Probing collective effects in hadronisation with the extremes of the underlying event
 548 Downloads
 2 Citations
Abstract
We define a new set of observables to probe the structure of the underlying event in hadron collisions. We use the conventional definition of the “transverse region” in jet events and, for a fixed window in jet \(p_\perp \), propose to measure several discriminating quantities as a function of the level of activity in the transverse region. The measurement of these observables in LHC data would reveal whether, e.g., the properties of “lowUE” events are compatible with equivalent measurements in \(e^+e^\) collisions (jet universality), and whether the scaling behaviour towards “highUE” events exhibits properties of nontrivial softQCD dynamics, such as colour reconnections or other collective phenomena. We illustrate at \(\sqrt{s} = 13 \,\mathrm{{TeV}}\) that significant discriminatory power is obtained in comparisons between MC models with varying treatments of collective effects, including Pythia 8, epos, and Dipsy.
Keywords
Underlie Event Baryon Production Multiple Parton Interaction Colour Reconnection Hard Scatter1 Introduction
The “jet pedestal effect”, now called the underlying event (UE), was first observed by the UA1 experiment at CERN’s \(\mathrm {Sp}{\bar{\mathrm p}}{\mathrm S}\) collider, in an early study of jet events [1]. This study concluded that, outside the core of a jet, “a constant \(E_\mathrm{T}\) plateau is observed, whose height is independent of the jet \(E_\mathrm{T}\)” [1]. The plateau was substantially higher than that observed in minimumbias events^{1} at the same energy [3, 4, 5, 6]. Its characteristics have since been studied extensively, in pp and \(p\bar{p}\) collisions over two orders of magnitude in Centre of Mass (CM) energy, from RHIC [7, 8] through Tevatron [9, 10, 11, 12] to LHC energies [13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25], the latter most recently extending to measurements at 13 TeV [26].
Theoretically, the UE is often roughly defined as the collection of particles produced in a single hadronic interaction which do not originate from the primary (“hard”) parton–parton scattering, nor relate directly to it in the form of hadronised parton showers. However, since the hardinteraction initiators are coloured (quarks or gluons), confinement implies that it is in general not possible to define uniquely which hadrons were produced by the hard interaction and which belong to the UE.
Instead, physical observables that measure phasespace regions “outside the core of jets” are used to provide operational definitions of the UE. Two main definitions are in current use: the most common one is a purely geometrical definition of the UE as an angular region transverse to a leading charged particle or jet [9], typically spanning \(90^\circ \pm 30^\circ \); an alternative definition of the UE is what remains when hard jets have been removed, an early example of this being the “Swiss Cheese” method introduced by CDF [10] now superseded by socalled “jet median/area” techniques proposed in [27] and used e.g. by CMS [19, 28].
In addition to the average UE properties, several studies at the LHC have now also measured observables sensitive to the perevent fluctuations. These include the ATLAS [14] study of the widths of the chargedparticle density and scalar\({p_{\perp }}\) sum distributions, and the CMS [22] study of jet multiplicity in the UE. Such studies are crucial to pin down the underlying physics mechanisms, revealing e.g. potentially interesting deviations from Poissonian fluctuations, and to what extent (soft) jets are required to build up the UE activity. In this paper, we propose to go a step further, and study the UE properties as a function of its insitu level; this will allow a detailed exploration of the evolution of quantities such as strangeness fractions, particle spectra, and (soft) jet rates, from low to high underlying activity. Though we focus mainly on identifiedparticle rates, we emphasise that the method is general and can be applied to any UE property as a function of any measure of UE activity. The explicit studies we propose are inspired by—and are intended to be complementary to—minimumbias studies as a function of multiplicity. Of special interest is whether events with very low UE levels exhibit particle yields and spectra more consistent with LEP fragmentation models than those of their higherUE counterparts, and whether highUE events exhibit any clear signs of flow or other collective effects. A complementary recent interesting proposal is that of [29] which suggests to measure similar observables using forward multiplicity as the indicator of event activity.
On the theory side, the fragmentation process in the simple reference case of hadronic Z decays is believed to be relatively well understood, and shower+hadronisation models such as those implemented in the generalpurpose Monte Carlo models Herwig [30], Pythia [31], and Sherpa [32], are capable of describing the majority of \(e^+e^\rightarrow \mathrm {hadrons} \) data well (see e.g. the plots available on mcplots [33]). However, moving to pp collisions, several additional complications arise, including initialstate radiation, beam remnants, diffraction, and multiple parton interactions (MPI). The corresponding physics models are therefore necessarily also more complicated. Starting from Rick Field’s observation with his “Tune A” [34] of the Pythia 6 MPI model [35, 36] to Tevatron data [10] that a much higher degree of colour correlation than anticipated (between partons from different MPI systems) was required to fit the \(p_\perp \) spectra of particles in the UE at CDF, there have appeared progressively more indications that the physics of hadron collisions is more complicated than was previously thought. The clues include the increase of the average \(p_\perp \) with event multiplicity [35, 37, 38, 39, 40, 41, 42], the large yields of hyperons [43, 44, 45, 46, 47, 48, 49, 50], and the overall relatively steep increase of average \(p_\perp \) with hadron mass [45, 46, 49, 50, 51, 52, 53, 54] observed in pp collisions. There are also more subtle indications, such as the unexpected ridgelike structure in twoparticle correlations at low \(\Delta \phi \) in highmultiplicity minimumbias pp collisions observed by CMS and ATLAS [55, 56]. To shed further light on these effects, many studies of minimumbias events now focus on the scaling with chargedparticle multiplicity (see e.g. [29, 41, 42, 57, 58, 59]), with a special emphasis on the highest accessible multiplicities. Here we extend this type of study to the UE context.

