For this final state only one π⁰ is produced as is the case for the “NC 1π⁰” sample, albeit with different π⁰ momentum distribution. In order to reproduce the νµ CC π⁰ final state and estimate the related systematic error, we have used the hybrid-π⁰ sample created for study of NC 1π⁰ and re-weighted its π⁰ momentum distribution to be that of theνµ CC π⁰ final state.

Furthermore, all verifications that were performed to the other hybrid-π⁰ samples were also performed for the samples composing the “νµ CC with π⁰” final state samples. As for the other NC events with π⁰ background samples we do not show here the data and

Table 4.5: Breakdown of events in the “νµ CC with π⁰” sample with particles in the Monte-Carlo above Cerenkov threshold. For each final state, there is defined the number of events expected in run I+run II (and its fraction in the “νµCC withπ⁰” sample). In the last column it is indicated how the error is estimated, which can be either by constructing a hybrid-π⁰ sample (in which case the name of the sample is given) either by assuming some ad-hoc 100% error.

Final state expected number of events in T2K run I+run II

systematic error

νµ CC π⁰+µ 0.00250 (∼ 50%) use “hybrid-π⁰+µ” sample ν_µ CC π⁰ 0.00108 (∼ 22%) use existing “hybrid-π⁰”

νµ CC π⁰+µ+π^± 0.00075 (∼ 15%) use “hybrid-π⁰+µ+π^±” sample νµ CC π⁰+µ+p 0.00055 (∼ 11%) assume 100% error

ν_µ CC π⁰+µ+ 2π^± 0.00005 (∼ 2%) assume 100% error

Table 4.6: Systematic error on the efficiency of the T2Kνe event selection for each final state defined on table 4.5. For the constructed hybrid sample, this error was estimated with the hybrid sample, while for the final states with no hybrid sample, the systematic error was assumed 100% and merged in only one line. Finally in the last line is shown the final value of the systematic error estimated for the “νµ CC with π⁰” sample.

Final state fraction systematic error

νµ CC π⁰+µ 50% 89.2%

νµ CC π⁰ 22% 12.3%

νµ CC π⁰+µ+π^± 15% 109.9%

remaining final states 13% ad-hoc 100%

νµ CC with π⁰ 100% 49.4%

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