DIFFRACTIVE EVENT CLASSES AT THE LHC New Trends in High Energy
Physics 2013, Alushta, Crimea 23.-29.9.2013 Risto Orava Slide 2 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. Slide 3 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
ids!) transverse energies/momenta, and In case of high luminosity,
measure timing Slide 4 Leading Protons measured at -220m from IP1
& IP5 Leading Protons measured at +220m from IP1 & IP5 T1
T2 Castor T2 RO Risto Orava Crimea 25.9.2013 CMS T1 cms-calorimetry
+ totem-tracking: unique fwd physics spectrometer for forward
physics at the lhc ZDC FSC Leading protons: RPs 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 Slide 5 0-12-10-8-6-4-2+2+4+6+8+10+12 p T
(GeV) 0.1 1 10 100 1000CMS ATLAS ALICE LHCb LHC Experiments: p T -
coverage Exp B (T) p T (GeV) ALICE0.2-0.50.1-0.25 ATLAS 2.0 0.5 CMS
4.0 0.75 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 Slide 6 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, 2010. 19pp. V.A. KhozeDurham U., IPPPSt.
Petersburg, INPF. KraussA.D. MartinDurham U., IPPPM.G. RyskinDurham
U., IPPPSt. Petersburg, INPK.C. ZappDurham U., IPPP Slide 7 M X
>3.4 GeV/c 2 (T2 acceptance) SD d SD /d SIBYLL/PYTHIA8
QGSJET-II-4 low mass contribution S. Ostapchenko arXiv:1103.5684v2
[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. Slide 8 An
example Monte Carlo event. Pythia 8.176 (mbr) A MONTE CARLO
GENERATED CENTRAL EXCLUSIVE EVENT An example CEP event Pythia 8.176
pp p + X + p J PC = 0 ++ colour rearrangements stable particles p T
cut-offs of the exp! Challenge: how to identify CEP events? Slide 9
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. Lms &RO Slide 10 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.
Lms &RO Slide 11 Single diffraction low Correlation between
leading proton and forward detector T2 M X 2 = s Rapidity Gap = -ln
SD TOTEM analysis Slide 12 Single diffraction large correlation
between leading proton and forward detector T2 M X 2 = s Rapidity
Gap = -ln SD TOTEM analysis Slide 13 Single diffraction: d /dt vs.
TOTEM analysis Slide 14 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 -
15.5.2013 Slide 15 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 - 15.5.2013 Slide 16 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?
Slide 17 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.
Slide 18 MASS SELECTION OF THE DIFFRACTIVE SYSTEMS -FSC+FSC -T2 +T2
+T1-T1 IP5 +10m p t min =100MeV =3.1-4.7 M p* < 10 GeV < 3.4
GeV < 1.2 GeV +14m 40MeV =5.3-6.5 >+60m a few MeV > 7.5
-ZDC+ZDC Slide 19 Event Classification by the T2s Tracks in both
T2s: dd & nd Tracks in T2: mostly sd (M* > 3.4 GeV)
-T2+T2-T1 Slide 20 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 Slide 21 A selection of multivariate
methods by the Helsinki group: Mikael KuuselaMikael Kuusela, Jerry
W. Lamsa, Eric Malmi, Petteri Mehtala, Tommi Vatanen and RO, Sep
2009. 32pp.Jerry W. LamsaEric MalmiPetteri Mehtala Published in
Int.J.Mod.Phys.A25:1615-1647,2010. 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. Slide 22 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. Slide 23 ENERGIES
MULTIPLICITIES 23 INPUTS FOR EVENT CLASSIFICATION Slide 24 see:
Mikael Kuuselas presentation on Wednesday! Slide 25 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! Slide 26 SOFT
EVENT CLASSIFICATION - AN EXAMPLE PLOT Normalized to: inel (CDF) =
58.96 mb SDL SDR DD ND 5.42 5.42 4.97 43.15 [mb] Slide 27
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.
