Post on 29-Sep-2020
Introduction
l An alternative framework for probing physics beyond SM is effective field theories(EFT)
the SM Lagrangian is supplemented by additional dimension-D operators
ℒ"#$ = ℒ'( +∑+,(.)
0.12 Ο4(5)
4,5
l 𝑐4(5) specify the strength of new interaction, are known as Wilson coefficients
l In this analysis, limits are set in Wilson coefficients of dimension-6 operators
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Effective field theories
l There are two bases for a dimension-6 EFT Lagrangian
l SILH: the basis of Strongly-Interacting-Light-Higgs Lagrangian
l SMEFT: the “Warsaw” basis of SM Effective Field Theory Lagrangian
l For different bases, different Wilson coefficients take effect
l Parameter ranges for each EFT parameter
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EFT analysis workflow
l Generate samples with MadGraph5, and output EVNT.root
l Change parameters in the param_card, generate a variety of samples
l Use Rivet to select over those EVNT.root and generate histograms into .yoda files
l Calculate Reweight scale factors, and applying reweight factors to all of the yoda files
l Use Professor software to make 2nd order polynomial interpolation over reweighted .yoda
files
l Scan over parameters in EFT through gamma-combo to obtain confidence intervals of these
parameters.2019/12/15 3
0 2 4 6 8 10 12 14 16 180
0.1
0.2
0.3
0.4
0.5
0.6Powheg ggH
SILH c=0
γγpT
0 0.5 1 1.5 2 2.5 3 3.5 4~
5
10
15
20
25
30
~
Powheg ggH
SILH c=0
From Amed
30 GeVjetexcl N
N_j_30
MadGraph generation with SILH modell Try to reproduce SM expectation with SILH model
generate ggH samples with commands below
l Import model HEL_UFO(set all Wilson coefficients to 0)
l Generate p p > h NP=1 QED=1 QCD=99, h > aa NP=1 QED=2 @0
l Add process p p > hj NP =1 QED=1 QCD=99, h > aa NP=1 QED=2 @1
l Add process pp > hjj NP=1 QED=1 QCD=99, h > aa NP=1 QED=2 @2
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l ggH125 Powheg+Pythia(H+j) as comparisonl There is still deviation on N_j_30 and pT_yyl More jet numbers in SILH(coefficients = 0)
than Powheg ggH
pT_yy
EFT parameters tested in ggH samples
l Choose two EFT parameters as tested( 𝑐89 & 𝑐8: : 5 points for per parameter, 25 points
totally), while keeping other Wilson coefficients to 0:
𝑐89 : [-0.001 , -0.0005 , 0.0 , 0.0005 ,0.001]
𝑐8: : [-0.001 , -0.0005 , 0.0 , 0.0005 ,0.001]
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Event Selection with HGamRivetl Perform event selection on EVNT.root from samples generated by MadGraph5 to generate
histogram(.yoda files)
l Selection Criteria for Photons(same as H->yy fiducial region):
l pT > 25GeV
l |eta| <1.37 or 1.52 < |eta| < 2.37
l Photons.size > 2 & relative pT cut 0.35(0.25)
for leading(sub-leading)photon
l 105GeV < m_yy < 160GeV
l HGamRivet can save distributions of a set of variables, and they will work in the period of limit-
setting.
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Rivet results of N_j_30 and pT_yyl HGamRivet can get N_j_30 and pT_yy distributions in the events passing event selection
l For different Wilson coefficient sets, the histograms would be quite different, even with one or two order of magnitude
difference
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0 0.5 1 1.5 2 2.5 3 3.5 490
210
210×2
210×3
210×4
210×5
210×6
210×7
210×8 cg= 0.001cg= 0.0005cg= 0.0cg= -0.0005cg= -0.001
scan over cg in N_j_30
0 0.5 1 1.5 2 2.5 3 3.5 4
60708090
210
210×2
210×3
210×4
210×5
210×6 tcg= 0.001tcg= 0.0005cg= 0.0tcg= -0.0005tcg= -0.001
scan over tcg in N_j_30
0 2 4 6 8 10 12 14 16 185−10×84−104−10×2
3−10
3−10×2
2−10
2−10×2
1−10
1−10×2
12
1020
HistoEntries 18Mean 5.862Std Dev 3.246
cg= 0.001cg= 0.0005cg= 0.0cg= -0.0005cg= -0.001
scan over cg in pT_yy
0 2 4 6 8 10 12 14 16 185−10×64−104−10×2
3−10
3−10×2
2−10
2−10×2
1−101−10×2
12
1020 Histo
Entries 18Mean 5.815Std Dev 3.238
tcg= 0.001tcg= 0.0005cg= 0.0tcg= -0.0005tcg= -0.001
scan over tcg in pT_yy
Re-weightl Get distribution shapes of kinematic variables(like N_j_30 and pT_yy) from MC Powheg ggH samples. Mimic it with our
histograms with all of Wilson coefficients set to zero through multiplying by scale factors.
l Those scale factor are then applied to all of the other histograms with some coefficients changed, which is the process of
re-weighting.
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BinContent of N_j_30 l Apply reweight factors to all ofthe N_j_30 histograms
l The factors in N_j_30 are[1.84556, 1.14742 , 0.545151,0.272658]
l Replace old histograms with reweighted ones beforeinterpolation
Interpolation with Professor
l Interpolation with 2nd polynomial functions
there are 3 parameters for the model function in 1D interpolation.
Since two SILH coefficients(cG and tcG) get involved in the interpolation, there are 6 parameters for the model.
l Interpolation parameters in 4 bins of N_j_30 varying cg and tcg in SILH
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Parameters of the model function
Interpolation over one parameter l 1D interpolation results for cG
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l 1D interpolation results for tcG
cG tcG
Njets=1 Njets=1
l The interpolated values of the cross-sections are in excellent agreement with those predicted by theevent generator
Limit setting with gamma-combol Limits on Wilson coefficients are set by means of a likelihood function
l Input distribution of data(.HepData file) and interpolation results from professor
l 1D Scan over cg/tcg to observe limits of 95% and 68% CI
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Limit setting with gamma-combo
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gc0.001− 0.0005− 0 0.0005 0.001
1-C
L
0
0.2
0.4
0.6
0.8
1
1.2 EFT Run 2 Scan of cg (Prob)
68.3%
95.5%
GammaCombo
Reweighted results (cg) publication results (cg)
gc~0.001− 0.0005− 0 0.0005 0.001
1-C
L
0
0.2
0.4
0.6
0.8
1
1.2 EFT Run 2 Scan of ~cg (Prob)
68.3%
95.5%
GammaCombo
Reweighted results (tcg)
publication results(tcg)
Next to do
l Increase the number of MadGraph events from 1 thousand to 10 thousand to follow the
workflow again. (Have submitted condor jobs.)
l Store more variables(like pT_yy, excl_N_j_30) into histograms in the event selection
l Replace old distribution of data with Summer_2019_nofJVT.HepData in gamma-combo,
since there are new pT_yy binning and data update in 2019 HepData
l Change other coefficients in SILH model to compare CI limits with publication
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Backup
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