Post on 14-Dec-2015
Oct. Coll Meet. 2005 1
Late Activity Cuts Without Bias
Thomas H. OsieckiUniversity of Texas at Austin
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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
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Introduction
Data Set Clues about origin New/Old MC Differences
Effects change previous results Results from application of different
late activity cuts Proposal Conclusion
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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
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Detector Clues
No Cut Strip PH > 2.0 pe
Tim
e (
ns)
Tim
e (
ns)
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Low PH Correlation For all lowph slices, found slice with
closest first plane
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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?
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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
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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
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MC Data difference I observe
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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
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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
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Event PH at Different Intensities Event Spectrum Shouldn’t change (at least for LE)
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‘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
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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?
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Rho vs EnergyNot in MC
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Effect of Rho Cut
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Zoom of effect of Rho
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Rho Cut at Different Intensities
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Bias from Rho
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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
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Weighted Rho vs Energy
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Effect of Weighted Rho
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Zoom on Effect of Weighted Rho
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Weighted Rho at diff. Intensities
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Bias from Weighted Rho
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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
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3 Rhos vs Energy
Data
MC
rho3 rho2 rho1
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Effect of 3 rho cut
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Zoom on Effect of 3 rhos
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3 Rho’s vs Intensity
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Bias from 3 different rhos
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Final Data/MC with cuts
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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?
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Plots for Proposal
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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.