Download SMP-J workshop (theory part), Jan 25 2017

SMP-J workshop (theory part), Jan 25 2017
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https://indico.cern.ch/event/594029/
local participants: H&M + 16 people + 11 remote
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attending
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remote: Bowen Xia, D. Tsitsonis, Gionata Luisoni, Giulia, Hayk Sargsyan,
Soner Zorbakir,
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Ionannis Evangelou, Majumder, P. Nason, Carlo Oleari, Paris
Gianneios, UOS Byeinghak,
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Mehmet
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local: Gavin, GiluiamPatrick, Ola, Daniela, Armando, Huansheng, James
Currie, Panos
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Kokkas, Enrico Bothman, Mikko,Engin,Hunyong, Deniz Cerci,
Salim, Kostas,
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Peter Richardson, Ugur
Intro by Hannes
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Goals: overview and update of latest theory developments
Where we need help from theory side
Friendly discussions
Dinner at 19:30, Restaurant de la Place
meet 19:00 in R1, walking 25 min or tram
Pt resummation for ttabr by Hayk Sargsyan, U Zurich
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Presenting on ttbar production (no jets here, sorry)
Top: strong coupling to Higgs, crucial to hierarchy problem
cross section at 14 TeV about 1 nb
qT^2 ~ M^2, standard fixed order expansion justified
qT^2 << M^2, large logarithms appear (soft and collinear g’s)
Production of colored particle more complicated than neutral
QCD radiation from final state particles
Color flow between initial and final state
+ top massive: collinear limit not singular (LL unaffected), additional NLL from
large-angle soft radiation
MC: large logs due to soft FSR and due to initial state interference are not under
control through the shower
s18: large distortion on pT spectrum from FSR (+/-40%)
s19: resummation scale variation O(15%) up to qT=100 GeV, but can increase
above 50% at higher qT (Q~2mt)
s20: renormalisation and factorization scale flat 15%
s21: table comparison of NLO+NLL to ATLAS
Jet production at NNLO by James Currie, IPPP
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[very useful plots of k-factors for 7 TeV CMS data!]
gluon PDF directly sensitive to jet data, esp. at high pT
(nice middle plot on s2!)
Would like to consistently include NNLO jet data in NNLO PDF fits without
kinematically limited approximations
Inclusive jet cross section used for running strong coupling
Scale choice tricky: e.g. leading jet pT in event (mu=pT1) or each individual jet pT
(mu=pT)
s11: comparison to CMS jet R=0.5 data: NNLO reduces uncertainty at high pT, but
increases at low pT (scale pT1), differs from jet data
s13: same but using scale mu=pT: same at high pT, better (less scale uncertainty) at
low pT
NNLO/NLO k-factors very small for R=0.7, especially for mu=pT
mu=pT/2 good scale for central, but bad for forward
Rapidity dependent scale choice? mu=1/2*exp(beta*y)
To make most from the NNLO calculations, need some thought into the scale choice
Canonical scale choice needed, instead of taking envelope as uncertainty
At high pT the variation from scale choice small anyway
Discussion:
James: Small R=0.4 optimal for balancing NP uncertainty to scale uncertainty, even
if pT1 vs pT scale choice difference bigger for low pT
Priorities: 8 TeV calculations first for PDF fitters, as SMP-14-001 release is imminent
Complete NLO corrections to dijets, Hua-Sheng Shao, CERN
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NLO QCD becomes standard: automation (e.g. MG5aMC)
=> scale uncertainty reaches 10% levell
Frontier: NNLO QCD and NLO EW, alphas^2 ~ alpha ~ 1%
Necessity for EW corrections (EWC) at TeV scale
Complete automation for QCD+EW, MagGraph5_aMC@NLO:
MadLoop (done!)
FKS (almost done)
CutTools (done!)
MC@NLO (in progress)
Jet definitions: how to define leptons and photons in the presence of jets
s9a: dense plot with many inclusive jet results for D=0.7
s9b: photon PDF is needed at very high pT~3.5-5 TeV
s9c: EWC magnitude
second part to continue 11:15
Jets: issues with NLO MCs by Paolo Gunnellini, DESY
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Summary and status of CMS of POWHEG, HERWIG and Sherpa
Experimentalists view, needing feedback from theorists
POWHEG dijet NLO (starting to look into trijet)
When adding PS, predictions differ quite much
Possible matching issues in trijet?