The strange quark, having a mass very near \(\Lambda _\mathrm {QCD} \), can act as a sensitive probe of any changes to the energy density available at the time of hadronisation. Simply put, the more strange quarks we see, relative to u and d quarks, the higher the effective string tension or effective temperature we would extract, depending on the model. Note, however, that the following two kinematic effects can also affect the interpretation of strangeness ratios: firstly, the energy cost of producing a strangequark rest mass, as compared to say an up or down one, is relatively more significant for a hadronising system with small invariant mass than for one with large invariant mass; thus we expect smallmass systems to exhibit lower strangeness ratios simply due to phasespace restrictions. Secondly, in the presence of cuts on \(p_\perp \), the modelling of \(p_\perp \) spectra affects the ratio of the observed to total strange production.

Baryon production probes a unique colour topology and has clear experimental signatures. Unlike mesons, baryons do not appear naturally in the “leadingcolour” approximation (\(N_C\rightarrow \infty \)), which is so heavily relied upon by MC event generators. This makes their production somewhat more difficult to model and interpret but also more interesting as a probe of aspects beyond leading \(N_C\). The dependence on mass and strangeness can be studied in detail, especially via dedicated Particle Identification (PID) capabilities. For example, a recent ALICE study managed to identify and study strongly decaying excited states such as \(\Sigma (1385)^{\pm }\) and \(\Xi (1530)^0\) [50].
In this study, the most powerful model discrimination arises from taking the ratios of averaged identifiedparticle yields as described in Sect. 3. This highlights the salient physics while minimising the effects from global scaling differences between the considered models.
Though we focus on central rapidities in this paper, we round off by noting that, at forward rapidities, the particle production is increasingly sensitive to the hadronisation of the beam remnants, and to diffraction. In the context of MPI models, the amount of energy (both total and transverse) scattered into the forward direction is also sensitive to the shape of parton distribution functions (PDFs) at low x (see e.g. [60]). Thus measurements at forward rapidities (e.g. AFP, ALFA, LHCb, LHCf, TOTEM) are uniquely sensitive to these aspects and are required to understand the full dynamics. An obvious example of interest is the colour structure of the beam remnant, which is tied in with that of the MPI by overall colour conservation.^{2} Studying the fragmentation process for diffractively tagged events would also furnish insights complementary to those provided by the measurements we propose here.
2 Monte Carlo models
MC generators, versions and tunes used in this paper.
Version  Tune  

Dipsy  ThePEG++ 20150811  NoSwing 
Dipsy  ThePEG++ 20150811  Rope 
epos  1.3  LHC 
Pythia 8  8.210  Monash 
Pythia 8  8.210  Monash + new CR 
2.1 Pythia and colour reconnections
The description of softinclusive physics in Pythia is rooted in perturbation theory, starting from a resummed (unitarised) picture of multiple perturbative parton–parton interactions (MPI) [35] supplemented by (interleaved) \(p_\perp \)ordered parton showers [63]. This builds up a partonic substructure which can act as the starting point for the nonperturbative modelling of each event.^{3} However, when there is more than one MPI the perturbative era does not completely fix the detailed colour structure of the beam remnant, nor does it fix the colour correlations between individual MPI systems [64]. Additionally, within each system, ambiguities beyond leading colour could affect the formation of strings, and strings could conceivably even interact dynamically in the limited time between formation and fragmentation. Recent years have therefore seen increased explorations of new alternatives though so far only a part of the full range of possibilities have been explicitly addressed [65, 66].
The default modelling of colour flow in Pythia 8 [67], which is the one used by the baseline Monash tune [60], is as follows: the partons of each MPI system are either allowed to form their own “skeleton” in colour space or, with a usermodifiable probability, they are merged with the colour structure of a higher\(p_\perp \) MPI system. In the latter case, each parton from the lower\(p_\perp \) system is merged onto a string piece of the higher\(p_\perp \) system where it will cause the least increase in total string length. Within the context of this model, the minimisation of string length is likely to be a physically reasonable dynamical principle, representing potentialenergy minimisation, but there is no a priori basis for guessing precisely what reconnection probability to choose, nor whether it should be constant at all CM energies.
More recently, a model that attempts to anchor itself more firmly in QCD was proposed [66], which we here label “Monash + New CR” (cf. Table 1). It uses an approximation to the full grouptheoretical weights from SU(3) to compute the probabilities for alternative string topologies at the subleading\(N_C\) level, and is again combined with a dynamical selection favouring topologies with low string lengths. A novel feature of this model is that it allows for “string junctions” (colourepsilon and antiepsilon structures) to form, which, combined with Pythia ’s model for junction fragmentation [68], furnishes a new source of baryon production. Moreover, since it is the global colour structure of the event which gives rise to these additional baryons, their correlations (and net baryonantibaryon compensation) can happen over longer distances than is the case for the conventional (local) baryonproduction mechanisms in the string model.
In both cases, the fragmentation parameters of the string model are constrained by LEP data [60, 66]. In particular, this means that the strangeness fraction is essentially fixed, modulo phasespace constraints and potential triggerbias effects. The same is true for baryon production in the baseline CR model, while, in the New CR model, the baryon fraction can increase with the amount of CR. Exotic heavy baryons can also be formed, containing multiple charm or bottom quarks.
2.2 DIPSY and rope hadronisation
Dipsy [69] implements Mueller’s dipole cascade model [70] which operates in transverse coordinate space (unlike conventional showers which operate in momentum space). Instead of conventional PDFs, the model starts from an explicit representation of each proton being composed of three colour dipoles in impactparameter space and rapidity. These are then evolved in rapidity space via iterated gluon emission, forming a dipole cascade. The resulting partonic final states are hadronised using the Lund string model, via an interface to Pythia 8 [31].
The recently developed rope extension to Dipsy [71] allows geometrically nearby strings to act as a combined “colour rope”, which can hadronise with a higher intrinsic tension. The consequences are larger \(p_\perp \) kicks (relative to the string direction, which for softparticle production in pp collisions largely coincides with the zaxis) and production of more strange hadrons and baryons, the latter via probabilistic collapses of ropes to string junctions. The model also incorporates a mechanism called finalstate “swing”, which acts to minimise the masses of finalstate colourdipoles via colour reconnections, driven by SU(3) colour rules combined with an ad hoc probabilistic evolution kernel.
