2000 Diffuse Analysis Jessica Hodges, Gary Hill, Jodi Cooley University of Wisconsin – Madison

Outline 1. Summary of what's happened in the diffuse analysis thus far review of Jodi's work issues presented by Gary at Bartol 2. New Quality Cut Levels passing rates and nusim normalization 3. Treatment for Coincident Muons choosing cuts to remove coincident muons 4. Final Energy Cut calculating the Model Rejection Factor at each quality level examining events that pass the optimized cuts

Jodi's Thesis Work on this Analysis Jodi's cut variables ldirb(up) jkchi(down)-jkchi(up) smootallphit(up) ndirc(up) zenith(up)-zenith(down) vs. ndirc(up)-ndirc(down) (downgoing muon and coincident muon cut) ldirc vs. track-to-shower ratio (only for nch>50 and positive smoothness) track-to-shower ratio vs. cogz (only for zenith(up)<120)

Review of Jodi's Analysis Cuts developed on 50% of the data After nch>80 cut: 6 events on atmospheric background of 3.3 Second 50% of the data yielded 4 events after the final nch cut One of these events is a coincident muon.

How this analysis has changed...... First, new coincident muon Monte Carlo was generated with dCorsika (and the pCorsika was no longer used). All files had 64-iteration maximum likelihood and downgoing reconstruction run on them. and.....

Issues from Bartol : Cascade fit problem At Bartol, Gary discussed a bump in the nch distribution for one half of the data. Jodi used a 2-dimensional cut on ldirc(up) vs. track-to-shower ratio on events with positive smoothness and nch > 50 to correct this problem.

Issues from Bartol : Cascade fit problem However, the cascade fit was done before the crosstalk filter was applied. Likelihood ratios based on different hit selections make no sense. After correcting the cascade fit, this cut did not correct the problem. Anyway, this discrepancy did not appear in the second half of the data. We have abandoned Jodi’s special two-dimensional cuts.

Comparison of Quality Levels Jodi Jessica same events same events Level 4 2-dim coincident muon cut jkrchi(up) quality cuts on: jkchi(down)-jkchi(up) ldirb(up) smootallphit(up) ndirc(up) Level 5 jkchi(down)-jkchi(up) ldirb(up) ldirc(up) vs. jkchi(shower)-jkchi(up) for nch<50, positive smoothness jkchi(shower)-jkchi(up) vs. cogz for zenith<120

Now consider passing rates and nusim normalization... Look at ratio of number of data events to atmospheric events at each quality level in order to normalize the nusim. Set the normalization at the value where the ratio of data to atmospheric events remains constant. The region of interest for this analysis corresponds to high nch values. The nusim can be normalized with 100% of the data at low nch values.

To find the differential passing rate: data (level A) - data (level B) atms (level A) – atms (level B) To find the integrated passing rate: data (level A) atms (level A) The blue line shows the 0.7 normalization factor that Jodi used.

Here, the cuts are exactly the same as Jodi's, but two of the 2-dim cuts use the new crosstalk-cleaned cascade fit. The normalization remains close to 0.7

Now consider the passing rate at the new levels Now consider the passing rate at the new levels. The new levels tighten the cuts only along the 4 one-dimensional cuts. Normalization does not appear to be 0.7. Why is the line sloping down?

Jodi's cuts Why is the line sloping down? Possibility 1) There is some sort of nch dependence and maybe the normalization will be different if it is calculated with events with nch<50 or nch<70, for example. Jodi's cuts Nch < 70 MC normalized to one year 100% data Still looks fairly constant about 0.7

Nch < 50 4 1- dim cuts 100% data Nch < 70 4 1- dim cuts 100% data At the highest quality levels, the nch < 50 and nch < 70 curves are very similar. An nch factor is probably not causing the different behavior in the passing rate.