SHERPA underlying event description
Default Shrimp and Amisic tunes
Good behavior only of strictly MB variables (pT>0), applying pT cut on charged
particles agreement gets worse
SHERPA azimuthal decorrelations
Is difference among parton multiplicities 2,3,4 understood?
HERWIG7 first comparisons
MMHT14 and default tune
CMS tune is not good for UE+ND(!) but default tune is
Inclusive jet cross section still no good with LO H7… but much better than with H++
Current main issue: Delta_min^2J has peak at 0 min many bins, also present for LO
Powheg dijet well established
Powheg trijet under study
Sherpa QCD needs more work
Herwig
SHERPA: multijet calculations, by Enrico Bothmann, Edinburgh
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tree-level + (one-loop) + shower + ME/PS all in one
Single framework due to early focus on matching/merging, i.e. focus on multijet
dynamics
pQCD uncertainties: alpha_s, PDFs, muR, muF
These appear in both ME and PS: traceable
Dedicated re-simulation often too expensive/slow
solutions: interpolation grids (ApplGrid, FastNLO), extended event files
(BlackHat/Sherpa tuple), on-the-fly reweighing of ME, and since 2016, of PS
(Sherpa/Pythia/Herwig)
s6: reweighing faster and more accurate than 100 re-runs
s12: color coherence data vs Sherpa (s11 with other MCs)
take home: sherpa (loves) multijets
merging: several multis @ (N)LO in one sample
color coherence looks good
Upcoming:
EW+QCD NLO
NLO splittings in PS
NNLO+PS
averaged PS with interpolation grids?
HERWIG7: NLO+PS, unc. and PS reweighting by Simon Plätzer, IPPP
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10 year project to replace Herwig with Herwig++ (>= as good)
Precision has become key goal
Herwig++ 3.0 -> Herwig7
NLO matched to parton shower now default
Two showers: angular ordered and dipole
Spin correlations and QED radiation in angular ordered PS
Uncertainties: each block at time, then pin down cross feed
s10: old input files still work, but new-style NLO inputs easier
Herwig7.1 preview:
NLO multijet merging with dipole PS
Leading order two and three jet merging
Soft physics modeling improved
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Afternoon
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Pythia8 status report by Stephen Mrenna, Fermilab
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Traditional new bigger releases at the beginning of the year
DIRE plugin in addition to VINCIA
(DIpole REsummed shower), “no iffy subleading logs”
s7: DIRE does well on ATLAS dijet azimuthal
s8: good also on ATLAS phiStar_eta
In Situ shower parameter variation, giving vector of alternative weights for each
event
Heavy flavor jets by Adam Kardos, Debrecen
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Low pT individual jets with R ~ O(0.1)
Deadcone effect around top Rd.c. ~ mt / pT
At high pT top decay products form a single jet
Top tagging efficiencies 30-50%, similar to b-tagging some years ago (now ~70%);
high b-tagging efficiency is welcome
Interesting to see break-down of HEPTopTagging above 1 TeV due to finite detector
granularity
ttbb production: NLO+PS may not be enough, but NNLO+PS is very far
b’s from top decay, hard process (gluon splitting), from shower (gluon splitting);
would be nice to distinguish the source
ttH production: development of multiobject taggers with deep learning can help
ttV(V), tt(2)gamma production
Event generation is important, using suppression to enrich analysis region (up to 800
GeV for boosted analysis, less than 500 GeV in the past)
Sudakov (TMD) resummation in back-to-back dijet, Bo-Wen Xiao, Central China
Normal University
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NLO can describe dijet azimuthal decorrelation up to close to DeltaPhi~pi
Breaks down in fully back-to-back region due to multiple soft gluon radiation
becoming important
Dijet asymmetry and deltaPhi very useful to characterise dijet data in pp and AA and
studying QGP medium effects
Sudakov resummation important to describe back-to-back dijets
Jet spectra with a small radius by Frederic Dreyer, MIT
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Degree of consistency between data and theory at different R provides a powerful
check of accuracy