To provide a reference case without these effects, we also include a “NoSwing” tune of Dipsy with parameters optimised without use of the finalstate rope and swing mechanisms.
2.3 EPOS and hydrodynamic core hadronisation
The epos MC [72] takes a PartonBased Gribov Regge theory approach to event generation [73]. String hadronisation in epos is treated differently based on the local density of string segments per unit volume with respect to a criticaldensity parameter. Each string is classified as being in either a low density coronal region or in a high density core region.
Corona hadronisation proceeds via unmodified string fragmentation whereas the core is subjected to a hydrodynamic evolution; i.e. it is hadronised including additional contributions from longitudinal and radial flow effects [74]. Core conditions are easily satisfied in ion collisions, however even for an average pp collision \((N_\mathrm{ch} = 30, \eta  < 2.4)\) at \(\sqrt{s} = 7 \,\mathrm{{TeV}}\), around 30 % of central particle production arises from the core region. This rises to 75 % for \(N_\mathrm{ch} = 100\).
The epos LHC [75] tune considered here is a dedicated parameter set used to describe all LHC energies and incident particles. A loss of particle multiplicity due to radial flow rescaling is a feature needed to model ion collisions; however, such effects are not observed in pp collisions. To compensate, the LHC tune adds an additional parametrisation which modifies the radial flow of the small, dense cores typical of pp interactions to fix the cluster mass and hence maintain the particle multiplicity. The total momentum is then rescaled after the radial boost to preserve energy conservation.
3 Observables
Track jets are clustered from prompt charged and prompt identifiable strange hadrons. They are reconstructed with the anti\(k_t\) algorithm [77] using radius parameter \(R=0.4\), the leading jet is required to be within \(\eta  < 2.3\).
3.1 Underlyingevent observables
Standard Underlying Event nomenclature is used with respect to the leading track jet in the event as illustrated in Fig. 1. In this note we will consider quantities constructed from particles only in the transverse region as this region is least affected by contributions from the leading \(2\rightarrow 2\) hard scatter.

\(\left\langle {N_\mathrm{{Inc.}}}\right\rangle \): The eventaveraged multiplicity of the inclusive set of particles (prompt charged plus prompt identifiable strange hadrons).

\(\left\langle N(X) \right\rangle \): The eventaveraged multiplicity of identified particle(s), X.

\(\left\langle \sum {p_{\perp }}\right\rangle \): The eventaveraged scalar sum of the transverse momentum of the inclusive set of particles.

\(\left\langle \mathrm{mean}\;{p_{\perp }}\right\rangle \): The eventaveraged mean \({p_{\perp }}\) of the inclusive set of particles.

\(\left\langle \mathrm{RMS} \right\rangle \): The eventaveraged root mean square \({p_{\perp }}\) of the inclusive set of particles, defined here for a given multiplicity, \({N_\mathrm{{Inc.}}}\), as \(\left\langle \mathrm{RMS} \right\rangle = \sqrt{ \left\langle \sum {p_{\perp }}^2 \right\rangle /{N_\mathrm{{Inc.}}}}\).
3.2 Relative transverse activity classifier, \({R_\mathrm{{T}}}\)
Above a lower threshold corresponding to the onset of the UE plateau in the transverse region (roughly, \(p_{\perp \mathrm {lead}} > 10 \,\mathrm{GeV}\)), the mean values of UE quantities calculated over the full inclusive set of particles have little dependence on the leading trackjet \({p_{\perp }}\): the multiple soft scatters which contribute the majority of the UE are largely independent of the leading jet. The slow rise of the UE plateau in the transverse region is understood to be due to additional contributions from wideangle radiation associated with the hard scatter, but this effect becomes significant only for jet \({p_{\perp }}> 50 \,\mathrm{GeV}\) [23]. In this study, we focus on events just above the onset of the plateau, with \(10 \,\mathrm{GeV}< p_{\perp \mathrm {lead}} < 30 \,\mathrm{GeV}\).
The shape of the leading trackjet \({p_{\perp }}\) spectrum in the various MC generators considered is shown in Fig. 2a. Dashed vertical lines indicate the lead \(p_{\perp }\) range used in this study. The Dipsy tunes clearly exhibit a significantly harder jet \({p_{\perp }}\) spectrum than Pythia 8 or epos. However, when we consider the average inclusiveparticle yields in the transverse region, shown in Fig. 2b, we observe that the activity in the transverse region is roughly constant and quite comparable between the models, independently of the jet \({p_{\perp }}\) spectrum. We therefore stress that any mismodelling in the absolute rate of jet events is expected to have little effect on the results in this study due to the profiled mean normalisation. Note that the most extreme variant of Dipsy NoSwing does exhibit a delayed onset of the \(\left<{N_\mathrm{{Inc.}}}\right>\) plateau. In the context of an experimental analysis, the robustness of the conclusions could be explicitly validated by comparing with one or more higher jet \(p_\perp \) windows, say 20–40 and/or 30–50 GeV, statistics allowing.
The average fiducial \(\left<{N_\mathrm{{Inc.}}}\right>\), the denominator of \({R_\mathrm{{T}}}\), for the MC models considered along with the width of the distributions
Generator  Tune  \(\left<{N_\mathrm{{Inc.}}}\right>\)  \(\sigma \) 

Pythia 8  Monash  24.7  12.5 
Pythia 8  Monash + New CR  25.5  12.6 
epos  LHC  24.2  14.6 
Dipsy  NoSwing  21.3  12.2 
Dipsy  Rope  25.1  12.0 
Measuring UE quantities versus \({R_\mathrm{{T}}}\) yields sensitivity to rare events with exceptionally large or small transverse activity with respect to the average event. The lower requirement on the leading trackjet \({p_{\perp }}\) acts to suppress softperiphery and diffractive interactions by ensuring that a hard scatter was present while the upper requirement limits the contamination by wideangle radiation off the hard scatter which increases slowly with the \({p_{\perp }}\) of the leading jet [23].
