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Register a new userA Module For Boosted Dark Matter Event Generation in GENIE
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Joshua Berger
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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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We propose the first experimental test of the inelastic boosted dark matter hypothesis, capitalizing on the new physics potential with the imminent data taking of the ProtoDUNE detectors. More specifically, we explore various experimental signatures at the cosmic frontier, arising in boosted dark matter scenarios, i.e., relativistic, inelastic scattering of boosted dark matter often created by the annihilation of its heavier component which usually comprises of the dominant relic abundance. Although features are unique enough to isolate signal events from potential backgrounds, vetoing a vast amount of cosmic background is rather challenging as the detectors are located on the ground. We argue, with a careful estimate, that such backgrounds nevertheless can be well under control by performing dedicated analyses after data acquisition. We then discuss some phenomenological studies which can be achieved with ProtoDUNE, employing a dark photon scenario as our benchmark dark-sector model.
In models of multi-component dark matter, the lighter component of dark matter can be boosted by annihilations of the heavier state if mass splitting is large enough. Such relativistic dark matter can be detectable via large neutrino detectors such as Super-Kamiokande and IceCube. Moreover, if the process is inelastic scattering and decay length of the produced particle is short enough, another signature coming from the decay can also be detectable. In this paper, we construct a simple two-component dark matter model with a hidden U(1)_D gauge symmetry where the lighter component of dark matter has a potential to improve the so-called small scale structure problems with large self-interacting cross section. We estimate number of multi-Cherenkov ring events due to both of the boosted dark matter and subsequent decay of the particle produced by inelastic scattering at Hyper-Kamiokande future experiment. Some relevant constraints, such as dark matter direct detection and cosmological observations, are also taken into account. The numerical analysis shows that some parameter space which can induce large self-interacting cross section can give a few multi-Cherenkov ring events per year at Hyper-Kamiokande.
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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.
Precision neutrino oscillation experiments of the future---of which DUNE is a prime example---require reliable event generator tools. The 1--4 GeV energy regime, in which DUNE will operate, is marked by the transition from the low-energy nuclear physics domain to that of perturbative QCD, resulting in rich and highly complex physics. Given this complexity, it is important to establish a validation procedure capable of disentangling the physical processes and testing each of them individually. Here, we demonstrate the utility of this approach by benchmarking the GENIE generator, currently used by all Fermilab-based experiments, against a broad set of inclusive electron-scattering data. This comparison takes advantage of the fact that, while electron-nucleus and neutrino-nucleus processes share a lot of common physics, electron scattering gives one access to precisely known beam energies and scattering kinematics. Exploring the kinematic parameter range relevant to DUNE in this manner, we observe patterns of large discrepancies between the generator and data. These discrepancies are most prominent in the pion-producing regimes and are present not only in medium-sized nuclei, including argon, but also in deuterium and hydrogen targets, indicating mismodeled hadronic physics. Several directions for possible improvement are discussed.
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