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Register a new userReconstructing boosted Higgs jets from event image segmentation
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Based on the jet image approach, which treats the energy deposition in each calorimeter cell as the pixel intensity, the Convolutional neural network (CNN) method has been found to achieve a sizable improvement in jet tagging compared to the traditional jet substructure analysis. In this work, the Mask R-CNN framework is adopted to reconstruct Higgs jets in collider-like events, with the effects of pileup contamination taken into account. This automatic jet reconstruction method achieves higher efficiency of Higgs jet detection and higher accuracy of Higgs boson four-momentum reconstruction than traditional jet clustering and jet substructure tagging methods. Moreover, the Mask R-CNN trained on events containing a single Higgs jet is capable of detecting one or more Higgs jets in events of several different processes, without apparent degradation in reconstruction efficiency and accuracy. The outputs of the network also serve as new handles for the $tbar{t}$ background suppression, complementing to traditional jet substructure variables.
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Inclusive Higgs boson production at large transverse momentum is induced by different production channels. We focus on the leading production through gluon fusion, and perform a consistent combination of the state of the art calculations obtained in the infinite-top-mass effective theory at next-to-next-to-leading order (NNLO) and in the full Standard Model (SM) at next-to-leading order (NLO). We thus present approximate QCD predictions for this process at NNLO, and a study of the corresponding perturbative uncertainties. This calculation is then compared with those obtained with commonly used event generators, and we observe that the description of the considered kinematic regime provided by these tools is in good agreement with state of the art calculations. Finally, we present accurate predictions for other production channels such as vector boson fusion, and associated production with a gauge boson, and with a $tbar{t}$ pair. We find that, at large transverse momentum, the contribution of other production modes is substantial, and therefore must be included for a precise theory prediction of this observable.
Jet identification tools are crucial for new physics searches at the LHC and at future colliders. We introduce the concept of Mass Unspecific Supervised Tagging (MUST) which relies on considering both jet mass and transverse momentum varying over wide ranges as input variables - together with jet substructure observables - of a multivariate tool. This approach not only provides a single efficient tagger for arbitrary ranges of jet mass and transverse momentum, but also an optimal solution for the mass correlation problem inherent to current taggers. By training neural networks, we build MUST-inspired generic and multi-pronged jet taggers which, when tested with various new physics signals, clearly outperform the variables commonly used by experiments to discriminate signal from background. These taggers are also efficient to spot signals for which they have not been trained. Taggers can also be built to determine, with a high degree of confidence, the prongness of a jet, which would be of utmost importance in case a new physics signal is discovered.
We show that the signature of two boosted $W$-jets plus large missing energy is very promising to probe heavy charged resonances ($X^pm$) through the process of $ppto X^+X^-to W^+W^- X^0 X^0$ where $X^0$ denotes dark matter candidate. The hadronic decay mode of the $W$ boson is considered to maximize the number of signal events. When the mass split between $X^pm$ and $X^0$ is large, one has to utilize the jet-substructure technique to analyze the boosted $W$-jet. For illustration we consider the process of chargino pair production at the LHC, i.e., $ppto chi_1^+chi^-_1 to W^+W^-chi_1^0chi_1^0$, and demonstrate that the proposed signature is able to cover more parameter space of $m_{chi_1^pm}$ and $m_{chi_1^0}$ than the conventional signature of multiple leptons plus missing energy. More importantly, the signature of our interests is not sensitive to the spin of heavy resonances.
By representing each collider event as a point cloud, we adopt the Graphic Convolutional Network (GCN) with focal loss to reconstruct the Higgs jet in it. This method provides higher Higgs tagging efficiency and better reconstruction accuracy than the traditional methods which use jet substructure information. The GCN, which is trained on events of the $H$+jets process, is capable of detecting a Higgs jet in events of several different processes, even though the performance degrades when there are boosted heavy particles other than the Higgs in the event. We also demonstrate the signal and background discrimination capacity of the GCN by applying it to the $tbar{t}$ process. Taking the outputs of the network as new features to complement the traditional jet substructure variables, the $tbar{t}$ events can be separated further from the $H$+jets events.
Models that produce a flux of semi-relativistic or relativistic boosted dark matter at large neutrino detectors are well-motivated extensions beyond the minimal weakly interacting massive particle (WIMP) paradigm. Current and upcoming liquid argon time projection chamber (LArTPC) based detectors will have improved sensitivity to such models, but also require improved theoretical modeling to better understand their signals and optimize their analyses. I present the first full Monte Carlo tool for boosted dark matter interacting with nuclei in the energy regime accessible to LArTPC detectors, including the Deep Underground Neutrino Experiment (DUNE). The code uses the nuclear and strong physics modeling of the GENIE neutrino Monte Carlo event generator with particle physics modeling for dark matter. The code will be available in GENIE v3. In addition, I present a code for generating a GENIE-compatible flux of boosted dark matter coming from the Sun that is released independently.
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