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

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Presentation on theme: "Oct. Coll Meet. 20051 Late Activity Cuts Without Bias Thomas H. Osiecki University of Texas at Austin."— Presentation transcript:

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

2 Oct. Coll Meet Motivation Red = Data Black = MC This huge excess Exists for both slices And Events Well known excess at low energies for both slices and events Normalized by Number Of Events CC+NC+junk

3 Oct. Coll Meet Introduction  Data Set  Clues about origin  New/Old MC Differences Effects change previous results  Results from application of different late activity cuts  Proposal  Conclusion

4 Oct. Coll Meet 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

5 Oct. Coll Meet Detector Clues No Cut Strip PH > 2.0 pe Time (ns)

6 Oct. Coll Meet Low PH Correlation  For all lowph slices, found slice with closest first plane

7 Oct. Coll Meet 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?

8 Oct. Coll Meet Exp Tail in Batch Structure  Tail indicates late-activity, can be studied using LI in an sgate – See Rustem Ospanov’s Talk Long Exponential Tail of Activity

9 Oct. Coll Meet 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

10 Oct. Coll Meet MC Data difference I observe

11 Oct. Coll Meet 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

12 Oct. Coll Meet 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

13 Oct. Coll Meet Event PH at Different Intensities  Event Spectrum Shouldn’t change (at least for LE)

14 Oct. Coll Meet ‘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

15 Oct. Coll Meet 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?

16 Oct. Coll Meet Rho vs Energy Not in MC

17 Oct. Coll Meet Effect of Rho Cut

18 Oct. Coll Meet Zoom of effect of Rho

19 Oct. Coll Meet Rho Cut at Different Intensities

20 Oct. Coll Meet Bias from Rho

21 Oct. Coll Meet 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

22 Oct. Coll Meet Weighted Rho vs Energy

23 Oct. Coll Meet Effect of Weighted Rho

24 Oct. Coll Meet Zoom on Effect of Weighted Rho

25 Oct. Coll Meet Weighted Rho at diff. Intensities

26 Oct. Coll Meet Bias from Weighted Rho

27 Oct. Coll Meet 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

28 Oct. Coll Meet Rhos vs Energy Data MC rho3 rho2 rho1

29 Oct. Coll Meet Effect of 3 rho cut

30 Oct. Coll Meet Zoom on Effect of 3 rhos

31 Oct. Coll Meet Rho’s vs Intensity

32 Oct. Coll Meet Bias from 3 different rhos

33 Oct. Coll Meet Final Data/MC with cuts

34 Oct. Coll Meet 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 spills. I suggest 1 to 2 days of running at 4-5e12. About 1 neu in the far every 4 hours. -> Would lose about 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?

35 Oct. Coll Meet Plots for Proposal

36 Oct. Coll Meet 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.


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