Possibility 2) One or more of Jodi's two dimensional cuts is causing the passing rate vs. quality level graph to become flat at the highest quality levels. Jodi's cuts applied in this plot: Jodi's cuts not applied in this plot: ldirb(up)  zenith vs.  ndirc (coincident muon cut) jkchi(down)-jkchi(up) smootallphit(up) ndirc(up) ldirc vs. track-to-shower ratio track-to-shower ratio vs. cogz

Jodi's 2-dim coincident muon cuts seems to be making the graph level off as the quality level increases Jodi's cuts applied in this plot: Jodi's cuts not applied in this plot: ldirb(up) ldirc vs. track-to-shower ratio jkchi(down)-jkchi(up) Track-to-shower ratio vs. cogz smootallphit(up) ndirc(up)  zenith vs.  ndirc (coincident muon cut)

After the 4 1-dimensional cuts, many data events remain which seem to resemble events in the coincident muon Monte Carlo. Now let's discuss how to cut against coincident muons......

Jodi's coincident muon cut Note that Jodi's coincident cut is not very effective with dCorsika files.

Consider a new coincident muon cut on jkrchi(up) This cut seems harsh, but it seems to best way to remove simulated coincident muons from the sample. Consider moving this cut around....

Must cut tightly against the coincident muon, otherwise high nch coincident muons will remain Nch of events with jkrchi(up) < 7.5 these are the events to the left of the yellow line Jkrchi(up) these are the coincident muons left at level 4.06 Nch

4 1-dim cuts and jkrchi(up) cut average of 12 points is 0.79

Number of coincident muons surviving at each level Cut options: 4 1-dim cuts 4 1-dim cuts + jkrchi cut 4 1-dim cuts + Jodi's 2-dim coincident cut Number of coincident muons surviving at each level

jkchi(down)-jkchi(up) Cut Keep Cuts Applied: ldirb(up) smootallphit(up) ndirc(up) jkrchi(up) Not Applied: jkchi(down)-jkchi(up) In this plot, cuts applied and the line shown correspond to level 4.06.

jkchi(down)-jkchi(up) smootallphit(up) ndirc(up) jkrchi(up) Cuts Applied: jkchi(down)-jkchi(up) smootallphit(up) ndirc(up) jkrchi(up) Not Applied: ldirb(up) Cut Keep In this plot, cuts applied and the line shown correspond to level 4.06.

jkchi(down)-jkchi(up) ldirb(up) smootallphit(up) jkrchi(up) Cut Keep Cuts Applied: jkchi(down)-jkchi(up) ldirb(up) smootallphit(up) jkrchi(up) Not Applied: ndirc(up) Note that at this particular level, the ndirc cut is not needed because all 169 data events with ndirc<10 do not satisfy the jkrchi cut. See next plot…

Keep Keep If this region is empty at a given quality level, then the ndirc cut is not needed. Keep Keep Keep

jkchi(down)-jkchi(up) ldirb(up) ndirc(up) jkrchi(up) Not Applied: Keep Cuts Applied: jkchi(down)-jkchi(up) ldirb(up) ndirc(up) jkrchi(up) Not Applied: smootallphit(up) Cut Cut In this plot, cuts applied and the line shown correspond to level 4.06.

jkchi(down)-jkchi(up) ldirb(up) ndirc(up) smootallphit(up) Cuts Applied: jkchi(down)-jkchi(up) ldirb(up) ndirc(up) smootallphit(up) Not Applied: jkrchi(up) Keep Cut In this plot, cuts applied and the line shown correspond to level 4.06.

cogz – no zen cut – no nch cut - level 4.07

cogz – (zen < 120) - no nch cut - level 4.07

Passing Rates at the Different Quality Levels first half of the data --- MC weighted to half a year 4 1-dim cuts + jkrchi(up) cut

Now that the quality level cuts are set and the coincident muons are taken care of .... Let's look at the final energy (nch) cut and the Model Rejection Factor at each quality level

Now, for the first half of the data, make the nch cut at each quality level and examine what events survive. The placement of the nch cut is determined when calculating the Model Rejection factor. These numbers are…

Quality Level with jkrchi cut applied What do these data events look like in the event viewer? Note: When the limit is set, the numbers will change slightly with the nusim normalization.

Quality level 4.__ with final optimized nch cut made what I think of the event in the viewer x x ok ok x Data events surviving x x x ok

cogz – no zen cut - nch cut – level 4.07

cogz – (zen<120) – nch cut - level 4.07

Diffuse 2000 Outlook.... Decide on a normalization factor for the nusim Choose a quality level for the analysis Would like permission to unblind now (again)… (this was already unblinded in Jodi’s thesis)