In the context of MPI models, the case where only a small amount of energy is deposited in the transverse region implies that only a small number of MPI occurred in that event. This therefore affords an opportunity to measure event properties in an ‘MPIsuppressed’ environment, where fragmentation properties may be closer to those of \(e^+e^\) collisions than the average pp jet event.
Illustrated in Fig. 4 are the average number of parton–parton interactions (MPI) and the average transverse protonproton impact parameter \(\left<b_\mathrm {MPI} \right>\), with the latter normalised such that 1 corresponds to the impact parameter of an average minimumbias event. Both are plotted as a function of \(\log _{10}({R_\mathrm{{T}}})\) for four different tunes of Pythia 8. For the most active events (\(\log _{10}({R_\mathrm{{T}}})=0.5\) corresponding to \({R_\mathrm{{T}}}=\sqrt{10}\sim 3.2\) times higherthanaverage UE activity) the average number of MPI increases by roughly a factor 2 relative to the mean (at \(\log _{10}({R_\mathrm{{T}}})=0\)), and the events are roughly twice as central as the average in this jet \(p_\perp \) window (which in turn are twice as central as the average minimumbias event). For lowactivity events, with less than a tenth of the average UE activity, \(\log _{10}({R_\mathrm{{T}}})<1\), an average of less than 2 MPI per event are predicted by these models, with an average impact parameter even larger than for minimumbias events, \(\left<b_\mathrm {MPI} \right> > 1\) for \(\log _{10}({R_\mathrm{{T}}})<0.5\). According to these models, our axis thus allows us to scan over almost an order of magnitude in both the average number of MPI and the average impact parameter, for fixed jet \(p_\perp \).
For completeness, we note that relative UE activity could also have been classified using summed transverseregion \({p_{\perp }}\) or by using the jet median/area techniques referred to in the introduction. Our choice of \({N_\mathrm{{Inc.}}}\) was based on two factors: firstly, we have a very direct relation with chargedparticle multiplicity based minimumbias studies of similar quantities, and secondly the fact that heavier hadrons exhibit harder \({p_{\perp }}\) spectra can lead to undesirable biases in a classifier based on \(\sum {p_{\perp }}\). For example a low \(\sum {p_{\perp }}\) UE would be biased to be made up entirely of pions since these have the softest spectra. Such biases would complicate the interpretation of effects we wish to study such as any evolution in total strangeness and baryon fractions.
4 Inclusiveparticle set results
As a precursor to the main study of identifiedparticle ratios, we first consider the evolution of the inclusiveparticle spectra with \({R_\mathrm{{T}}}\). In Fig. 5, MC predictions for the average \({N_\mathrm{{Inc.}}}\) and \(\sum {p_{\perp }}\) of the inclusive set of particles are plotted as a function of \({R_\mathrm{{T}}}\). The averages and widths of the inclusive \({p_{\perp }}\) spectra are investigated in Fig. 6. The Dipsy Rope tune generates events with the highest overall average energy density followed by epos and Pythia 8. For Dipsy Rope this is observed to be due to a similar average particle multiplicity as for Pythia 8 combined with a 20 % harder mean \({p_{\perp }}\) distribution and a similarly larger RMS. The Dipsy NoSwing tune has both the lowest average multiplicity spectra and the energy density of the models, its mean \({p_{\perp }}\) is similar to that of Pythia 8.
Only minor differences are observed for Pythia 8 with the new colourreconnection model with respect to the standard Monash tune in these distributions.
5 Identifiedparticle results
The production of identified particles is useful to investigate the evolution of the underlying event as a function of transverse activity levels in the event. The strange and baryon contents of the final state arising from nonperturbative effects are expected to be particularly sensitive to the modelling described in Sect. 2.
The average \({K_\mathrm{{s}}^{0}}\) and \(K^\pm \) multiplicities with respect to the inclusive multiplicity are shown in Fig. 7b, c. A baseline increase in the kaon fraction with \({R_\mathrm{{T}}}\) due to phasespace effects is observed for the Pythia 8 Monash and Dipsy NoSwing tunes. epos predicts a significant strange meson enhancement at high \({R_\mathrm{{T}}}\) whereas Dipsy Rope predict an enhancement for all \({R_\mathrm{{T}}}\), in both cases it reaches 5–10 %. Interestingly Pythia 8 Monash + new CR exhibits slightly lower charged and neutralkaon fractions at all \({R_\mathrm{{T}}}\). This is likely to be due to a smaller baseline strangeness fraction in that tune combined with a tendency of the model to create a large number of lowmass string systems in which the production of highmass hadrons (including strange ones) is suppressed due to phasespace restrictions.
The baryonic enhancement is probed in Fig. 8. In Fig. 8a the \(p\bar{p}\) multiplicity is normalised to the inclusive charged multiplicity. Some phasespace effects are apparent in all MCs with the smallest rise in proton enhancement coming from Pythia 8 and Dipsy NoSwing. In epos the magnitude of the enhancement is steepest as a function of \({R_\mathrm{{T}}}\) leading it to be in agreement with Pythia 8 and Dipsy NoSwing only for low values of \({R_\mathrm{{T}}}\). Pythia 8 + new CR and Dipsy Rope both predict significant enhancement, with up to a 50 % increase in proton fraction relative to their simpler model tunes.
By normalising the proton yield to the chargedkaon yield, in Fig. 8b, we obtain a probe that is sensitive to any relative enhancement between baryons and strangeness. Pythia 8 Monash + new CR exhibits the highest ratio, as expected since it incorporates a mechanism for baryon enhancement but no mechanism for strangeness enhancement. Next highest is Dipsy Rope, due to a baryon enhancement significantly larger than its strange meson enhancement. We note that in epos, the two effects are of the same magnitude in both sectors—making it similar to the baseline models, Pythia 8 and Dipsy NoSwing in this observable.