Investigate R-dependence of jet spectra, focusing on small R
Tools:
Fixed order calculations, NLO, NNLO R-dependent (NLO 3-jet)
Small-R resummation
Non-perturbative effects from MC
Small-R resummation with inclusive microjet fragmentation function
s6: (un)correlated scale variation, pT>100 GeV
s8: NNLO calculation sizably different from NLO only
s20: Powheg+Pythia and Powheg+Herwig differ from each other and NNLO
s13: UE + had with 4 different MCs and two tunes (6 variants)
s15,16: good agreement with ATLAS data for NNLO+k-factors
s19: comparison to CMS 2.76 TeV data (for R=0.2,0.3,0.4)
Multiple R values powerful probe of systematics
Suggestions:
R=0.1, 0.2 (enhances hadronization, suppresses UE)
R=0.4 (mixes all effects)
R=0.6 or 0.7 (enhances UE, suppresses hadronization)
Need perturbative control over the full R range, gains insight into using NNLO_R ja
LL_R:
R dependence strongly modified compared to NLO
LL_R resummation can be important for R<0.4
Quark and gluon jet discrimination by Matthew Schwartz, Harvard
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New physics mostly quark jets, backgrounds mostly gluon jets
Quark/glue basic: CF=4/3 ~ 1.3 vs CA = 3
P(q->gg) vs P(g->gg)
Looked at 10,000 variables: jets.physics.harvard.edu/qgv
Two best:
particle counts; better spatial and energy resolution better (particles >
caloclusters > subjets)
liner radial moment (girth), similar to jet broadening
Then top-5 boosted decision tree (BDT)
ATLAS: procedure to disentangle quark and gluon jets
Relative pure samples, dijets for gluon, gamma+jet for quark
Pythia and Herwig agree with data on quarks, but differ on gluons: data closer to
Herwig, that has less quark/gluon difference
s10: Monte Carlos can be improved
New approach, deep learning: many hidden layers, computationally intensive, but
modern algos+GPUs effective
Easy usability with Python libraries
Jet images: treat energy deposits as an image (e.g. W jet)
Deep learning for Q vs G: greyscale DNN better than BDT-5, color DNN significantly
better
Especially good at 1 TeV, less so at 100 GeV; more particles and information at high
pT
cDNN trained on Herwig worked well on Pythia and vice versa so perfect MC not
needed. Herwig has less Q/G differences than Pythia, but these differences are still
the same
Traditional variables: shape (mass, girth, n-jettiness) and count (#particles, #subjets)
Could the network be trained on data? Probably yes, could be useful
Multiplicity jump b-tagger by Todd Huffman, Oxford
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ARXIV:1701.06832
problem: b-tagging drops above 200 GeV, plots rarely go above 600 GeV
why: secondary vertex tracks get very collimated, but more importantly b lifetime
gets long enough to hit first tracking layers
solution: don’t worry about vertices, just count multiplicity increase
history: tried in 80’s and 90’s fixed target expts at hadron machines; did not work
very well, tails in signals, but modern si pixel detectors have very high granularity
s6: b-baryon energy fraction quite independent of energy
s8: long tail in hit fraction for jets with B baryon
s11: b-efficiency for taggable ones 50-60%, fake rate for all 10-20%; not so
impressive by itself, but flat vs pT at TeV scale (this is the party piece)
s15: insensitive to pileup (up to mu=200), possibly because restricting to a narrow
cone R<0.04 around jet center
s18: 2 hidden-layer ANN worked best
s23: efficiency/mistag much higher for NN than cut-based
Biggest problem: hit information is normally not saved
=> ask Matti Kortelainen
100e6*TMath::Pi()*0.04*0.04/(TMath::TwoPi()*2*1.3)
(double) 3.076923e+04
so 30-120 kB might be needed, unless zero-suppressed
Color coherence and color entanglement by Joe Osborn, U. Michigan
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Transverse-Momentum-Dependence (TMD)
PDFs: f(x,Q^2) -> f(x,kT,Q^2)
two scale problem: LambdaQCD < kT << Q^2
Color entanglement arises from interference between soft gluons in strong coupling
regime; not predicted in e+eColor coherence: originally 30 years ago in e+e- -> 3 jets, destructive interference of
gluons in final state
Both result from interference of gluon radiation between ISR and FSR
Some differences: are these phenomena related, and if so, how?