Another source of systematic uncertainty originates in the difference in the reconstruction efficiency for small energy deposits between data and MC.
This difference is seen when comparing the detected energy from annihi-lation gammas produced by a 22Na source, to the MC simulation of these interactions. As mentioned before, these annihilation gammas have an en-ergy of 511 keV, which is low compared to the minimal required enen-ergy of 2 MeV for the ES signals in the IBD selection. In case all energy of an annihi-lation gamma is deposited and detected, this corresponds to a signal of∼50 PA. Most of the detected signals will however be smaller. The measured and simulated energy distributions for annihilation gammas detected in a SoLid cube are shown in figure 7.4. A discrepancy of 20% per cube or 5% per WLS fibre is found between the two distributions over the full energy range from 10 to 50 PA.
This seemingly large uncertainty can be explained by the fact that each of the fibres can only detect a few PAs for these low energy deposits. As a con-sequence, the√
Nbehaviour of statistical fluctuations causes large variations in the resulting signal efficiency as a function of the actual deposited energy.
The evaluation of the impact of this uncertainty on the reconstructed energy spectrum is just starting at the time of writing. The procedure will exist in generating toys for which 20% of the cubes with detected energies below 500 keV is randomly removed from the reconstruction of each event. For the cubes that share their row or column with the maximal amplitude cube, i.e.
that cube where the positron deposited most of its energy, the cube removal will be reduced to only 10%, as 2 out of the 4 fibres will most probably be above threshold.
7.8 Relative detection efficiency
The effect of an error on the IBD selection efficiency can act as a bias in the signal normalisation. The absolute selection efficiency uncertainty does not affect a relative oscillation analysis, but variations in this efficiency between detector modules will have an impact on a relative oscillation search and should therefore be correctly included in the MC detector simulation. There-fore, the differences in selection efficiencies between the Monte Carlo simu-lation and what is measured from calibration data need to be determined in order to derive an uncertainty interval on each effect.
The IBD selection efficiency is a combination of the neutron detection efficiency and the ES reconstruction efficiency.
178 CHAPTER 7. UNCERTAINTY PROPAGATION
23
Mor e on Dat a/MC
Anihilation gamma cube agreement : 20% efficiency/cube less Agreement @ 5%
per fibers.
PRELIMINARY PRELIMINARY
data MC
23
Mor e on Dat a/MC
Anihilation gamma cube agreement : 20% efficiency/cube less Agreement @ 5%
per fibers.
PRELIMINARY PRELIMINARY
data MC
MC Data
Figure 7.4: Data/MC comparison of the reconstructed energies for energy deposits below 55 PA (∼ 600 keV), based on the measured and simulated reconstruction of annihilation gammas from a22Na source. A discrepancy of 20% is seen for these low energetic signals.
7.9. SUMMARY 179 In figure 2.18, the neutron reconstruction efficiency, determined from cal-ibration data, was shown for each individual Phase I detector plane. From this calibration data, an average neutron reconstruction efficiency was deter-mined within 4% uncertainty. The uncertainty per detector module will be of the same order. The neutron capture efficiency might vary between modules due to geometrical differences between the outer and inner modules, but is automatically taken into account in the Monte Carlo simulation.
For the ES reconstruction, the plane-per-plane variation in the efficiency mostly depends on the variations in the average LY between planes. These variations are included in the detector simulation.
A direct way to evaluate the remaining systematic uncertainties is to look at the data-to-MC comparison of the Lreco-distribution of the excess events.
However, such a study might not be precise enough, due to limited statistics.
Another possible method is to compare the distribution of ES events over the detector planes in a BiPo data sample and in a MC sample. To match the IBD topology, a subsample of BiPo events can be made that have a cube with a reconstructed NS signal and at least one other cube that tagged a radiative gamma. This study will not cover effects due to light leaks at higher energies. To evaluate this, two IBD simulations that have different simulated light leakage values can be used.
7.9 Summary
In this chapter, we have presented the general methods used for an evalu-ation of the systematic uncertainties on the prediction for the experiment.
In a second step, we have listed the most relevant uncertainty contributors for a relative oscillation search. The statistical uncertainties are determined from the different signal and background selections and are based on Poisson statistics, as described in section 7.3. We have also seen in this section that a relative fit, that uses the measured data as a reference for the prediction, introduces correlated statistical uncertainties.
On the other hand, a relative fit removes a large part of the systematic uncertainties. For the current SoLid dataset, the dominant systematic uncer-tainties are expected to come from the background selection, the dependence of the detector acceptance on the reactor loading map, the energy reconstruc-tion and relative detecreconstruc-tion differences between detector modules. We have briefly discussed the methods that can and/or will be used to quantitatively determine these uncertainties for the first full SoLid oscillation analysis.
Results and analysis
outlook 8
In this chapter, we present a first SoLid sensitivity contour using real data, determined according to the oscillation analysis steps that were described in chapter 6. The contour will be based on the preliminary IBD excess pre-sented in chapter 5, determined from a limited open dataset containing only 1 reactor-ON cycle. As a consequence, the result will lack sensitivity and is not useful for a final oscillation search. The purpose is, however, to demon-strate the readiness of the oscillation analysis code for the study of the full Phase I dataset. In addition, we summarise the remaining steps and studies that are required to produce the full Phase I dataset exclusion contour.
8.1 Current status
As mentioned, the SoLid collaboration is currently still working on the valida-tion of the IBD analysis techniques and the full Phase I dataset is not available yet for further analysis. For the production of a first exclusion contour with real SoLid data, the same limited dataset is used as the one for which a first IBD excess study was presented in chapter 5. This set includes 25 days of reactor-ON data, taken during the third BR2 reactor cycle of 2018. The goal is to evaluate the implementation of the oscillation analysis machinery, which was my dominant contribution to the SoLid analysis.
The input for the oscillation fit is the measured Lreco versus Ereco distri-bution of the IBD signal excess, which is shown in figure 8.1. All statistical uncertainties were propagated during the event selection and background subtraction procedures and are stored per histogram bin. A projection of these uncertainties is shown in figure 8.2. Systematic uncertainties are not taken into account for this study, as they are negligible compared to the sta-tistical uncertainties on this limited dataset.
181
182 CHAPTER 8. RESULTS AND ANALYSIS OUTLOOK
0 10 20 30 40 50
(plane) Lreco
2 2.5 3 3.5 4 4.5 5 5.5 6 6.5 7 (MeV)recoE
0 50 100 150 200
Figure 8.1: Signal excess measured by the SoLid experiment for BR2 reactor cycle 3-2018, presented in terms ofLrecoversusEreco. This is the data input for an oscillation analysis.
For the prediction, the full simulation of reactor cycle 3-2018 was used.
The simulated IBD interactions were folded with the detector response, us-ing a migration matrix trained on the data of reactor cycle 1-2018, that was processed with the full ROsim and uBDT selection method. This migration matrix was already presented in chapter 4. The prediction was then scaled to match the number of events in the open dataset. The background effects were included via their contribution to the statistical uncertainties. The expected sensitivity, based on this prediction and using the relative, PROSPECT-like fit, is shown in figure 8.3. Because of the low statistics, the fit is only sensitive to a very limited part of the parameter space where the sin2(2θ14) mixing terms are larger than about 0.6.
A fit of the data with the null-oscillation prediction results in aχ2-value of