# Presentation: 008369

## Neutral Current π0 Production in MiniBooNE

- Neutral Current π0 Production in MiniBooNE

- Jonathan Link
- Virginia Polytechnic Institute & State University
- Fifth International Workshop on Neutrino-Nucleus Interactions in the Few-GeV Region
- June 1st 2007

- Jonathan Link

## Neutral Current π0 Events in MiniBooNE

- π0 Candidate Event in MiniBoone

- Neutral current π0 production is a major background to the ν*e* appearance analysis.  It is also a prime opportunity to study neutrino induced π0 production.

- Today I’ll talk about:
-  The performance of the reconstruction algorithm.
-  Our method of measuring the π0 production rate.
-  Our extraction of the coherent production relative to resonant.
-  And I’ll show some comparisons of the various generator to MiniBooNE data.

- Jonathan Link

## MiniBooNE Neutral Current π0 Analysis

- The analysis builds on the thesis work of Jen Raaf (NuInt04).
- The major difference is that there has been great progress in the reconstruction algorithm and in the optical model.  As a result we now have much better data/MC agreement and a better signal to noise ratio.
- The central improvement of the new reconstruction algorithm is that it uses several starting points to the π0  and therefore does not get trapped in the wrong local  minimum as often.
- Also we have since discovered and fixed some inadequacies in the Monte Carlo generator.  These effects have had a non-trivial impact on the resulting numbers.

- Jonathan Link

## MiniBooNE Neutral Current π0 Analysis

- The focus of the analysis has been pragmatic:
- Our main objective is to measure the rate of π0 production so that misidentification in the ν*e* oscillation sample can be determine as a function reconstructed νe energy in the CCQE mode.
- As a result the initial product of the NC π0 analysis effort is not an absolute cross section.  Instead it is a measurement of the total π0 production in bins of true π0 momentum, and a measurement of the coherent contribution which effectively fixes the angular distribution.
- Correcting the Monte Carlo to reflect the observed π0 production provides a much better estimate of π0 misidentification.

- Jonathan Link

## Reconstruction Performance

- New π0 algorithm (by Ryan Patterson) improves efficiency and data to Monte Carlo agreement while admitting less background.

- The improved algorithm allows us to use the Monte Carlo to efficiency correct, background subtract and unsmear the data.
- Algorithm returns likelihoods for the electron, muon and π0 hypotheses whose absolute differences are powerful indicators of event type.

- Jonathan Link

## Analysis Cuts

- Pre-cuts (Also applied in the oscillation analysis):
- 1 sub-event (No evidence of a decaying muon)
- Tank hits > 200 (Above the muon decay endpoint)
- Veto hits < 6 (Eliminates cosmic rays)
- Analysis cuts:
- Event radius < 500 cm (Reduces edge effects)
- eμ likelihood difference prefers electron hypothesis
- eπ likelihood difference prefers pion hypothesis
- Mass window:
- 80 MeV/c2 < *m*γγ < 200 MeV/c2

- Jonathan Link

## Data Unsmearing and Efficiency Correction

- MC is used to unsmear the data:

- In bins of true momentum vs. reconstructed momentum, count MC events, over BG, in the signal window.
- Divide by the total number of π0 events generated in that true momentum bin.
- Invert the matrix.
- Perform a BG subtraction on the data in each reconstructed momentum bins.
- Multiply the data vector by the MC unsmearing Matrix

- Monte Carlo Events Passing Analysis Cuts

- All events

- Events with no π0

- Jonathan Link

## The Corrected Data Distribution

- The corrected π0 momentum distribution is softer than the default Monte Carlo.  The normalization discrepancy is across all interaction channels in MiniBooNE.

- From this distribution we derive a reweighting function for Monte Carlo events.

- Ratio of  data and MC

- MC: Generated distribution
- Data: Corrected to true
-          momentum and
-          100% efficiency

- Preliminary

- Preliminary

- Jonathan Link

## Reweighting Monte Carlo to Data

- Reweighting in generated momentum improves data/MC agreement.

- Here we see:
-  Decay opening angle
-  Energy of high energy γ
-  Energy of low energy γ
-  The energy asymmetry
- In all cases the momentum reweighting significantly improves the Data/MC agreement.

- Preliminary

- Jonathan Link

## Neutral Current Resonant and Coherent π0 Production

- ν

- ν

- 12C

- p (n)

- π0

- Z0

- Δ+ (Δ0)

- ν

- ν

- 12C

- π0

- Z0

- Resonant π0 production occurs through a resonance like the Δ(1232).

- Coherent π0 production is an interaction involving the entire nucleus.  In these events one expects the π0s to scatter in a more forward direction.

- Jonathan Link

## Cos θ and Coherent π0 Production

- Coherent and resonant production are distinguishable by the π0 angle with respect to the beam direction (cos θπ).

- We can use this fact to extract a measure of the coherent fraction.

- Jonathan Link

## Study Coherent as a Function of E(1-cosθ)

- In coherent events E(1-cosθ) has a more regular shape, as a function of momentum, than cosθ alone, so we fit for coherent content as a function of this composite.

