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pp transverse multiplicity estimators Clear observations of - PowerPoint PPT Presentation

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Studies of particle production in jets using pp transverse multiplicity estimators Clear observations of strangeness enhancement and flow-like effects with pp charged multiplicity in minimum-bias events Recently, ALICE presented

  1. Studies of particle production in → jets using pp transverse multiplicity estimators ๏ Clear observations of strangeness enhancement and “flow”-like effects with pp charged multiplicity in minimum-bias events ๏ Recently, ALICE presented similar measurements in events with a hard (jet) trigger: complementary probe of central impact parameters. ๏ Used “KNO-like” variable as activity classifier (Martin, / ⟨ N TRNS ⟩ R T = N TRNS ch ch PS, Farrington, Eur.Phys.J.C 76 (2016) 5, 299 ) , with TRNS a geometric region transverse to the leading jets ~ a measure of underlying-event activity. ๏ I comment on R T , on the ALICE measurements, and on wishes for the future. Peter Skands (Monash University) VINCIA ALICE Week, November 2020, CERN

  2. What is the “TRANSVERSE” Region? (Not infrared safe) (More infrared safe) (Infrared safe) ๏ In events with a hard trigger = Hardest Track Hardest track-jet Hardest jet (+ generalisations to Drell-Yan, , …) t ¯ t Let hard trigger define φ = 0 TOWARDS region: (in x-y plane) M ultiplicity dominated by hard trigger (jet) TRANSVERSE region: Useful observable definition Beam axis: ⊗ of the “Underlying Event” (Pioneered by R. Field, CDF) Issue: Transverse region can be sensitive to contamination from AWAY region: bremsstrahlung from the hard scattering; will get back to that. Momentum conservation ➤ contains recoil jet (at LO) Note: prefer to express contents as densities (per unit Δ 𝜒 Δ η ) ➤ easier comparisons 2 Multiplicity Probes of the Underlying Event P. Skands Monash U

  3. From Minimum-Bias (MB) to the Underlying Event (UE) ch N ALICE pp, s = 13TeV ) ϕ density Toward region Δ η track p > 0.15 GeV/ c , | | < 0.8 η Away region Δ T ev ๏ Pedestal effect (1983): Transverse region N 4 1/( UA1, Phys. Lett. B 132 (1983) 214-222 • oduction “Maximum “Outside the [jet], a constant E T plateau is observed, whose height Bias” 2 is independent of the jet E T . Its value is substantially higher GeV/ c Minimum than the one observed for minimum bias events.” Bias jet 5 10 15 20 25 30 35 40 Now called the “Underlying Event” leading p (GeV/ c ) T ALI − PUB − 340799 • Multiple Parton Interactions with impact-parameter dependence (eg PYTHIA): Rise from minimum-bias to UE interpreted as a biasing effect . ๏ Small pp impact parameters → larger matter overlaps → more MPI ๏ → higher probability for a hard interaction . ๏ 3 Multiplicity Probes of the Underlying Event P. Skands Monash U

  4. MPI in Minimum-Bias and UE pp Main idea: UE in events 13000 GeV ⟹ ๏ triggered by a hard scattering = ) MPI Number of parton-parton interactions complementary probe of small Prob(n 1 ND impact parameters UE ( p =20) + input to high-p T program @ LHC T <MB> Z <UE> 1 − 10 ๏ The Underlying Event tt (here defined with hard scattering at p T > 20 GeV, but no significant dependence on specific hard process; similar story for Drell-Yan and ) t ¯ t Extreme UE 2 ๏ Has substantially larger average − 10 number of MPI than minimum-bias (as modelled by PYTHIA) 3 − ๏ Still some events have few MPI 10 ~ jets without pedestals? V I N C I A R O O T ๏ Tail towards high numbers of MPI Pythia 8.227 Monash 2013 4 − 10 high-N ch tail of Min-Bias? ↔ 0 10 20 n ๏ (Martin, PS, Farrington, Eur.Phys.J.C 76 (2016) 5, 299 ) MPI 4 Multiplicity Probes of the Underlying Event P. Skands Monash U

  5. The Transverse Activity Classifier R T ๏ Aim: study UE properties (<p T >, strangeness, …) as function of UE multiplicity ~ like we do in min-bias N TRNS • Normalise by average value “KNO-style” variable ⟹ R T = ⟨ N TRNS ⟩ 0.1 0.25 0.5 1 2 3 0.1 0.25 0.5 1 2 3 25 2 > > MPI MPI <n > vs R <b > vs R MPI T MPI T <n <b Monash 13 Monash 13 20 4C 4C 1.5 4Cx 4Cx AU2-CT6L1 AU2-CT6L1 R T ~ 0.25 15 <MB> 1 MB ≡ <UE> 10 R T ~ 1 <UE> R T ~ 0.25 0.5 V I N C I A R O O T V I N C I A R O O T 5 More central <MB> Pythia 8.216 Pythia 8.216 0 0 1.4 1.4 1.2 1.2 Ratio Ratio 1 1 0.8 0.8 0.6 0.6 1 0.5 0 0.5 1 0.5 0 0.5 − − − − log (R ) log (R ) T T 10 10 ๏ (Martin, PS, Farrington, Eur.Phys.J.C 76 (2016) 5, 299 ) 5 Multiplicity Probes of the Underlying Event P. Skands Monash U