In Fig. 8c strangebaryon enhancement is plotted in the ratio of \(\Lambda \bar{\Lambda }\) hyperons to \({K_\mathrm{{s}}^{0}}\). The profile is similar to that of the proton enhancement except that all of epos, Dipsy Rope and Pythia 8 + new CR are in reasonable agreement and show up to a 90 % enhancement effect with increasing \({R_\mathrm{{T}}}\).
Finally doubly strange \(\Xi ^\pm \) baryons are investigated in Fig. 8d normalised to the \(\Lambda \bar{\Lambda }\) multiplicity spectra. A clear enhancement with increasing \({R_\mathrm{{T}}}\) is observed only for epos; other models do not predict a strong rise; however, Dipsy Rope predicts a slightly larger baseline fraction of doubly strange to singly strange baryons.
6 Conclusions
A new axis for underlyingevent studies \({R_\mathrm{{T}}}= {N_\mathrm{{Inc.}}}/\left<{N_\mathrm{{Inc.}}}\right>\) is proposed to be used as an alternate to leadingobject \({p_{\perp }}\) for events which contain at least one hard scatter and are hence on the ‘underlyingevent plateau’. \({R_\mathrm{{T}}}\) isolates events with exceptionally large or small activity in the transverse underlyingevent region with respect to the eventaveraged mean. Distributions which are sensitive to hadronisation effects are studied as a function of this eventactivity classifier.
Identifiedparticle ratios (strangeness and in particular baryon fractions) are observed to be highly sensitive to the event activity density with a marked increase in average \(p\bar{p}/{N_\mathrm{{Inc.}}}\) and \(\Lambda \bar{\Lambda }/{K_\mathrm{{s}}^{0}}\) multiplicity ratios in dense events for Pythia 8 with the new colourreconnection model, Dipsy Rope and epos when compared to models with a less detailed treatment, Pythia 8 and Dipsy NoSwing.
Distributions are also presented for K, \(\phi \) and \(\Xi ^\pm \) resonances.
These distributions are experimentally accessible at the LHC and would allow for discrimination between alternate colourreconnection, colour “rope” and hydrodynamic modelling of dense protonproton interactions.
Footnotes
 1.
For a definition of the term minimumbias, see e.g. [2, Chp. 7].
 2.
Since the colliding hadrons are colour neutral, the colour charge of each hadron remnant must be equal and opposite (in phase) to the sum of those of the MPI initiators (including the hardestinteraction initiators) taken from that hadron.
 3.
Exceptions are elastic and lowmass diffractive scatterings, which are modelled without a perturbative era.
 4.
So for example \(\Lambda \) originating from \(\Xi ^\) decay are excluded.
Notes
Acknowledgments
We gratefully acknowledge support from the MonashWarwick Alliance Development Fund, without which this study would not have been possible. PS is the recipient of an Australian Research Council Future Fellowship, FT130100744. This work was also supported in part by the ARC Centre of Excellence for Particle Physics at the Terascale and we acknowledge the support of the UK Science and Technology Facilities Council. We thank C. Bierlich and J. Christiansen for supplying the tunes of Dipsy and Pythia 8 used in this paper.
References
 1.UA1 Collaboration, G. Arnison et al., Hadronic jet production at the CERN proton  antiproton collider. Phys. Lett. B 132, 214 (1983)ADSCrossRefGoogle Scholar
 2.A. Buckley et al., Generalpurpose event generators for LHC physics. Phys. Rep. 504, 145–233 (2011). arXiv:1101.2599 [hepph]ADSCrossRefGoogle Scholar
 3.UA1 Collaboration, G. Arnison et al., Transverse energy distributions in the central calorimeters. in Proceedings, 21st International Conference on High Energy Physics (ICHEP 1982), 1982. http://alice.cern.ch/format/showfull?sysnb=0050334
 4.UA1 Collaboration, G. Arnison et al., Some observations on the first events seen at the CERN protonantiproton collider. Phys. Lett. B 107, 320–324 (1981). [Erratum: Phys. Lett. 109B, 510(1982)]Google Scholar
 5.UA1 Collaboration, G. Arnison et al., Charged particle multiplicity distributions in proton antiproton collisions at 540 GeV centerofmass energy. Phys. Lett. B 123, 108 (1983)ADSCrossRefGoogle Scholar
 6.UA1 Collaboration, C. Albajar et al., Production of low transverse energy clusters in antip p collisions at \(\sqrt{s} = \) 0.2 TeV to 0.9 TeV and their interpretation in terms of QCD jets. Nucl. Phys. B 309, 405 (1988)ADSCrossRefGoogle Scholar
 7.STAR Collaboration, H. Caines, Exploring jet properties in pp collisions at 200 GeV with STAR. Nucl. Phys. A 830, 263C–266C (2009). arXiv:0907.3460 [nuclex]
 8.STAR Collaboration, H. Caines, Jet and underlying event measurements in p + p collisions at RHIC. Nucl. Phys. A 855, 376–379 (2011)Google Scholar