- Jonathan Link

## Study Coherent as a Function of E(1-cosθ)

- Meanwhile E(1-cosθ) can have large variation in the resonant process in this energy range.
- The Δ decay tends to scramble the π0 angular distribution, which is particularly true at low momentum.

- Jonathan Link

## 2D Fits for Coherent Fraction

- The data are fit in γγ mass and E(1-cos θ) using three templates from the Monte Carlo: Resonant, Coherent and Background.  The Nuance Generator* is the underlying model.

- * Maintained by Dave Casper.   The NC π0 model is based on Rein and Sehgal.

- Variable binning is used to get approx. equal numbers of events in each bin.  The number of bins in each projection is varied from 15 to 25 and the average fit parameters are used.

- Jonathan Link

## Fit Results

- Here the resulting fit is plotted against the projections, and the fit components are shown.

- For the MiniBooNE flux and with the Nuance model we find that (19.5±1.1)% of all exclusive neutral current π0 production is coherent.

- Preliminary

- Preliminary

- Jonathan Link

## Coherent Fraction Systematic Error

- We studied systematic error due to a number of sources including:
-  Choice of binning
-  Make up of background
-  Momentum reweighting
-  Neutrino flux uncertainty
-  Choice of analysis cuts
-  Optical model (new since NuFact06)
- All systematic sources are small compared to the statistical error/fit uncertainty *except *the optical model error which is now the dominate error in the analysis.  The optical model largely measures an uncertainty in energy scale.

| Source | Error |
| --- | --- |
| Binning | 0.21 |
| Background  Model | 0.64 |
| Reweighting | 0.51 |
| Flux | 0.06 |
| Analysis Cuts | 0.51 |
| Optical  Model | 2.34 |
| All Systematics | 2.54 |

- Jonathan Link

## Energy Dependence of the Coherent Fit

- Remember, although we expect a fairly uniform E(1-cos) as a function of momentum, we expect different behavior for the resonant events...

- A slewing of the energy distribution with respect to Monte Carlo will effect the fit.  This uncertainty is accounted for in the optical model error.

- Jonathan Link

## Model Dependence of the Coherent Extraction

- As a test of the robustness of this coherent result, we looked at  drastic variations to the cross section model parameters...

- The fit value is stable against these large changes.  In all cases, a coherent fraction of zero is highly disfavored.

| Variation | Coherent  | Avg CL (%) |  |
| --- | --- | --- | --- |
|  | Fraction (%) | Coh | No Coh. |
| Default Model | 19.5±1.1 | 5.97 | 1.8×10-16 |
| MA Coherent | 19.6±1.1 | 5.73 | 3.8×10-17 |
| No Diffractive | 17.9±1.0 | 11.63 | 5.7×10-17 |
| MA π± Hi | 17.9±1.1 | 3.27 | 1.5×10-14 |
| MA π± Lo | 21.1±1.1 | 5.00 | 7.2×10-22 |
| Binding Energy Hi | 19.4±1.1 | 5.85 | 6.4×10-16 |
| Binding Energy Low | 19.6±1.1 | 6.29 | 2.2×10-17 |
| Fermi Momentum Hi  | 18.2±1.1 | 3.29 | 1.3×10-15 |
| Fermi Momentum Lo | 21.0±1.1 | 4.24 | 4.6×10-23 |

- Jonathan Link

## Model Dependence of the Coherent Extraction

- Just before NuFact06 we discovered that Nuance decays the Deltas isotropically.  This disagrees with the Rein and Seghal model.  We now reweight events, based on the Delta decay angle, to the Rein and Seghal angular distribution.
- The effect of this change is not trivial:

| Variation | Coherent  | Avg CL (%) |  |
| --- | --- | --- | --- |
|  | Fraction (%) | Coh | No Coh. |
| Rein and Sehgal | 19.5±1.1 | 5.97 | 1.8×10-16 |
| Isotropic (NuFact06) | 18.1±1.1 | 1.88 | 7.1×10-17 |
| Pure spin projection 3/2 | 20.8±1.0 | 3.49 | 1.7×10-18 |
| Pure spin projection 1/2 | 16.9±1.2 | 0.01 | 1.3×10-19 |

- In addition we expect that the Delta decay angle could have an affect on π0 misidentification in the oscillation analysis.

- Jonathan Link

## Generator Comparisons

- Comparison of NEUGEN and NEUT to MiniBooNE data distribution.

- Plots from Sam Zeller

- Jonathan Link

## Conclusions

- Neutral current π0 production is both a major background to the MiniBooNE oscillation analysis, and an opportunity to make high impact measurements in neutrino cross sections (with world’s largest data set of GeV neutrino interactions).
- In this talk:
-  I demonstrated the goal oriented approach we have adopted to dealing with π0 events in MiniBooNE.
-  This results in a measurement of  the total π0s produced in bins of momentum
-  And a measurement of the coherent fraction: 			(19.5 ± 1.1 (stat) ± 2.5 (sys)) %
-  These π0 rates as a function of momentum and coherent fraction are used to reweight the Monte Carlo in the oscillation analysis.

- Jonathan Link

## π0 Events in MiniBooNE Antineutrino Running

- π0 Events in MiniBooNE Antineutrino Running

- See poster by Van Nguyen for a discussion of π0 production in the MiniBooNE antineutrino data.

- Jonathan Link