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SMP-J workshop (theory part), Jan 25 2017 ————————————————— https://indico.cern.ch/event/594029/ local participants: H&M + 16 people + 11 remote • attending • remote: Bowen Xia, D. Tsitsonis, Gionata Luisoni, Giulia, Hayk Sargsyan, Soner Zorbakir, • Ionannis Evangelou, Majumder, P. Nason, Carlo Oleari, Paris Gianneios, UOS Byeinghak, • Mehmet • local: Gavin, GiluiamPatrick, Ola, Daniela, Armando, Huansheng, James Currie, Panos • Kokkas, Enrico Bothman, Mikko,Engin,Hunyong, Deniz Cerci, Salim, Kostas, • Peter Richardson, Ugur Intro by Hannes ——— 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 ——————————————————— 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 ————————————————— [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 —————————————————— 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 ——————————————————— 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 —————————————— 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 ————————————————— 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 ————————————— **** Afternoon **** ————————————— Pythia8 status report by Stephen Mrenna, Fermilab ————————————————— 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 ——————————— 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 ——————————— 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 ——————————— 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 —————————— 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 ———————————— 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 ———————— 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?