DIFFRACTIVE EVENT CLASSES AT THE LHC New Trends in High Energy Physics 2013, Alushta, Crimea Risto Orava

 TOT   EL +  SD +  DD +  CD +  ND HOW TO CLASSIFY INELASTIC LHC EVENTS AS DIFFRACTIVE or NON-DIFFRACTIVE IN AN EXPERIMENT? The event classes are not uniquely defined.

AN IDEAL EXPERIMENT FOR DIFFRACTIVE SCATTERING IS SUPPOSED TO: Measure rapidity gaps   3 (?) for a maximal  - and p T -span, and/or Measure leading protons for a maximal ( , t,  ) range, and Measure forward systems for full  : acceptance down to M* ~ 1 GeV charged multiplicities (particle id’s!) transverse energies/momenta, and In case of high luminosity, measure timing

Leading Protons measured at -220m from IP1 & IP5 Leading Protons measured at +220m from IP1 & IP5 T1 T2 Castor T2  RO Risto Orava Crimea CMS T1 cms-calorimetry + totem-tracking: unique fwd physics spectrometer for forward physics at the lhc ZDC FSC Leading protons: RP’s at (  147m),  240m Rap gaps & Fwd particle flows: T1 & T2 spectrometers Fwd energy flows: Castor & ZDC Fwd counters at:  60m to  100 (140)m – FSCs, PPS at  240m? Castor

 p T (GeV) CMS ATLAS ALICE LHCb LHC Experiments: p T -  coverage Exp B (T) p T  (GeV) ALICE ATLAS CMS LHCb 4Tm 0.1 Forward detectors can be deployed as diffractive mass selectors. Note: p T min depends on magnetic field strength, noise,... T1 T2 RP  s? FSC p T max ~  s exp(-  )  R. Orava Diffraction 2006 Milos Island CASTOR ZDC HF

p t,min =1 GeV p t,min =0.5 GeV p t,min =0.1 GeV Probability of finding a rap gap (in inclusive QCD events) depends on the p T cut-off KKMRZ: V.A. Khoze, (Durham U., IPPP & St. Petersburg, INP), F. Krauss, A.D. Martin, (Durham U., IPPP), M.G. Ryskin, (Durham U., IPPP & St. Petersburg, INP), K.C. Zapp, (Durham U., IPPP). IPPP-10-38, DCPT-10-76, MCNET-10-10, pp. V.A. KhozeDurham U., IPPPSt. Petersburg, INPF. KraussA.D. MartinDurham U., IPPPM.G. RyskinDurham U., IPPPSt. Petersburg, INPK.C. ZappDurham U., IPPP

M X >3.4 GeV/c 2 (T2 acceptance)  SD d  SD /d  SIBYLL/PYTHIA8 QGSJET-II-4 low mass contribution S. Ostapchenko arXiv: v2 [hep-ph] Correction based on QGSJET-II-3 Correction for the low mass single diffractive cross-section:  Mx < 3.4 GeV = 3.2 ± 1.6 mb Low-Mass Diffraction TOTEM analysis See: Lazlo et al.

An example Monte Carlo event. Pythia (mbr) A MONTE CARLO GENERATED CENTRAL EXCLUSIVE EVENT An example CEP event – Pythia – pp  p + X + p J PC = 0 ++ colour rearrangements stable particles p T cut-offs of the exp! Challenge: how to identify CEP events?

Forward Detectors – Mass Selectors Calculate using the rap gap: lnM X 2 =  Access to small M X iff forward detectors at   > 5. T1, T2 and the FSCs see diffractive systems with decreasing masses – a natural way to select. J. Lämsä &RO

EFFICIENCY OF DETECTING sd EVENTS FSC + T1, T2 FSC T1, T2 WITH FSC, DETECT sd EVENTS DOWN TO M diff  1.1 GeV p T min = 100 MeV for T1 p T min = 40 MeV for T2 J. Lämsä &RO

Single diffraction low  Correlation between leading proton and forward detector T2 M X 2 =  s Rapidity Gap  = -ln   SD  TOTEM analysis

Single diffraction large  correlation between leading proton and forward detector T2  M X 2 =  s Rapidity Gap  = -ln   SD TOTEM analysis

