Higgs Branching Ratio H→bb, cc, gg for new detector models at ILD Masakazu Kurata, Ryo Yonamine, Hiroaki Ono ILD meeting 02/27/2019
Higgs Branching RatioH→bb, cc, gg
for new detector models at ILDMasakazu Kurata, Ryo Yonamine, Hiroaki Ono
ILD meeting
02/27/2019
Introduction • Start to re-optimize new detector models using physics
benchmarks
• Higgs analyses are one of the most important benchmark processes for detector optimization at ILD
• H→bb, cc, gg measurement:• Flavor tagging is indispensable for jet flavor separation
• Try@500GeV, focus on ννH process
• Based on Hiroaki’s 1TeV analysis in DBD era
• So far, do not consider separation of ZH and VBF• But, considering it for future plan
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LCFIPlus summary: Comparison among any situation• Ryo made great efforts to resolve internal problems in
LCFIPlus for new verson of iLCsoft
• Use 6f flavor tag samples: bbbbbb, cccccc, qqqqqq
• Tune MVA training torecover performance tothose of DBD era
• Almost same performanceas DBD era
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Comparison between large and small• MVAoutput between large and small
• Very similar…
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but
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Small has excess hereAll the jet flavors have excess
After selectionLargesmall
Check samples• Check nnH→nncc sample
• Compare MVAoutput between Large and Small
• Same tendency can be seen• This excess directly reflects on measurement precision
• No bias on c-jet likeliness from:• Event selection
• Beam background rejection
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Status of analysis
Iso
late
d L
epto
n ID Look for
electron and muon
Jet
Clu
ster
ing Durham with beam background rejection
2 jet clustering
Even
t se
lect
ion Reject
backgrounds
Preselection
MVA
Polarization (e-, e+)=(-,+) (+,-) (-,-) (+,+)
Luminosity(fb-1) 1600 1600 400 400
• ECM=500GeV• Luminosity: 4ab-1
• Analysis flow
• Signal and backgrounds• Signal: use nnH→nnbb, nncc, nngg• Backgrounds: 2f, 4f, 5f, 6f, aa, ZH, nnh→nn(no bb,cc,gg) 7
Beam background rejection
• 𝑦𝑖𝑗 =2min(𝐸𝑖
2, 𝐸𝑗2)(1−cos 𝜃)
𝐸𝑣𝑖𝑠2 , 𝑦𝑏𝑒𝑎𝑚 =
2𝐸𝑖2α2(1−cos 𝜃)
𝐸𝑣𝑖𝑠2
α: beam rejection parameter
smaller→ beam rejection becomes stronger• Particle i with yij>ybeam is discarded
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ννZ@500GeV(DBD) • 2 jet clustering• Parameters are tuned
for better result
w/o beam b.g. rejectionKtDurhamValencia
Apply to nnH events• Apply Durham beam b.g. rejection to nnH→nnbb
events
• Parameter scan to make Higgs mass distribution better• 5.5 seems best for both detector models
large small
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Preselection
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• Just reject backgrounds which are trivial ones• Signal tail part of each variable is rejected
• No Isolated lepton
• Njets=2
• Npfo≧5
• Evis≦300
• 40≦m2jets≦200
Multivariate Analysis
• Expect better background rejection efficiency than cut based
• Need to reject other Higgs processes as much as SM backgrounds
• Important to separate both SM backgrounds and other Higgs processes for backgrounds
• Use Binary Classification• Due to the phase space difference of each background
component
• Signal vs.: other Higgs, 2f&4f, 5f&6f&aa
• 3kinds of classifier trained
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Input variables• Need to suppress bias between H→bb,cc and H→gg
• Reduce difference by combining variables• m2jets, cosθH, cosθjj, Ej1, cosθj1, Ej2, cosθj2, mmiss, Pt,
Principal Thrust, Major Thrust, Log(y23)*npfo, Log(y34)*npfo
• Example: signal vs. 2f&4f
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Stacked
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vs. other Higgs vs. 2f&4f
vs. 5f&6f&aa
• Determine operation point:MVA_1>x.xx && MVA_2>y.yy && MVA_3>z.zz
Cut table• Large, (e-, e+)=(-,+) polarization,
L=1600fb-1
Signal efficiency: typically ~60% for all the polarization & detector models
Significance: 275.3
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Template fit• Toy MC to extract the measurement precision of H→bb,
cc, gg• From LCFIPlus output, calculate x-likeliness in each event:
x1, x2: LCFIPlus output(b, c, bc=c/(b+c))
• Create 3-D template with those b, c, bc likeliness
• Fitting is performed according to Poisson statistics• 3 scale factors of signal events are parameters, other fixed:
• Do Toy MC• 10000 pseudo-experiments performed
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Template examples• Templates are 3-D, so project into 2-D space
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Results• Large detector, all the polarization
• Small detector, all the polarization
• Comparison with DBD for check• (-0.8, +0.3) 500fb-1
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Process(-,+) H→bb H→cc H→gg
Precision(%) 0.69 6.79 2.89
Process(-,+) H→bb H→cc H→gg
Precision(%) 0.66 6.2 4.1
IDR-L DBD(125GeV scaled)
Plot• Results of branching ratio measurement
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Plot• Combined only
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• We construct pseudo jet flavor taggers for comparison
• We do the pseudo experiments with those (artificially) perfect & pessimistic case
• Artificial fraction• Perfect Pessimistic
• According to those fraction, jet flavor is determined• All the samples are processed, and measurement precision is
estimated
Impact of flavor tagging on precision measurement
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• (-,+) polarization, L=1600fb-1
• Better flavor tagging is very important for better precision• b-tagging is excellent• Better c-tagging is important
Comparison between pseudo experiments
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Plot• Impact of flavor tagging performance on measurement
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Summary & Prospects• We can get the results of both detectors & all the
polarizations• B-tagging is very excellent already
• Small detector has better performance than large in H→cc measurement
• Coming from c-tag output distribution
• Thanks to stronger B-field(4.0T>3.5T)?