 9.CDF Collaboration, T. Affolder et al., Charged jet evolution and the underlying event in \(p\bar{p}\) collisions at 1.8 TeV. Phys. Rev. D 65, 092002 (2002)Google Scholar
 10.CDF Collaboration, D. Acosta et al., The underlying event in hard interactions at the Tevatron \(\bar{p}p\) collider. Phys. Rev. D 70, 072002 (2004). arXiv:hepex/0404004 [hepex]
 11.CDF Collaboration, T. Aaltonen et al., Studying the underlying event in drellyan and high transverse momentum jet production at the tevatron. Phys. Rev. D 82, 034001 (2010). arXiv:1003.3146 [hepex]
 12.CDF Collaboration, T. Aaltonen et al., A study of the energy dependence of the underlying event in protonantiproton collisions. arXiv:1508.05340 [hepex]
 13.CMS Collaboration, V. Khachatryan et al., First measurement of the underlying event activity at the LHC with \(\sqrt{s} = 0.9\) TeV. Eur. Phys. J. C 70, 555–572 (2010). arXiv:1006.2083 [hepex]
 14.ATLAS Collaboration, G. Aad et al., Measurement of underlying event characteristics using charged particles in pp collisions at \(\sqrt{s} = 900\) GeV and 7 TeV with the ATLAS detector. Phys. Rev. D 83, 112001 (2011). arXiv:1012.0791 [hepex]
 15.ATLAS Collaboration, G. Aad et al., Measurements of underlyingevent properties using neutral and charged particles in \(pp\) collisions at 900 GeV and 7 TeV with the ATLAS detector at the LHC,” Eur. Phys. J. C71 (2011) 1636, arXiv:1103.1816 [hepex]
 16.CMS Collaboration, S. Chatrchyan et al., Measurement of the underlying event activity at the LHC with \(\sqrt{s}= 7\) TeV and comparison with \(\sqrt{s} = 0.9\) TeV. JHEP 09, 109 (2011). arXiv:1107.0330 [hepex]
 17.ALICE Collaboration, B. Abelev et al., Underlying event measurements in \(pp\) collisions at \(\sqrt{s}=0.9\) and 7 TeV with the ALICE experiment at the LHC. JHEP 07, 116 (2012). arXiv:1112.2082 [hepex]
 18.ATLAS Collaboration, G. Aad et al., Measurements of the pseudorapidity dependence of the total transverse energy in protonproton collisions at \(\sqrt{s}=7\) TeV with ATLAS. JHEP 11, 033 (2012). arXiv:1208.6256 [hepex]
 19.CMS Collaboration, S. Chatrchyan et al., Measurement of the underlying event activity in \(pp\) collisions at \(\sqrt{s} = 0.9\) and 7 TeV with the novel jetarea/median approach. JHEP 08, 130 (2012). arXiv:1207.2392 [hepex]
 20.CMS Collaboration, S. Chatrchyan et al., Measurement of the underlying event in the DrellYan process in protonproton collisions at \(\sqrt{s}=7\) TeV. Eur. Phys. J. C 72, 2080 (2012). arXiv:1204.1411 [hepex]
 21.CMS Collaboration, S. Chatrchyan et al., Study of the underlying event at forward rapidity in pp collisions at \(\sqrt{s}\) = 0.9, 2.76, and 7 TeV. JHEP 04, 072 (2013). arXiv:1302.2394 [hepex]
 22.CMS Collaboration, S. Chatrchyan et al., Jet and underlying event properties as a function of chargedparticle multiplicity in protonproton collisions at \(\sqrt{s}\) = 7 TeV. Eur. Phys. J. C 73(12), 2674 (2013). arXiv:1310.4554 [hepex]
 23.ATLAS Collaboration, G. Aad et al., Measurement of the underlying event in jet events from 7 TeV protonproton collisions with the ATLAS detector. Eur. Phys. J. C 74(8), 2965 (2014). arXiv:1406.0392 [hepex]
 24.ATLAS Collaboration, G. Aad et al., Measurement of distributions sensitive to the underlying event in inclusive Zboson production in \(pp\) collisions at \(\sqrt{s}=7\) TeV with the ATLAS detector. Eur. Phys. J. C 74(12), 3195 (2014). arXiv:1409.3433 [hepex]
 25.CMS Collaboration, V. Khachatryan et al., Measurement of the underlying event activity using chargedparticle jets in protonproton collisions at \(\sqrt{s} = \) 2.76 TeV. JHEP 09, 137 (2015). arXiv:1507.07229 [hepex]
 26.ATLAS Collaboration, G. Aad et al., Leading track underlying event at 13 TeV. https://cds.cern.ch/record/2037684
 27.M. Cacciari, G.P. Salam, S. Sapeta, On the characterisation of the underlying event. JHEP 04, 065 (2010). arXiv:0912.4926 [hepph]ADSCrossRefGoogle Scholar
 28.M. Heinrich, A jet based approach to measuring soft contributions to protonproton collisions with the CMS experiment. PhD thesis, KIT, Karlsruhe, EKP, 2011. https://inspirehep.net/record/1087971/files/CERNTHESIS2011190.pdf
 29.C. Bierlich, J.R. Christiansen, Effects of color reconnection on hadron flavor observables. Phys. Rev. D 92(9), 094010 (2015). arXiv:1507.02091 [hepph]ADSCrossRefGoogle Scholar
 30.M. Bähr et al., Herwig++ physics and manual. Eur. Phys. J. C 58, 639–707 (2008). arXiv:0803.0883 [hepph]ADSCrossRefGoogle Scholar
 31.T. Sjöstrand, S. Ask, J.R. Christiansen, R. Corke, N. Desai, P. Ilten, S. Mrenna, S. Prestel, C.O. Rasmussen, P.Z. Skands, An introduction to PYTHIA 8.2. Comput. Phys. Commun. 191, 159–177 (2015). arXiv:1410.3012 [hepph]ADSCrossRefGoogle Scholar