Single diffraction: d  /dt vs.  TOTEM analysis

Miettinen & Pumplin, PRD 1978 Diffraction due to peripheral interactions; fluctuations in : impact parameter 45% number of 45% rapidities10% of the wee partons. B = 6.9 GeV -2 Diffractive cross section at the ISR (0.95 < x F < 1.0) 12/5/2015Risto Orava - LHCP Barcelona

d  /dt (mb) -t (GeV 2 ) total n b y At small -t fluctuations in the no. of wee parton states dominate? Diffractive cross section at the ISR (0.95 < x F < 1.0) Miettinen & Pumplin, PRD 1978 only this chain interacts! 12/5/2015Risto Orava - LHCP Barcelona

d  /dt (mb) -t (GeV 2 ) B = 9.6  1.5 GeV -2 B = 8.0  1.5 GeV -2 B = 6.6  1.5 GeV -2 3 GeV< M < 7 GeV7 GeV< M < 350 GeV 350 GeV< M < 1100 GeV Diffractive cross section at the LHC - speculation n tot n n bb b y yy At small diffractive masses (small  values), fluctuations in number of wee states grows in relative importance vs. b- or y- fluctuations?

How to identify diffraction at the LHC? -Events that have rapidity gaps beyond  > 3 units. - experimentally depends on detector thresholds, p T,min - rapidity correlations  exp(-  ),  1, hadronization models? - rapidity gaps are not unique to diffraction - Diffraction is a coherent phenomenon, each component present with a non-zero probability amplitude in a pp interaction  Classify the events by assigning each pp event a probability to belong to all event classes: SD, DD, CD, ND  Use all the relevant input information to characterize the space-time evolution of an event.

MASS SELECTION OF THE DIFFRACTIVE SYSTEMS -FSC+FSC -T2 +T2 +T1-T1 IP5 +10m p t min =100MeV  = M p* < 10 GeV < 3.4 GeV < 1.2 GeV +14m 40MeV  = >+60m a few MeV  > 7.5 -ZDC+ZDC

Event Classification by the T2s Tracks in both T2s: dd & nd Tracks in  T2: mostly sd (M* > 3.4 GeV) -T2+T2-T1

Event Classification by the T1s&T2s Tracks in both  T2s No Tracks in  T1s : Clean dd! - analysis ongoing Tracks in either +T2 or –T2 No Tracks in T1s: Mostly sd (M* > 3.4 GeV), - But not so clean -T2+T2-T1

A selection of multivariate methods by the Helsinki group: Mikael KuuselaMikael Kuusela, Jerry W. Lamsa, Eric Malmi, Petteri Mehtala, Tommi Vatanen and RO, Sep pp.Jerry W. LamsaEric MalmiPetteri Mehtala Published in Int.J.Mod.Phys.A25: ,2010. Recent work by Mikael Mieskolainen & RO How to classify pp interactions/diffraction in a consistent way at the LHC? Use Multivariate Techniques for Identifying Diffractive Interactions at the LHC.

INPUT INFORMATION FOR MULTIVARIATE EVENT CLASSIFICATION A PROBABILISTIC APPROACH: EACH EVENT BELONGS TO EVERY ONE OF THE EVENT CLASSES WITH A WEIGHT  0. Good-Walker approach event-by-event.

ENERGIES MULTIPLICITIES 23 INPUTS FOR EVENT CLASSIFICATION

see: Mikael Kuusela’s presentation on Wednesday!

mixing between SDL-SDR belonging to other event classes some mixing between DD and SDL some mixing between DD and SDR clean ND events negligible mix of SDL events clean ND events negligible mix of SDR events clean ND events a small mix of DD events large mass DD events - no RAPGAP based on CDF 1.96 TeV analysis by M. Mieskolainen SOFT CLASSIFICATION OF DIFFRACTIVE EVENTS CEP events!

SOFT EVENT CLASSIFICATION - AN EXAMPLE PLOT Normalized to:  inel (CDF) = mb SDL SDR DD ND [mb]

CONCLUSIONS: Diffractive events are distinct Rapidity gap is not an experimental observable Multivariate soft classification of pp events  Access to inclusive analysis of diffraction Need good coverage of forward (backward) regions down to small diffractive masses.