arXiv subjects

Karan Kumar

Publications and source records attributed to Karan Kumar.

4 recordsLinked to original sources

arXiv2023

Forward production of prompt neutrinos in the atmosphere and at high-energy colliders

The atmospheric neutrino flux at very high energies is dominated by prompt neutrinos, mostly contributed by the decays of charmed hadrons produced in the forward direction by cosmic ray interactions with air nuclei. Theoretical predictions of the prompt atmospheric neutrino flux have large uncertainties mainly related to charm hadron production. Prompt neutrinos can also be studied through high-energy colliders. In particular, two ongoing forward experiments and the proposed Forward Physics Facility at the LHC can detect forward prompt neutrinos. We will present the kinematic regions relevant to the prompt atmospheric neutrino flux in terms of collider kinematic variables, the collision energy $\sqrt{s}$ and the center-of-mass rapidity of charm hadrons $y$, and discuss implications of the forward experiments at the LHC on the theoretical predictions of the prompt atmospheric neutrino flux.

hep-ph↗

arXiv2022

Forward production of prompt neutrinos from charm in the atmosphere and at high energy colliders

The high-energy atmospheric neutrino flux is dominated by neutrinos from the decays of charmed hadrons produced in the forward direction by cosmic ray interactions with air nuclei. We evaluate the charm contributions to the prompt atmospheric neutrino flux as a function of the center-of-mass energy $\sqrt{s}$ of the hadronic collision and of the center-of-mass rapidity $y$ of the produced charm hadron. Uncertainties associated with parton distribution functions are also evaluated as a function of $y$. We find that the $y$ coverage of LHCb for forward heavy-flavour production, complemented by the angular coverage of present and future forward neutrino experiments at the LHC, bracket the most interesting $y$ regions for the prompt atmospheric neutrino flux. At $\sqrt{s}=14$ TeV foreseen for the HL-LHC phase, nucleon collisions in air contribute to the prompt neutrino flux prominently below $E_\nu\sim 10^7$~GeV. Measurements of forward charm and/or forward neutrinos produced in hadron collisions up to $\sqrt{s}=100$ TeV, which might become possible at the FCC, are relevant for the prompt atmospheric neutrino flux up to $E_\nu=10^8$ GeV and beyond.

hep-ph↗

arXiv2022

Prompt electron and tau neutrinos and antineutrinos in the forward region at the LHC

Neutrino fluxes at high rapidity and at high energy are sensitive to QCD dynamics of heavy-flavor production in kinematic regions where measurements have not yet been made. The FASER$\nu$ and SND@LHC experiments scheduled for Run 3 at the LHC and the proposed Forward Physics Facility with a suite of experiments during the High-Luminosity LHC phase will probe neutrinos at high pseudorapidity. This short paper reports on recent evaluations of the prompt $\nu_\tau+\bar\nu_\tau$ and $\nu_e+\bar\nu_e$ double-differential cross sections in $pp$ collisions at the Large Hadron Collider from the production and decays of $D_s^\pm$ and $D^\pm$, respectively. For $\sqrt{s}=14$ TeV, the double-differential neutrino energy and pseudorapidity distributions are evaluated at NLO QCD. Data tables with these predictions are presented. Future work needed to refine predictions of neutrino and antineutrino fluxes in the forward region at the LHC is discussed.

hep-ph↗

arXiv2022

Maximum likelihood reconstruction of water Cherenkov events with deep generative neural networks

Large water Cherenkov detectors have shaped our current knowledge of neutrino physics and nucleon decay, and will continue to do so in the foreseeable future. These highly capable detectors allow for directional and topological, as well as calorimetric information to be extracted from signals on their photosensors. The current state-of-the-art approach to water Cherenkov reconstruction relies on maximum-likelihood estimation, with several simplifying assumptions employed to make the problem tractable. In this paper, we describe neural networks that produce probability density functions for the signals at each photosensor, given a set of inputs that characterizes a particle in the detector. The neural networks we propose allow for likelihood-based approaches to event reconstruction with significantly fewer assumptions compared to traditional methods, and are thus expected to improve on the current performance of water Cherenkov detectors.

hep-ex↗