Introduction

π 0 ID and Characterization Performance

FV Studies

π 0 Background ID and Selection in fiTQun̈
Zachary Liptak

December 15, 2014

Zachary Liptak

π 0 Background ID and Selection in fiTQun̈

Conclusions

Introduction

π 0 ID and Characterization Performance

1

Introduction2

π 0 ID and Characterization Performance

3FV Studies
Dead Region π 0 s

4Conclusions

Zachary Liptak

FV Studies

π 0 Background ID and Selection in fiTQun̈

Conclusions

Introduction

π 0 ID and Characterization Performance

FV Studies

Conclusions

Introduction

fiTQun’s first (and current) use is in the identification andrejection of π 0 s in SK.
Studies have been ongoing to show how well π 0 s are identified andcharacterized.

Zachary Liptak

π 0 Background ID and Selection in fiTQun̈

Introduction

π 0 ID and Characterization Performance

FV Studies

Conclusions

Introduction

fiTQun’s first (and current) use is in the identification andrejection of π 0 s in SK.
Studies have been ongoing to show how well π 0 s are identified andcharacterized.
Events are generated with a particle gun of π 0 s up to 1 GeV.

Zachary Liptak

π 0 Background ID and Selection in fiTQun̈

Introduction

π 0 ID and Characterization Performance

FV Studies

Conclusions

Software aside: FiTQun Handling of π 0 s
π 0 s are reconstructed with both a constrained andnon-constrained mass fit.
Constrained fit does not perform well at the moment; all plots
shown are for non-constrained fit

All π 0 events are considered to have a single sub-event
Output variables contained in the fqpi0* variables

Zachary Liptak

π 0 Background ID and Selection in fiTQun̈

Introduction

π 0 ID and Characterization Performance

FV Studies

Position Reconstruction

Zachary Liptak

π 0 Background ID and Selection in fiTQun̈

Conclusions

Introductionπ 0 ID and Characterization Performance

FV Studies

Position Reconstruction

Zachary Liptak

π 0 Background ID and Selection in fiTQun̈

Conclusions

Introduction

π 0 ID and Characterization Performance

FV Studies

Mass Reconstruction

Zachary Liptak

π 0 Background ID and Selection in fiTQun̈

Conclusions

Introduction

π 0 ID and Characterization Performance

FV Studies-log(Likelihood)

Zachary Liptak

π 0 Background ID and Selection in fiTQun̈

Conclusions

Introduction

π 0 ID and Characterization Performance

FV Studies

Opening Angle

Zachary Liptak

π 0 Background ID and Selection in fiTQun̈

Conclusions

Introduction

π 0 ID and Characterization Performance

FV Studies

Opening Angle

Zachary Liptak

π 0 Background ID and Selection in fiTQun̈

Conclusions

Introduction

π 0 ID and Characterization Performance

FV Studies

Likelihood by towall

Zachary Liptak

π 0 Background ID and Selection in fiTQun̈

Conclusions

Introduction

π 0 ID and Characterization Performance

FV Studies

Likelihood by fromwall

Zachary Liptak

π 0 Background ID and Selection in fiTQun̈

Conclusions

π 0 ID and Characterization Performance

Introduction

FV Studies

Conclusions

0

Dead Region π sDead Region π 0 s

To investigate the possibility of photons from π 0 s decaying in thedeadregion converting in the ID, a sample of π 0 events wasgenerated entirely in the SK dead region.

Zachary Liptak

π 0 Background ID and Selection in fiTQun̈

π 0 ID and Characterization Performance

Introduction

FV Studies

0Dead Region π sConversion Length

Zachary Liptak

π 0 Background ID and Selection in fiTQun̈

Conclusions

π 0 ID and Characterization Performance

Introduction

FV Studies

0

Dead Region π sConversion Length

Zachary Liptak

π 0 Background ID and Selection in fiTQun̈

Conclusions

π 0 ID and Characterization Performance

Introduction

FV Studies0Dead Region π sConversion Position (Contained Events)

Zachary Liptak

π 0 Background ID and Selection in fiTQun̈

Conclusions

π 0 ID and Characterization Performance

Introduction

FV Studies

Conclusions

0Dead Region π sEvent Selection

The question of event selection remains to be addressed. Several
possibilities exist:
1

Individual cuts on variables

2

Multivariate selection using fiTQun variables

Individual cuts have been performed in the past, but studies should
be carried out to determine if the selection can be improved.
Additionally, multiple analyses can be carried out as a secondary
check on results.

Zachary Liptak

π 0 Background ID and Selection in fiTQun̈

π 0 ID and Characterization Performance

Introduction

FV Studies

0Dead Region π sEvent Selection

For multivariate processes, work has begun with TMVA to test
several options, including Boosted Decision Trees (BDT) andk-Nearest Neighbor (kNN) techniques.
ROOT trees can be customized to relevant variables:

Zachary Liptak

π 0 Background ID and Selection in fiTQun̈

Conclusions

π 0 ID and Characterization Performance

Introduction

FV Studies

0

Dead Region π sEvent Selection

For multivariate processes, work has begun with TMVA to test
several options, including Boosted Decision Trees (BDT) andk-Nearest Neighbor (kNN) techniques.
ROOT trees can be customized to relevant variables:

This process is streamlined to allow for easy, streamlined use ofFiTQun variables in TMVA.

Zachary Liptak

π 0 Background ID and Selection in fiTQun̈

Conclusions

π 0 ID and Characterization Performance

Introduction

FV Studies

Conclusions

Conclusions

fiTQun readily identifies π 0 s over a wide range of momenta
π 0 PID remains quite good even for low wall/towall values
Event selection improvements may result from improved MVAor cut determination

Zachary Liptakπ 0 Background ID and Selection in fiTQun̈