# Presentation: 470693

## MiniBooNE Event Reconstruction and Particle Identification

- Hai-Jun Yang
- University of Michigan, Ann Arbor
- (for the MiniBooNE Collaboration)
- DNP06, Nashville, TN
- October 25-28, 2006

## Outline

- Physics Motivation
- MiniBooNE Event Types
- Event Reconstruction
- Particle Identification
- Summary

- DNP06, H.J.Yang,  MiniBooNE PID

- 10/26/2006

## Physics Motivation

-  LSND observed a positive signal, but not confirmed.
-  The MiniBooNE is designed to confirm or refute LSND
- oscillation result at m2 ~ 1.0 eV2 .

- DNP06, H.J.Yang,  MiniBooNE PID

- 10/26/2006

## MiniBooNE Flux

- The intrinsic *e* is ~0.5% of the
- neutrino Flux, it’s one of major
- backgrounds for   e search.

-  L(m), E(MeV), m2(eV2)

- DNP06, H.J.Yang,  MiniBooNE PID

- 10/26/2006

## Event Topology

- DNP06, H.J.Yang,  MiniBooNE PID

- 10/26/2006

## Event Reconstruction

- To reconstruct event position, direction, time, energy and invariant mass etc.
- Cerenkov light – prompt, directional
- Scintillation light – delayed, isotropic
- Using time likelihood and charge likelihood method to determine the optimal event parameters.
- Two parallel reconstruction packages
- S-Fitter is based on a simple, point-like light source model;
- P-Fitter differs from S-Fitter by using more 0th approximation tries, adding e/ tracks with longitudinally varying light source term, wavelength-dependent light propagation and detection, non-point-like PMTs and photon scattering, fluorescence and reflection.

- DNP06, H.J.Yang,  MiniBooNE PID

- 10/26/2006

## Reconstruction Performance

- Michel Electron

- DNP06, H.J.Yang,  MiniBooNE PID

- 10/26/2006

## Particle Identification

- Two complementary and parallel methods:
- Log-likelihood technique:
- simple to understand, widely used in HEP data analysis but less sensitive
- Boosted Decision Trees:
- Non-linear combination of input variables
- Great performance for large number of input variables (about two hundred variables)
- Powerful and stable by combining many decision trees to make a “majority vote”

- DNP06, H.J.Yang,  MiniBooNE PID

- 10/26/2006

## Boosted Decision Trees

-           How to build a decision tree ?
- For each node, try to find the best variable and splitting
- point which gives the best separation based on Gini index.
- Gini_node = Weight_total*P*(1-P), P is weighted purity
- Criterion = Gini_father – Gini_left_son – Gini_right_son
- Variable is selected as splitter by maximizing the criterion.

-           How to boost the decision trees?
- Weights of misclassified events in current tree are increased, the
- next tree is built using the same events but with new weights,
- Typically, one may build few hundred to thousand trees.

-           How to calculate the event score ?
- For a given event, if it lands on the signal leaf in one tree, it is
- given a score of 1, otherwise, -1. The sum (probably weighted)
- of scores from all trees is the final score of the event.

- DNP06, H.J.Yang,  MiniBooNE PID

- 10/26/2006

## Performance vs Number of Trees

-  Boosted decision trees focus on the misclassified events which usually have high weights after hundreds of tree iterations. An individual tree has a very weak discriminating power; the weighted misclassified event rate errm is about 0.4-0.45.

- The advantage of using boosted decision trees is that it combines many decision trees,  “weak” classifiers, to make a powerful classifier. The performance of boosted decision trees is stable after a few hundred tree iterations.

- Ref1: H.J.Yang, B.P. Roe, J. Zhu, “Studies of Boosted Decision Trees for MiniBooNE Particle Identification”,
-           Physics/0508045, Nucl. Instum. & Meth. A 555(2005) 370-385.
- Ref2: B.P. Roe, H.J. Yang, J. Zhu, Y. Liu, I. Stancu, G. McGregor, ”Boosted decision trees as an alternative to
-           artificial neural networks for particle identification”, physics/0408124, NIMA 543 (2005) 577-584.

- DNP06, H.J.Yang,  MiniBooNE PID

- 10/26/2006

## Output of Boosted Decision Trees

- Osc e CCQE vs All Background

- MC vs  Data

- DNP06, H.J.Yang,  MiniBooNE PID

- 10/26/2006

## Summary

- MiniBooNE Event Reconstruction
- Position resolution ~ 23 cm
- Direction resolution ~ 6o
- Energy resolution ~ 15%
- Reconstructed 0 mass resolution ~ 20 MeV/c2
- MiniBooNE Particle Identification
- For 0.1%  eff., ~ 90% electron eff.
- For 1% 0 eff., ~ 70% electron eff.
- For 0.5% all background eff., ~ 80% electron eff.
- MiniBooNE Results are coming soon ...

- DNP06, H.J.Yang,  MiniBooNE PID

- 10/26/2006

## Backup Slides

- DNP06, H.J.Yang,  MiniBooNE PID

- 10/26/2006

## Light Model

- θc

- η

**Directional Cherenkov light **ρ

**Isotropic Scintilation light  ****φ**

**Point-like light source model**

**(x y z t)**

**(ux uy uz)**

**r****i**

** ****Predicted charge****  **

** ****Cerenkov light - directional**

** ****Scintillation light - isotopic**

**Cerenkov angular distribution - F(cos********)**
**PMT angular response –  f(cos********)**
**Cerenkov attenuation length – ********cer**
**Scintillation attenuation length ****- ********sci**
**Relative quantum efficiency - ********i**
**Cerenkov light strength - ******
**Scintillation light strength - ******

**f(cos****η****)**

**cos****η**

- DNP06, H.J.Yang,  MiniBooNE PID

- 10/26/2006

## Light Model

**Corrected time**

**2.  Cerenkov light t****corr****(i)**** distribution**

**3.  Scintillation light t****corr****(i)**** distribution**

**4.  Input: Cerenkov light – t****0****cer**** ****,****σ****cer**
**      ****Scintillation light – t****0****sci**** ****,****σ****sci****,****τ****sci**

**5. Total negative log time likelihood**

- exp