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Register a new userExtraction of the multiplicity dependence of Multiparton Interactions from LHC pp data using Machine Learning techniques
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Over the last years, Machine Learning (ML) methods have been successfully applied to a wealth of problems in high-energy physics. For instance, in a previous work we have reported that using ML techniques one can extract the Multiparton Interactions (MPI) activity from minimum-bias pp data. Using the available LHC data on transverse momentum spectra as a function of multiplicity, we reported the average number of MPI ($langle N_{rm mpi} rangle$) for minimum-bias pp collisions at $sqrt{s}=5.02$ and 13,TeV. In this work, we apply the same analysis to a new set of data. We report that $langle N_{rm mpi} rangle$ amounts to $3.98 pm 1.01$ for minimum-bias pp collisions at $sqrt{s}=7$,TeV. These complementary results suggest a modest center-of-mass energy dependence of $langle N_{rm mpi} rangle$. The study is further extended aimed at extracting the multiplicity dependence of $langle N_{rm mpi} rangle$ for the three center-of-mass energies. We show that our results qualitatively agree with existing ALICE measurements sensitive to MPI. Namely, $langle N_{rm mpi} rangle$ increases approximately linearly with the charged-particle multiplicity. But, it deviates from the linear dependence at large charged-particle multiplicities. The deviation from the linear trend can be explained in terms of a bias towards harder processes given the multiplicity selection at mid-pseudorapidity. The results reported in this paper provide additional evidence of the presence of MPI in pp collisions, and they can be useful for a better understanding of the heavy-ion-like behaviour observed in pp data.
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Multi-Parton Interactions (MPI) in pp collisions have attracted the attention of the heavy-ion community since they can help to elucidate the origin of collective-like effects discovered in small collision systems at the LHC. In this work, we report that in PYTHIA 8.244, the charged-particle production in events with a large number of MPI (${rm N}_{rm mpi}$) normalized to that obtained in minimum-bias pp collisions shows interesting features. After the normalization to the corresponding $langle {rm N}_{rm mpi} rangle$, the ratios as a function of $p_{rm T}$ exhibit a bump at $p_{rm T}approx3$ GeV/$c$; and for higher $p_{rm T}$ ($>8$ GeV/$c$), the ratios are independent of ${rm N}_{rm mpi}$. While the size of the bump increases with increasing ${rm N}_{rm mpi}$, the behavior at high $p_{rm T}$ is expected from the binary scaling (parton-parton interactions), which holds given the absence of any parton-energy loss mechanism in PYTHIA. The bump at intermediate $p_{rm T}$ is reminiscent of the Cronin effect observed for the nuclear modification factor in p--Pb collisions. In order to unveil these effects in data, we propose a strategy to construct an event classifier sensitive to MPI using Machine Learning-based regression. The study is conducted using TMVA, and the regression is performed with Boosted Decision Trees (BDT). Event properties like forward charged-particle multiplicity, transverse spherocity and the average transverse momentum ($langle p_{rm T} rangle$) are used for training. The kinematic cuts are defined in accordance with the ALICE detector capabilities. In addition, we also report that if we apply the trained BDT on existing (${rm INEL}>0$) pp data, i.e. events with at least one primary charged-particle within $|eta|<1$, the average number of MPI in pp collisions at $sqrt{s}=5.02$ and 13 TeV are 3.76$pm1.01$ and 4.65$pm1.01$, respectively.
Using three different Monte Carlo generators of high energy proton-proton collisions (HIJING, NEXUS, and PSM) we study the energy dependence of multiplicity distributions of charged particles including the LHC energy range. Results are used for calculation of the information entropy, Renyis dimensions and other multifractal characteristics of particle production.
We derive expressions for the cross section of the multiparton interactions based on the analysis of the relevant Feynman diagrams. We express the cross sections through the double (triple, ...) generalized parton distributions (GPDs). In the mean field approximation for the double GPDs the answer is expressed through the integral over two gluon form factor which was measured in the exclusive DIS vector meson production.We explain under what conditions the derived expressions correspond to an intuitive picture of hard interactions in the impact parameter representation. The mean field approximation in which correlations of the partons are neglected fail to explain the data, while pQCD induced correlation enhance large $p_perp$ and $ 0.001 < x < 0.1$ typically enhance the cross section by a factor of 1.5 -- 2 explaining the current data. We argue that in the small x kinematics ($10^{-4} le x le 10^{-3}$) where effects of perturbative correlations diminish, the nonperturbative mechanism kicks in and generates positive correlations comparable in magnitude with the perturbative ones. We explain how our technique can be used for calculations of MPI in the proton - nucleus scattering. The interplay of hard interactions and underlying event is discussed, as well as different geometric pictures for each of MPI mechanisms-pQCD, nonperturbative correlations and mean field. Predictions for value of effs for various processes and a wide range of kinematics are given. We show that together different MPI mechanisms give good description of experimental data, both at Tvatron, and LHC, including the central kinematics studied by ATLAS and CMS detectors, and forward (heavy flavors) kinematics studied by LHCb.
The dependence of the inelasticity in terms of the center of mass energy is studied in the eikonal formalism, which provides connection between elastic and inelastic channels. Due to the absence of inelasticity experimental datasets, the present analysis is based on experimental information available on the full phase space multiplicity distribution covering a large range of energy, namely 30 $<$ $sqrt{s}$ $leq$ 1800 GeV. Our results indicate that the decrease of inelasticity is consequence of minijets production from semihard interactions arising from the scattering of gluons carrying only a very small fractions of the momenta from their parent protons. Alternative methods of estimating the inelasticity are discussed and predictions to the LHC energies are presented.
The prospects of observing the non-resonant di-Higgs production in the Standard Model at the proposed high energy upgrade of the LHC, $viz.$ the HE-LHC$~$($sqrt{s}=27~{rm TeV}$ and $mathcal{L} = 15~{rm ab^{-1}}$) is studied. Various di-Higgs final states are considered based on their cleanliness and signal yields. The search for the non-resonant double Higgs production at the HE-LHC is performed in the $bbar{b}gammagamma$, $bbar{b}tau^{+}tau^{-}$, $bbar{b}WW^{*}$, $WW^{*}gammagamma$, $bbar{b}ZZ^{*}$ and $bbar{b}mu^{+}mu^{-}$ channels. The signal-background discrimination is performed through multivariate analyses using the Boosted Decision Tree Decorrelated$~$(BDTD) algorithm in the$~$TMVA framework, the XGBoost toolkit and Deep Neural Network$~$(DNN). The variation in the kinematics of Higgs pair production as a function of the self-coupling of the Higgs boson, $lambda_{h}$, is also studied. The ramifications of varying $lambda_{h}$ on the $bbar{b}gammagamma$, $bbar{b}tau^{+}tau^{-}$ and $bbar{b}WW^{*}$ search analyses optimized for the SM hypothesis is also explored.
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