• We will investigate that point
• Template fitting is being checked now• We cannot see huge bias on binning so far(backup)
• Checking and discussing fitting stability with reviewers now
• Note & Going to IDR input23
Backups
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MVA output
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vs. other Higgs vs. 2f&4f
vs. 5f&6f&aa
• large
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• small
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Check template fit• Need to check
• Num. of empty bins: must be as small as possible
• Bins with small statistics: need to avoid
Should be checked
• Num. of binning is important• To include the shape effect
• This is the advantage of template fit
• The tendency of template binning:• b-likeliness is very robust: num. of bin is 2 enough
• H→gg is relatively robust: don’t need much care about (b, c, bc)=(0.0,0.0,0.0) value
• Shape of c & bc-likeliness is very important
⇒H→cc precision varies very much 28
One case• Num. of bins: (b-, c-, bc-)=(2,3,3) (LCWS: (10,10,10))
• Variable bin size is used(LCWS: 0.1 interval from 0 to 1)
• Check the bin stat. (all the events accumulated)
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b-likeliness<0.4Num. of empty bins: 2Smallest stat: 1072
b-likeliness>0.4Num. of empty bins: 7Smallest stat: 904.8
More aggressive case• Num. of bins: (b-, c-, bc-)=(2,5,5) to include shape info more
• Variable bin size is used
• Check the bin stat. (all the events accumulated)• Typically, num. of events in a bin is >~100
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b-likeliness<0.4Num. of empty bins: 10Smallest stat: 98.1
b-likeliness>0.4Num. of empty bins: 19Smallest stat: 11.0
Check different binning effect• (e-,e+)=(-,+),L=1600fb-1
• Large:
• Small:
• Small is better for c-tagging• Results does not change so drastically, but H→cc varies if shape info includes more(have to be
careful of statistics)• Zero bins do not affect well• We use (2,5,5) binning for result
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Large(10,10,10) H→bb H→cc H→gg
Precision(%) 0.43 3.80 1.68
Large(2,3,3) H→bb H→cc H→gg
Precision(%) 0.43±0.01 3.98±0.02 1.76±0.01
Small(2,3,3) H→bb H→cc H→gg
Precision(%) 0.43±0.01 3.69±0.02 1.81±0.01
Small(10,10,10) H→bb H→cc H→gg
Precision(%) 0.42 3.55 1.73
Large(2,5,5) H→bb H→cc H→gg
Precision(%) 0.43±0.01 3.88±0.02 1.70±0.01
Small(2,5,5) H→bb H→cc H→gg
Precision(%) 0.44±0.01 3.56±0.02 1.77±0.01
Optimization with 2 detector models
• Re-optimize ILD detector• Revisit optimization of cost and detector performance
Detector models ILD-L ILD-S
B-field 3.5T 4T
VTX inner radius 1.6cm 1.6cm
TPC inner radius 33cm 33cm
TPC outer radius 180cm 146cm
TPC length (z/2) 235cm 235cm
Inner ECAL radius 184cm 150cm
Outer ECAL radius 202.5cm 168.5cm
Inner HCAL radius 206cm 172cm
Outer HCAL radius 335cm 301cm
Coil inner radius 344cm 310cm
ILD-L ILD-S
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Check samples• Beam background rejection?
• No bias from beam background rejection
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Before beam b.g. rejectionLargeSmall