  6. TOWARD region - p T spectrum TOWARD region Somewhat analogous to a jet (with ) Δ R ∼ 1 Soft base of jet ( GeV) p T ≲ 3 varies with UE estimator Hard tip of jet ( GeV) ~ p T ≳ 5 independent of UE estimator The UE fluctuates: Hard High UE ➤ “Polluted” jet “tip” of jet Low UE ➤ “Clean” jet Soft “base” of jet ALI-PREL-322959 Low UE ➤ cleaner jets ➤ Interesting for precision jet studies ? Better calibrations ? 6 Multiplicity Probes of the Underlying Event P. Skands Monash U

  7. TRANSVERSE region - p T spectrum TRANSVERSE region ~ the “Underlying Event” increases with the ⟨ p ⊥ ⟩ UE estimator similarly to in ⟨ p ⊥ ⟩ ( N ch ) min-bias One of the classic indicators of collectivity High UE ➤ Harder Spectra Low UE ➤ Softer Spectra ALI-PREL-342263 7 Multiplicity Probes of the Underlying Event P. Skands Monash U

  8. TRANSVERSE region: MC Comparison Solid lines: PYTHIA 8.244 Dashed Lines: EPOS LHC In low-UE events , both In high-UE events , Pythia and EPOS predict PYTHIA does a a too soft p T spectrum reasonable job of modelling the p T in the transverse region spectrum in the transverse region Especially for p T > 1 GeV/c (Probably at least in part due to MPI and CR modelling tuned to high- Naively, could have N ch tail of min-bias) expected PYTHIA good at modelling a single jet with low UE ~ LEP? But remember: here we look TRANSVERSE to the jet. Interestingly (?) something More challenging than collinear fragmentation. similar was seen at LEP 8 Multiplicity Probes of the Underlying Event P. Skands Monash U

  9. TRANSVERSE region: Comparison to LEP? Pythia describes a wide range of LEP event shapes, jet rates, and particle spectra well See eg PS et al., Eur.Phys.J.C 74 (2014) 8, 3024 A longstanding significant exception are the p T distributions transverse to the main jet axis Related? Status: unresolved Many ideas including CR / subleading colour, N-jet merging, thermal tails, … Highlights that low-UE events are particularly interesting to compare with the no-UE events we have in e + e − (However as defined here, these observables are not directly comparable. They cover different regions, have different trigger biases, different q vs g Born-level starting points, and different contributions from extra jets) 9 Multiplicity Probes of the Underlying Event P. Skands Monash U

  10. 𝑺 𝑼 � 𝑺 𝑼 � • • • • Strangeness • • ๏ 2019 analysis: strangeness ratios as functions of p T • Would have liked to start from p T -integrated <N X >/<N Y > as functions of R T • (that would still be useful; Yields are changing at the same time as the p T spectra. Yields first, then spectra.) ๏ 𝑺 𝑼 ๏ • Mesons • TRNS Quite hard to see what is going on in this region All ~ constant 02/03/2020 Adrian Fereydon Nassirpour • Overall trends: PYTHIA underpredicts strangeness, even at low R T • EPOS has the <strangeness> but not the right R T dependence. 10 Multiplicity Probes of the Underlying Event P. Skands Monash U

  11. 𝑺 � � • 𝑺 � • Baryons • 𝑺 � ๏ Baryons: crucial to get full picture; require the formation of • diquarks and/or colour-epsilon structures in the confinement field. • 𝑺 � • • Baryons TRNS Ξ • 𝑺 � • • 𝑺 � 02/03/2020 Adrian Fereydon Nassirpour EPOS predicts large high-p T baryon fractions at high R T not seen in data • PYTHIA underpredicts baryon fractions, especially at high R T Ξ • • Would be interesting to test with QCD CR, Rope Hadronisation, and Shoving 11 Multiplicity Probes of the Underlying Event P. Skands Monash U

  12. Comments & Subtleties: N ch vs N inc vs track-jets vs jets ๏ N ch : cleanest / easiest to meausure • But quite “infrared unsafe”. E.g., a K + always counts as one particle, but a K 0S either counts as zero (if treated as stable or decaying to π 0 π 0 ) or 2 if decaying to π + π - . • Can lead to counter-intuitive biases eg in strangeness fractions vs R T ๏ Alternatively = Identifiable weakly decaying strange hadrons ( S , Λ , Σ , ¯ ) + long- K 0 Σ , Ξ , Ω N inc π ± , K ± , p ± lived prompt charged hadrons ( ) • Less weird biases (but prompt π 0 still “invisible”; use EM information?) ๏ Alternatively measure UE activity in complementary (non-overlapping) region (eg ) N FWD ch • Must be correlated with activity in measurement region to be useful. • If using how to distinguish between low-angle ISR jets and events with many MPI ? N FWD ch Require Forward AND Backward coincidence? Forward AND Inclusive Central? Exploit momentum- ๏ conservation (anti-)correlation between ISR and jet(s) from hard scattering? ๏ Using Jets to Define : φ = 0 • Instead of hardest track, use a clustered (track) jet to define . φ = 0 • Brings in information from more than a single (charged) particle. • Capability to use jets can then also be used e.g. to define exclusive 2-jet events … 12 Multiplicity Probes of the Underlying Event P. Skands Monash U

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