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The direct measurement of the top quark-Higgs coupling is one of the important questions in understanding the Higgs boson. The coupling can be obtained through measurement of the top quark pair-associated Higgs boson production cross-section. Of the multiple challenges arising in this cross-section measurement, we investigate the reconstruction of the partons originating from the hard scattering process using the measured jets in simulated ttH events. The task corresponds to an assignment challenge of m objects (jets) to n other objects (partons), where m>=n. We compare several methods with emphasis on a concept based on deep learning techniques which yields the best results with more than 50% of correct jet-parton assignments.
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We present a method for studying the detection of jets in high energy hadronic collisions using multiplicity detector in forward rapidities. Such a study enhances the physics scope of multiplicity detectors at forward rapidities in LHC. At LHC energies the jets may be produced with significant cross section in forward rapidities. A multi resolution wavelet analysis technique can locate the spatial position of jets due to its feature of space-scale locality. The discrete wavelet proves to be very effective in probing physics simultaneously at different locations in phase space and at different scales to identify jet-like events. The key feature this analysis exploits is the difference in particle density in localized regions of the detector due to jet-like and underlying events. We find that this method has a significant sensitivity towards detecting jet position and its size. The jets can be found with the efficiency and purity of the order of 46%.
The dijet double-differential cross section is measured as a function of the dijet invariant mass, using data taken during 2010 and during 2011 with the ATLAS experiment at the LHC, with a center-of-mass energy of 7 TeV. The measurements are sensitive to invariant masses between 70 GeV and 4.27 TeV with center-of-mass jet rapidities up to 3.5. A novel technique to correct jets for pile-up (additional proton-proton collisions) in the 2011 data is developed and subsequently used in the measurement. The data are found to be consistent with fixed-order NLO pQCD predictions provided by NLOJET++. The results constitute a stringent test of pQCD, in an energy regime previously unexplored. The dijet analysis is a confidence building step for the extraction of the signal of hard double parton scattering (DPS) in four-jet events, and subsequent extraction of the effective overlap area between the interacting protons, expressed in terms of the variable, sigma(eff). The measurement of DPS is performed using the 2010 ATLAS data. The rate of DPS events is estimated using a neural network. A clear signal is observed, under the assumption that the DPS signal can be represented by a random combination of exclusive dijet production. The fraction of DPS candidate events is determined to be f(DPS) = 0.081 +- 0.004 (stat.) +0.025-0.014 (syst.) in the analyzed phase-space of four-jet topologies. Combined with the measurement of the dijet and four-jet cross sections in the appropriate phase-space regions, the effective cross section is found to be sigma(eff) = 16.0 +0.5-0.8 (stat.) +1.9-3.5 (syst.) mb. This result is consistent within the quoted uncertainties with previous measurements of sigma(eff) at center-of-mass energies between 63 GeV and 7 TeV, using several final states.
The first measurement of lepton-jet momentum imbalance and azimuthal correlation in lepton-proton scattering at high momentum transfer is presented. These data, taken with the H1 detector at HERA, are corrected for detector effects using an unbinned machine learning algorithm OmniFold, which considers eight observables simultaneously in this first application. The unfolded cross sections are compared to calculations performed within the context of collinear or transverse-momentum-dependent (TMD) factorization in Quantum Chromodynamics (QCD) as well as Monte Carlo event generators. The measurement probes a wide range of QCD phenomena, including TMD parton distribution functions and their evolution with energy in so far unexplored kinematic regions.
A Monte-Carlo event-generator has been developed which is dedicated to simulate electron-positron annihilations. Especially a new approach for the combination of matrix elements and parton showers ensures the independence of the hadronization parameters from the CMS energy. This enables for the first time the description of multijet-topologies, e.g. four jet angles, over a wide range of energy, without changing any parameter of the model. Covering all processes of the standard model our simulator is capable to describe experiments at present and future accelerators, i.e. the LEP collider and a possible Next Linear Collider(NLC).
Jet interactions in a hot QCD medium created in heavy-ion collisions are conventionally assessed by measuring the modification of the distributions of jet observables with respect to the proton-proton baseline. However, the steeply falling production spectrum introduces a strong bias toward small energy losses that obfuscates a direct interpretation of the impact of medium effects in the measured jet ensemble. Modern machine learning techniques offer the potential to tackle this issue on a jet-by-jet basis. In this paper, we employ a convolutional neural network (CNN) to diagnose such modifications from jet images where the training and validation is performed using the hybrid strong/weak coupling model. By analyzing measured jets in heavy-ion collisions, we extract the original jet transverse momentum, i.e., the transverse momentum of an identical jet that did not pass through a medium, in terms of an energy loss ratio. Despite many sources of fluctuations, we achieve good performance and put emphasis on the interpretability of our results. We observe that the angular distribution of soft particles in the jet cone and their relative contribution to the total jet energy contain significant discriminating power, which can be exploited to tailor observables that provide a good estimate of the energy loss ratio. With a well-predicted energy loss ratio, we study a set of jet observables to estimate their sensitivity to bias effects and reveal their medium modifications when compared to a more equivalent jet population, i.e., a set of jets with similar initial energy. Finally, we also show the potential of deep learning techniques in the analysis of the geometrical aspects of jet quenching such as the in-medium traversed length or the position of the hard scattering in the transverse plane, opening up new possibilities for tomographic studies.
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