Oct. Coll Meet. 20051 Late Activity Cuts Without Bias Thomas H. Osiecki University of Texas at...

Oct. Coll Meet. 2005 1

Late Activity Cuts Without Bias

Thomas H. OsieckiUniversity of Texas at Austin


Motivation

Red = Data

Black = MC

This huge excessExists for both slicesAnd Events

Well known excess at low energies for both slices and events

Normalized by NumberOf Events

CC+NC+junk


Introduction

Data Set Clues about origin New/Old MC Differences

Effects change previous results Results from application of different

late activity cuts Proposal Conclusion


Data Set 5.96e18 POT August LE-10 Data 2.35e18 POT New LE-10 MC All plots Normalized to 1.0 / POT unless

stated otherwise Cut on Horn Current to be nominal, there

was high and low current running and it makes a difference

Did not use July because of toroid callibration

All Events are subject to fiducial volume cut


Detector Clues

No Cut Strip PH > 2.0 pe

Tim

e (

ns)

Tim

e (

ns)


Low PH Correlation For all lowph slices, found slice with

closest first plane


Time Correlation If I look at events on that correlate with beginning plane, one finds a

long time distribution, a.k.a. late activity

How to get rid of them?


Exp Tail in Batch Structure Tail indicates late-activity, can be studied using LI

in an sgate – See Rustem Ospanov’s Talk

Long ExponentialTail of Activity


New MC LE-10 Inter-nuclear scattering

turned on B-field Map 159

(newer) Better estimate of

cosmic rays, ala Robert Hatcher

Normalized to POT


MC Data difference I observe


Different Late-activity Cuts

Timing Cut and Strip Removal (Niki) Will focus on the cuts that I have

explored (Peter S. Suggestion) Rho – Fraction of event with early activity Exponentially Weighted Rho Rho in different time regimes


Plan of Attack For each rho cut I look at:

Spectra of Data/MC before/after cut Can one get them to agree? How much statistics does one lose?

Effect of Cut at different beam intensities If there exists no bias, then the event spectrum should

be the same after a cut for different beam intensities Use of Kolmogorov-Smirnov Test and Chi2 Test Keep in mind that statistics lower at lower intensities

Single Event Spectrum Ideally would like infinite single-event sample, but will

use this just for comparison


Event PH at Different Intensities Event Spectrum Shouldn’t change (at least for LE)


‘Single’ Event Spectrum Take the first event from every snarl and

plot this as a kind of ‘single’ event spectrum – throws out any notion of late activity

I’m selecting one event per snarl, so I can’t just scale by POT.

Need to scale using number of events Since this is to study bias, need to scale

according to where I KNOW they agree, i.e. the HE tail.

Keep in mind this is approximate, since it includes NO late activity


Rho

Cut based on previous hypothesis Since these junk slices correlate in time

with a previous event, why not make a cut depending on how much previous activity occurred in the channels for said slice?


Rho vs EnergyNot in MC


Effect of Rho Cut


Zoom of effect of Rho


Rho Cut at Different Intensities


Bias from Rho


Weighted Rho

Tau is approximately the characteristic time for later hits to be considered late-activity

By weighting each strip hit by an exponential factor will increase w dramatically depending on how ‘late’ the activity is

If all an events hits are less than the ‘late’ activity one expects for ‘good’ events for rho to be small and for ‘bad’ event, rho is large


Weighted Rho vs Energy


Effect of Weighted Rho


Zoom on Effect of Weighted Rho


Weighted Rho at diff. Intensities


Bias from Weighted Rho


3 Different Rhos

In addition to the first rho I define Rho1 = Rho between[0,200] ns Rho2 = Rho between[200,1000] ns Rho3 = Rho between[1000,infinity] ns

Hope is that since we observe different time scales for late activity that splitting rho up will give us greater cleanup power


3 Rhos vs Energy

Data

MC

rho3 rho2 rho1


Effect of 3 rho cut


Zoom on Effect of 3 rhos


3 Rho’s vs Intensity


Bias from 3 different rhos


Final Data/MC with cuts


Proposal So we need to clean up our data

Essential to understand for NC analysis, not as big an issue for CC

How are we making sure we do not bias? See how cuts affect spectra at different intensities

Issue – Low statistics at lower intensities Use a ‘single’ event spectrum

Not a real single event spectrum Proposal

For a batch every 3 seconds, running 20 hours a day, one would get 24000 spills. I suggest 1 to 2 days of running at 4-5e12.

About 1 neu in the far every 4 hours. -> Would lose about 6-12. Is this acceptable?

The only way to truly know if we’re biasing is to get as close to a single event spectra as we can.

Comments?


Plots for Proposal


Conclusions All 3 do a comparable job of cleaning up the data Original rho seems to match data/mc the best Weighted rho seems to cause the least bias –

especially to the lower side of the main peak (minor effect)

Still this minor deficit in data on lower side of peak I like the original rho because it matches data better,

and slight bias is almost neglible compared to weighted rho Last NC meeting I concluded that the 3 rho’s is

better, but that was before new MC.

Oct. Coll Meet. 20051 Late Activity Cuts Without Bias Thomas H. Osiecki University of Texas at...

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