 32.T. Gleisberg, S. Hoeche, F. Krauss, M. Schonherr, S. Schumann, F. Siegert, J. Winter, Event generation with SHERPA 1.1. JHEP 02, 007 (2009). arXiv:0811.4622 [hepph]
 33.A. Karneyeu, L. Mijovic, S. Prestel, P.Z. Skands, MCPLOTS: a particle physics resource based on volunteer computing. Eur. Phys. J. C 74, 2714 (2014). arXiv:1306.3436 [hepph]ADSCrossRefGoogle Scholar
 34.CDF Collaboration, R. Field, R.C. Group, PYTHIA tune A, HERWIG, and JIMMY in run 2 at CDF. arXiv:hepph/0510198 [hepph]
 35.T. Sjöstrand, M. van Zijl, A multiple interaction model for the event structure in hadron collisions. Phys. Rev. D 36, 2019 (1987)ADSCrossRefGoogle Scholar
 36.T. Sjöstrand, S. Mrenna, P.Z. Skands, PYTHIA 6.4 physics and manual. JHEP 05, 026 (2006). arXiv:hepph/0603175 [hepph]
 37.L.D. McLerran, M. Kataja, P.V. Ruuskanen, H. von Gersdorff, Studies of the hydrodynamical evolution of matter produced in fluctuations in p antip collisions and in ultrarelativistic nuclear collisions. 2. Transverse momentum distributions. Phys. Rev. D 34, 2755 (1986)ADSCrossRefGoogle Scholar
 38.UA1 Collaboration, C. Albajar et al., A study of the general characteristics of \(p\bar{p}\) collisions at \(\sqrt{s}\) = 0.2 TeV to 0.9 TeV. Nucl. Phys. B 335, 261–287 (1990)ADSCrossRefGoogle Scholar
 39.CDF Collaboration, D. Acosta et al., Soft and hard interactions in \(p\bar{p}\) collisions at \(\sqrt{s}=\) 1800 GeV and 630 GeV. Phys. Rev. D 65, 072005 (2002)Google Scholar
 40.CDF Collaboration, T. Aaltonen et al., Measurement of particle production and inclusive differential cross sections in \(p \bar{p}\) collisions at \(\sqrt{s} = 1.96\)TeV. Phys. Rev. D 79, 112005 (2009). arXiv:0904.1098 [hepex]. [Erratum: Phys. Rev. D 82, 119903 (2010)]
 41.ATLAS Collaboration, G. Aad et al., Chargedparticle multiplicities in pp interactions measured with the ATLAS detector at the LHC. New J. Phys. 13, 053033 (2011). arXiv:1012.5104 [hepex]
 42.CMS Collaboration, V. Khachatryan et al., Charged particle multiplicities in \(pp\) interactions at \(\sqrt{s}=0.9\), 2.36, and 7 TeV. JHEP 01, 079 (2011). arXiv:1011.5531 [hepex]
 43.STAR Collaboration, B.I. Abelev et al., Strange baryon resonance production in \(\sqrt{s}\)(NN) = 200 GeV p+p and Au+Au collisions. Phys. Rev. Lett. 97, 132301 (2006). arXiv:nuclex/0604019 [nuclex]
 44.CDF Collaboration, T. Aaltonen et al., Production of \(\Lambda \), \(\bar{\Lambda }^0\) \(\Xi ^{\pm }\) and \(\Omega ^{\pm }\) hyperons in \(p \bar{p}\) collisions at \(\sqrt{s} = 1.96\) TeV. Phys. Rev. D 86, 012002 (2012). arXiv:1101.2996 [hepex]
 45.ATLAS Collaboration, G. Aad et al., Kshort and \(\Lambda \) production in \(pp\) interactions at \(\sqrt{s}=0.9\) and 7 TeV measured with the ATLAS detector at the LHC,” Phys. Rev. D 85 (2012) 012001, arXiv:1111.1297 [hepex]
 46.CMS Collaboration, V. Khachatryan et al., “Strange Particle Production in \(pp\) Collisions at \(\sqrt{s}=0.9\) and 7 TeV. JHEP 05, 064 (2011). arXiv:1102.4282 [hepex]
 47.LHCb Collaboration, R. Aaij et al., “Measurement of prompt hadron production ratios in \(pp\) collisions at \(\sqrt{s} = \) 0.9 and 7 TeV. Eur. Phys. J. C 72, 2168 (2012). arXiv:1206.5160 [hepex]
 48.ALICE Collaboration, B. Abelev et al., Multistrange baryon production in \(pp\) collisions at \(\sqrt{s} = 7\) TeV with ALICE. Phys. Lett. B 712, 309–318 (2012). arXiv:1204.0282 [nuclex]
 49.CMS Collaboration, S. Chatrchyan et al., Measurement of neutral strange particle production in the underlying event in protonproton collisions at \(\sqrt{s} = \)7 TeV. Phys. Rev. D 88, 052001 (2013). arXiv:1305.6016 [hepex]
 50.ALICE Collaboration, B. B. Abelev et al., Production of \(\Sigma (1385)^{\pm }\) and \(\Xi (1530)^{0}\) in protonproton collisions at \(\sqrt{s}=\) 7 TeV. Eur. Phys. J. C 75(1), 1 (2015). arXiv:1406.3206 [nuclex]
 51.STAR Collaboration, B.I. Abelev et al., Strange particle production in p+p collisions at \(\sqrt{s} =\) 200 GeV. Phys. Rev. C 75, 064901 (2007). arXiv:nuclex/0607033 [nuclex]
 52.ALICE Collaboration, K. Aamodt et al., Production of pions, kaons and protons in \(pp\) collisions at \(\sqrt{s}= 900\) GeV with ALICE at the LHC. Eur. Phys. J. C 71, 1655 (2011). arXiv:1101.4110 [hepex]
 53.ALICE Collaboration, B. Abelev et al., Production of \(K^*(892)^0\) and \(\phi (1020)\) in \(pp\) collisions at \(\sqrt{s}=7\) TeV. Eur. Phys. J. C 72, 2183 (2012). arXiv:1208.5717 [hepex]
 54.ALICE Collaboration, J. Adam et al., Measurement of pion, kaon and proton production in protonproton collisions at \(\sqrt{s} = 7\) TeV. Eur. Phys. J. C 75(5), 226 (2015). arXiv:1504.00024 [nuclex]
