Plans for fiTQun FV
cut optimization
Andrew Missert
University of Colorado, Boulder
fiTQun workshop
Dec. 15, 20141

Introduction•fiTQun needs a dedicated study of the fiducial volume cuts••With improvements to vertex resolution, PID, direction
resolution, it is possible that current FV cuts are tooconservative••

Previous cuts optimized for APFit

Significant proportion of the ID volume is currently
outside the FV cuts, could improve statistics with looser
cuts

Need a unified way of incorporating systematic errors in FVcuts

2Performance Near ID WallmisID ratetrue muon!
true electron

average Δ θ [rad]average Δ vtx [cm]average Δ p [MeV]3

Performance Near ID Wall
MisID rate

Muon MC (Rrc, Zrc)

average △θ

average △vtx

average △p4

FV Optimization Method•

We need to settle on an exact method for optimizing thefiducial volume cuts.

•Choosing a method for dealing with systematic errors will
determine the direction we take in our studies of control
samples•

Proposed method for FV cut optimization:•Use a reweighing scheme to reweight the T2K MC toaccount for systematic errors•Maximize signal significance for each set of reweighed
MC to see how the FV cuts are affected by systematics

5

Systematic error
parameters anduncertainties from control
samples studies

T2K MC sorted into signal
and background channels

re-weight signal andbackground samples
using throws of systematic
error parameters
Optimize FV cuts for each
set of weighted MC

Distribution of best-fit FV
cuts to choose fromFor a given FV cut, sys.
error and covariance forfor all Enu bins
6

Toy Example: Numu FV optimization•A general framework to do all of this has
been written•

Demonstrate this idea for the case ofnumu FV optimization•Use naive numu CCQE event
selection•Sort T2K MC into signal, non-CCQE
background, and dead region
background•

Maximize S = s/sqrt(b) on grid of walland towall cuts•

re-weight the background to see how
optimal point changes

7

signalnonCCQE bg
dead region bg

Toy Example: Numu FV optimization8

Toy Example: Numu FV optimization
dead region background x10

9Metrics for Optimization•S/sqrt(S+B) is clearly not the best metric for optimization•

It is insensitive to kinematic reconstruction problems•

Is there a better metric that is sensitive to the kinematicreconstruction?

•For now I”m using a “energy bin weighted significance”

S=NP
bins
i=0

each Enu bin weighted by
the derivative of expected
number of events w.r.t. theoscillation parameteri p si( dn)d✓s +bi

10i

for the purpose of thismetric, events from truebins !=i are counted as
backgroundToy Example: Numu FV optimization

best point: (87cm, 10cm)( (88cm, 25cm) if using s/sqrt(b) )11

Toy Example: Numu FV optimization
before cuts
CCQE signal
mis-binned CCQE
non CCQE
dead region

12Toy Example: Numu FV optimization
after cuts
CCQE signal
mis-binned CCQE
non CCQE
dead region13

Next Steps: Parameterization of Systematic Error
0-1 GeV
Data/MC comparison
for fiTQun cosmics

1-2 GeV>2 GeV

14

Control samples for fiTQun variables
atmospheric
neutrinos

stopping cosmicmuons••constrain error on pμ using
reconstructed muon momentum/rangeconstrain vertex resolution by seeing
how far into the detector the muons
are reconstructed•

constrain pe using decay electronenergy spectrum•

constrain muon/electron mis-ID rate••

covers T2K ν energy range•

Comparing sizes of CCQE, CC1π,NCπ0 and CCOther provides
constraints on PID and backgroundrejection•

lepton energy spectrum provides
additional constraint on reconstructed
momentumhybrid π0

T2K RC out of ID•

hybrid data/MC sample consisting of one
MC electron ring and one data electron ring

Use as a sideband sample forconstraining dead region background•

Useful for constraining efficiency of π
background rejection

150Summary•

MC reweighting seems like a good general scheme toaccount for systematic errors•

A general framework this reweighing scheme has beendeveloped, seems to give qualitatively reasonable results•

Need to think more about best metrics for optimization,
consider some kind of simple sensitivity analysis.

•Next steps, parameterizing systematic errors in a realistic
way on a case-by-case basis.
•returning to cosmic muon samples to start this16