arXiv subjects

I. Komarov

Publications and source records attributed to I. Komarov.

7 recordsLinked to original sources

arXiv2022

Search for $Z^{'} \rightarrow μ^{+} μ^{-}$ in the $L_μ{-} L_τ$ gauge-symmetric model at Belle

We search for a new gauge boson $Z'$ that couples only to heavy leptons and their corresponding neutrinos in the process $e^{+} e^{-} \rightarrow Z'(\rightarrow μ^{+}μ^{-}) μ^{+}μ^{-}$, using a 643 fb$^{-1}$ data sample collected by the Belle experiment at or near the $Υ(1S,2S,3S,4S,5S)$ resonances at the KEKB collider. For the first time, effects due to initial state radiation are used in estimating the detection efficiency. No signal is observed in the mass range of 0.212 \ -- 10 GeV/${\it c}^2$ and we set an upper limit on the coupling strengh, $g'$, constraining $Z'$ as a possible contributor to the anomalous magnetic dipole moment of muon.

hep-ex↗

arXiv2022

$B$-flavor tagging at Belle II

We report on new flavor tagging algorithms developed to determine the quark-flavor content of bottom ($B$) mesons at Belle II. The algorithms provide essential inputs for measurements of quark-flavor mixing and charge-parity violation. We validate and evaluate the performance of the algorithms using hadronic $B$ decays with flavor-specific final states reconstructed in a data set corresponding to an integrated luminosity of $62.8$ fb$^{-1}$, collected at the $Υ$(4$S$) resonance with the Belle II detector at the SuperKEKB collider. We measure the total effective tagging efficiency to be $\varepsilon_{\rm eff} = \big(30.0 \pm 1.2(\text{stat}) \pm 0.4(\text{syst})\big)\%$ for a category-based algorithm and $\varepsilon_{\rm eff} = \big(28.8 \pm 1.2(\text{stat}) \pm 0.4(\text{syst})\big)\%$ for a deep-learning-based algorithm.

hep-ex↗

arXiv2021

Punzi-loss: A non-differentiable metric approximation for sensitivity optimisation in the search for new particles

We present the novel implementation of a non-differentiable metric approximation and a corresponding loss-scheduling aimed at the search for new particles of unknown mass in high energy physics experiments. We call the loss-scheduling, based on the minimisation of a figure-of-merit related function typical of particle physics, a Punzi-loss function, and the neural network that utilises this loss function a Punzi-net. We show that the Punzi-net outperforms standard multivariate analysis techniques and generalises well to mass hypotheses for which it was not trained. This is achieved by training a single classifier that provides a coherent and optimal classification of all signal hypotheses over the whole search space. Our result constitutes a complementary approach to fully differentiable analyses in particle physics. We implemented this work using PyTorch and provide users full access to a public repository containing all the codes and a training example.

hep-ex↗

arXiv2020

Test of lepton-flavor universality in ${B\to K^\ast\ell^+\ell^-}$ decays at Belle

We present a measurement of $R_{K^{\ast}}$, the branching fraction ratio ${{\cal B}(B\to K^\ast μ^+ μ^-)}$/ ${{\cal B}(B\to K^\ast e^+ e^-)}$, for both charged and neutral $B$ mesons. The ratio for the charged case, $R_{K{^{\ast +}}}$, is the first measurement ever performed. In addition, we report absolute branching fractions for the individual modes in bins of the squared dilepton invariant mass, $q^2$. The analysis is based on a data sample of $711~\mathrm{fb}^{-1}$, containing $772\times 10^{6}$ $B\bar B$ events, recorded at the $Υ(4S)$ resonance with the Belle detector at the KEKB asymmetric-energy $e^+e^-$ collider. The obtained results are consistent with Standard Model expectations.

hep-ex↗

arXiv2020

Monitoring radiation damage in the LHCb Tracker Turicensis

This paper presents the techniques used to monitor radiation damage in the LHCb Tracker Turicensis during the LHC Runs 1 and 2. Bulk leakage currents in the silicon sensors were monitored continuously, while the full depletion voltages of the sensors were estimated at regular intervals by performing dedicated scans of the charge collection efficiency as a function of the applied bias voltage. Predictions of the expected leakage currents and full depletion voltages are extracted from the simulated radiation profile, the luminosity delivered by the LHC, and the thermal history of the silicon sensors. Good agreement between measurements and predictions is found.

physics.ins-det↗

arXiv2019

Global Decay Chain Vertex Fitting at B-Factories

We present a particle vertex fitting method designed for B factories. The presented method uses a Kalman Filter to solve a least squares estimate to globally fit decay chains, as opposed to traditional methods that fit each vertex at a time. It allows for the extraction of particle momenta, energies, vertex positions and flight lengths, as well as the uncertainty estimates of these quantities. Furthermore, it allows for the precise extraction of vertex parameters in complex decay chains containing neutral final state particles, such as $γ$ or $K^0_L$ , which cannot properly be tracked due to limited spatial resolution of longitudinally segmented single-layer crystal calorimeters like the Belle II ECL. The presented technique can be used to suppress combinatorial background and improve resolutions on measured parameters. We present studies using Monte Carlo simulations of collisions in the Belle II experiment, where modes with neutrals are crucial to the physics analysis program.

hep-ex↗

arXiv2016

Tesla : an application for real-time data analysis in High Energy Physics

Upgrades to the LHCb computing infrastructure in the first long shutdown of the LHC have allowed for high quality decay information to be calculated by the software trigger making a separate offline event reconstruction unnecessary. Furthermore, the storage space of the triggered candidate is an order of magnitude smaller than the entire raw event that would otherwise need to be persisted. Tesla, following the LHCb renowned physicist naming convention, is an application designed to process the information calculated by the trigger, with the resulting output used to directly perform physics measurements.

physics.ins-det↗