Download presentation

Presentation is loading. Please wait.

Published byReilly Lowrance Modified over 2 years ago

1
Studies towards TGC limit extraction Nick Edwards, University of Glasgow Nick Edwards 1

2
Outline Reminder on TGC binnings. Comparison of SM and TGC distributions at truth level in these bins. Check how C ZZ looks in the four TGC samples compared to the SM. First results using a grid method to parameterise yields as a function of the coupling. Nick Edwards 2 Quick reminder on TGCs We add them as extra terms in an effective Lagrangian. For on shell ZZ, we can parameterise them in four independent constants: f5Z, f4Z, f4γ,f5γ With the 1fb -1 analysis we set limits at the 0.1 level

3
Variables and Bins Reminder: We have chosen to use the leading Z pT, four- lepton mass and leading lepton pT to calculate the TGC limits. Chosen bins: –M(4l) : 0-240, 240-300, 300-400, 400-600, 600+ –Pt(lead Z) : 0-60, 60-100, 100-200, 200-300, 300+ –Pt(lead lepton) : 0-60, 60-100, 100-150, 150-250, 250+ Check how TGC distributions look in these binnings using the four Sherpa MC11c samples: –nTGC 0(f4γ=0.1, f5γ=f4Z=f5Z=0.0) –nTGC 1(f5γ=-0.1, f4γ=f4Z=f5Z=0.0) –nTGC 2(f4γ=f5γ=0.1, f4Z=f5Z=0.0) –nTGC 3 (f4γ=f5γ=0.0, f4Z=f5=0.1) NB these samples are roughly at the boundary of what we have already excluded. These distributions are at truth level. Nick Edwards 3

4
Four Lepton Mass Nick Edwards 4 Not really sensitive in first 2 bins

5
Leading Z pT Nick Edwards 5 Not really sensitive in first 2 bins

6
Leading Lepton pT Nick Edwards 6 Not really sensitive in first 2 bins

7
C ZZ in TGC samples CZZ seems to be ~ 10% higher in the TGC samples in the four electron channel and ~5% in the 2e2mu channel. Nick Edwards 7

8
C ZZ in TGC samples : m ZZ Nick Edwards 8 In the electron channels, C ZZ seems much higher in the TGC samples in the high bins than in the SM. In four muon channel is a bit lower in the last bin – but could be stats.

9
C ZZ in TGC samples : P T Z1 Nick Edwards 9

10
C ZZ in TGC samples : P T Lep1 Nick Edwards 10

11
Yield Parameterisation Need to parameterise the predicted yield at as a function of the anomalous coupling, varying one and holding the others constant. Doing this find a quadratic dependence: e.g. Yield = F 00 + F 44 (f 5 Z ) 2 Previously we used afterburner, which takes the 4 fully reconstructed TGC samples (actually it can do it with one, but four are used as a cross check) and reweights to different values of the couplings to obtain the yield curves. An alternative “brute force” approach is to generate a grid of samples: –Pick one constant to vary, holding the others constant. –Generate a load of samples at different values of the coupling. –Obtain the predicted yield at each value of the coupling, then can fit a parabola to obtain the paramaterised yield. Nick Edwards 11 From 1fb-1 analysis Differential Cx including TGCsSet all except f 5 Z to 0

12
f 5 Z Parameterisation Try this approach this for f 5 Z. Generate 100k events with Sherpa 1.4.0 with f 5 Z = -0.4, -0.25, - 0.1 0, 0.1, 0.25, 0.4. f 5 Z = -0.4 sample not finished yet… Then run analysis code to obtain predicted yield at truth level and fit to a parabola. These “predicted yields” are all at truth level so far. What we want is the predicted yield at reconstruction level – ie what we observe. Unfortunately it’s not really practical to fully simulate and reconstruct a grid of samples, so instead predict the yield at truth level and use C ZZ to come back to the reconstruction level –Relies on C ZZ being the same in the TGC samples as the SM – have shown it isnt’! Also need to apply k-factor to get normalisation right. Nick Edwards 12

13
f 5 Z Parameterisation - Unbinned Nick Edwards 13

14
f 5 Z Parameterisation - m ZZ Nick Edwards 14 Same thing but in the 5 bins of m ZZ that we’ve chosen. Fit looks worse in the first 2 bins, but this is just because it’s very zoomed in compared to the other 2. Can see by looking at the “b” parameter that sensitivity is very low in these bin.

15
f 5 Z Parameterisation – P T Z1 Nick Edwards 15 Same thing but in the 5 bins of P T Z1 that we’ve chosen.

16
f 5 Z Parameterisation – P T lep1 Nick Edwards 16 Same thing but in the 5 bins of P T lep1 that we’ve chosen.

17
Conclusions C ZZ seems to be 5-10% different in the TGC samples to the SM ones. Difference seems to be coming mainly from electron channels. –Need to understand where this comes from. First attempts at parameterising “truth yield” as a function of f 5 Z. If C ZZ were the same would be simple to convert this to expected observed events. Then can compare this to afterburner as a cross- check. Nick Edwards 17

Similar presentations

© 2017 SlidePlayer.com Inc.

All rights reserved.

Ads by Google