2000 diffuse analysis

2000 Diffuse Analysis

Jessica Hodges, Gary Hill, Jodi CooleyUniversity 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 variablesldirb(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.

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.

How this analysis has changed......

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 LevelsLevel 3

Jodi Jessicasame events same events

Level 42-dim coincident muon cut jkrchi(up)

quality cuts on:jkchi(down)-jkchi(up)

ldirb(up)smootallphit(up)ndirc(up)

Level 5quality cuts on:jkchi(down)-jkchi(up)ldirb(up)smootallphit(up)ndirc(up)ldirc(up) vs. jkchi(shower)-jkchi(up) for nch<50, positive smoothnessjkchi(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. 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?

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.

Nch < 70MC

normalized to one year100% data

Jodi's cuts

Still looks fairly constant about 0.7

Nch < 504 1- dim cuts100% data

Nch < 704 1- dim cuts100% 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.

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

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) ldirc vs. track-to-shower ratiojkchi(down)-jkchi(up) Track-to-shower ratio vs. cogzsmootallphit(up)ndirc(up) zenith vs. ndirc (coincident muon cut)

Jodi's 2-dim coincident muon cuts seems to be making the graph level off as the quality level increases

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.

This cut seems harsh, but it seems to best way to remove simulated coincident muons from the sample. Consider moving this cut around....

Consider a new coincident muon cut on jkrchi(up)

Must cut tightly against the coincident muon, otherwise high nch coincident muons will remain

Nch of events with jkrchi(up) < 7.5

Jkrchi(up)

Nch

these are the coincident muons left at level 4.06

these are the events to the left of the yellow line

4 1-dim cuts and jkrchi(up) cut

average of 12 points is 0.79

Cut options: 4 1-dim cuts 4 1-dim cuts + jkrchi cut 4 1-dim cuts + Jodi's 2-dim coincident cut

4 1-dim cuts 4 1-dim cuts 4 1-dim cuts+ jkrchi cut + 2-dim coinc cut

4.01 57.32 .56 33.014.02 31.34 .22 19.184.03 16.95 .11 10.824.04 8.59 .00 5.804.05 5.13 .00 3.574.06 3.23 .00 2.904.07 1.00 .00 .894.08 .45 .00 .454.09 .33 .00 .334.10 .00 .00 .00

Number of coincident muons surviving at each level

In this plot, cuts applied and the line shown correspond to level 4.06.

Cuts Applied:ldirb(up)smootallphit(up)ndirc(up)jkrchi(up)Not Applied:jkchi(down)-jkchi(up)

Cut Keep

Cuts Applied:jkchi(down)-jkchi(up)smootallphit(up)ndirc(up)jkrchi(up)Not Applied:ldirb(up)

KeepCut

In this plot, cuts applied and the line shown correspond to level 4.06.

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…

Cut Keep

Keep

Keep

Keep

Keep

Keep

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

Cuts Applied:jkchi(down)-jkchi(up)ldirb(up)ndirc(up)jkrchi(up)Not Applied:smootallphit(up)

KeepCut

Cut

In this plot, cuts applied and the line shown correspond to level 4.06.

Cuts Applied:jkchi(down)-jkchi(up)ldirb(up)ndirc(up)smootallphit(up)Not Applied:jkrchi(up)

In this plot, cuts applied and the line shown correspond to level 4.06.

Keep Cut

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

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

-2 MC Atms muon MC Coinc muon MC

4.01 317 256.9 57.6 0 .564.02 245 245.0 55.2 0 .224.03 202 231.5 52.0 0 .114.04 190 216.1 48.2 0 04.05 173 200.5 44.3 0 04.06 154 180.5 39.9 0 04.07 135 157.9 34.7 0 04.08 109 133.8 29.7 0 04.09 81 109.2 24.4 0 04.10 57 83.4 19.3 0 04.11 33 57.8 13.2 0 04.115 23 42.8 10.3 0 04.12 14 34.5 8.2 0 04.125 10 22.7 5.9 0 04.13 10 15.3 4.2 0 0

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…

-2

Cut at4.01 0.3126 85 27 8.0 19.84.02 0.3149 85 20 7.3 19.04.03 0.3192 85 12 6.7 18.14.04 0.3259 85 10 6.0 17.04.05 0.3338 85 8 5.3 15.84.06 0.3489 80 7 5.9 15.84.07 0.3699 77 8 5.6 14.64.08 0.3943 75 5 4.9 13.04.09 0.4399 70 6 5.1 11.84.10 0.4996 70 5 3.8 9.54.11 0.6285 69 2 2.4 6.64.115 0.7266 70 1 1.8 5.24.12 0.8537 68 1 1.5 4.34.125 1.0445 67 1 1.3 3.34.13 1.3404 61 1 1.3 2.6

Quality Level with jkrchi cut applied

Note: When the limit is set, the numbers will change slightly with the nusim normalization.

What do these data events look like in the event viewer?

ievent

4869527 X X

1836554 X X X

1355187 X X X X X X X

1760779 X X X X X X X X X X X X X

4908796 X X X

3028219 X X

3593604 X X X X X

3892853 X

234639 X X X X X X X X

1735409 X X X X X X

3842463 X

4953648 X X X X X X X X

4377402 X X

826017 X

491361 X X

2277256 X X X

Quality level 4.__ with final optimized nch cut madeD

ata

even

ts s

urv

ivin

g

xx

x

xx

x

ok

ok

ok

what I think of the event in the viewer

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)