 55.CMS Collaboration, V. Khachatryan et al., Observation of longrange nearside angular correlations in protonproton collisions at the LHC. JHEP 09, 091 (2010). arXiv:1009.4122 [hepex]
 56.ATLAS Collaboration, G. Aad et al., Observation of longrange elliptic anisotropies in \(\sqrt{s}=\)13 and 2.76 TeV \(pp\) collisions with the ATLAS detector. arXiv:1509.04776 [hepex]
 57.CDF Collaboration, D. Acosta et al., \(K^0_S\) and \(\Lambda ^0\) production studies in \(p\bar{p}\) collisions at \(\sqrt{s}=\) 1800 GeV and 630 GeV. Phys. Rev. D 72, 052001 (2005). arXiv:hepex/0504048 [hepex]
 58.ALICE Collaboration, B. Abelev et al., Multiplicity dependence of twoparticle azimuthal correlations in pp collisions at the LHC. JHEP 09, 049 (2013). arXiv:1307.1249 [nuclex]
 59.A. Ortiz, G. Bencédi, H. Bello, S. Jena, Jet effects in highmultiplicity pp events. in 7th International Workshop on Multiple Partonic Interactions at the LHC (MPI@LHC 2015) Miramare, Trieste, Italy, November 23–27, 2015. 2016. arXiv:1603.05213 [hepph]
 60.P. Skands, S. Carrazza, J. Rojo, Tuning PYTHIA 8.1: the Monash 2013 Tune. Eur. Phys. J. C 74(8), 3024 (2014). arXiv:1404.5630 [hepph]ADSCrossRefGoogle Scholar
 61.A. Buckley, J. Butterworth, L. Lonnblad, D. Grellscheid, H. Hoeth, J. Monk, H. Schulz, F. Siegert, Rivet user manual. Comput. Phys. Commun. 184, 2803–2819 (2013). arXiv:1003.0694 [hepph]ADSCrossRefGoogle Scholar
 62.M. Cacciari, G.P. Salam, G. Soyez, FastJet user manual. Eur. Phys. J. C 72, 1896 (2012). arXiv:1111.6097 [hepph]ADSCrossRefGoogle Scholar
 63.T. Sjöstrand, P.Z. Skands, Transversemomentumordered showers and interleaved multiple interactions. Eur. Phys. J. C 39, 129–154 (2005). arXiv:hepph/0408302 [hepph]
 64.T. Sjöstrand, P.Z. Skands, Multiple interactions and the structure of beam remnants. JHEP 03, 053 (2004). arXiv:hepph/0402078 [hepph]
 65.S. Argyropoulos, T. Sjöstrand, Effects of color reconnection on \(t\bar{t}\) final states at the LHC. JHEP 11, 043 (2014). arXiv:1407.6653 [hepph]ADSCrossRefGoogle Scholar
 66.J.R. Christiansen, P.Z. Skands, String formation beyond leading colour. JHEP 08, 003 (2015). arXiv:1505.01681 [hepph]Google Scholar
 67.R. Corke, T. Sjöstrand, Interleaved parton showers and tuning prospects. JHEP 03, 032 (2011). arXiv:1011.1759 [hepph]ADSCrossRefGoogle Scholar
 68.T. Sjöstrand, P.Z. Skands, Baryon number violation and string topologies. Nucl. Phys. B 659, 243 (2003). arXiv:hepph/0212264 [hepph]
 69.C. Flensburg, G. Gustafson, L. Lönnblad, Inclusive and exclusive observables from dipoles in high energy collisions. JHEP 08, 103 (2011). arXiv:1103.4321 [hepph]ADSCrossRefGoogle Scholar
 70.A.H. Mueller, Soft gluons in the infinite momentum wave function and the BFKL pomeron. Nucl. Phys. B 415, 373–385 (1994)ADSCrossRefGoogle Scholar
 71.C. Bierlich, G. Gustafson, L. Lönnblad, A. Tarasov, Effects of overlapping strings in pp collisions. JHEP 03, 148 (2015). arXiv:1412.6259 [hepph]CrossRefGoogle Scholar
 72.K. Werner, F.M. Liu, T. Pierog, Parton ladder splitting and the rapidity dependence of transverse momentum spectra in deuterongold collisions at RHIC. Phys. Rev. C 74, 044902 (2006). arXiv:hepph/0506232 [hepph]
 73.H.J. Drescher, M. Hladik, S. Ostapchenko, T. Pierog, K. Werner, Parton based GribovRegge theory. Phys. Rep. 350, 93–289 (2001). arXiv:hepph/0007198 [hepph]
 74.K. Werner, Corecorona separation in ultrarelativistic heavy ion collisions. Phys. Rev. Lett. 98, 152301 (2007). arXiv:0704.1270 [nuclth]
 75.T. Pierog, I. Karpenko, J.M. Katzy, E. Yatsenko, K. Werner, EPOS LHC: test of collective hadronization with data measured at the CERN large hadron collider. Phys. Rev. C 92(3), 034906 (2015). arXiv:1306.0121 [hepph]ADSCrossRefGoogle Scholar
 76.ATLAS Collaboration, G. Aad et al., Chargedparticle distributions in \(\sqrt{s}=13\) TeV \(pp\) interactions measured with the ATLAS detector at the LHC. arXiv:1602.01633 [hepex]
 77.M. Cacciari, G.P. Salam, G. Soyez, The antik(t) jet clustering algorithm. JHEP 04, 063 (2008). arXiv:0802.1189 [hepph]ADSCrossRefGoogle Scholar
 78.Z. Koba, H.B. Nielsen, P. Olesen, Scaling of multiplicity distributions in highenergy hadron collisions. Nucl. Phys. B 40, 317–334 (1972)ADSCrossRefGoogle Scholar
 79.CMS Collaboration, S. Chatrchyan et al., Observation of longrange nearside angular correlations in protonlead collisions at the LHC. Phys. Lett. B 718, 795–814 (2013). arXiv:1210.5482 [nuclex]
Copyright information
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Funded by SCOAP^{3}