arXiv subjects

Slavomira Stefkova

Publications and source records attributed to Slavomira Stefkova.

5 recordsLinked to original sources

arXiv2026

Constraints on invisible $B^{+}\to K^{+} X$ decays from the Belle II $B^{+} \to K^{+} ν\barν$ measurement

The Belle II measurement of the branching fraction for $B^+\to K^+ ν\bar ν$ shows a $2.7σ$ excess over the Standard Model prediction and motivates new-physics explanations such as axion-like particles, Higgs-like scalars, or beyond Standard Model gauge bosons. A two-body decay $B^+\to K^+ X$ with an invisible $X$ provides a natural candidate explanation. This work provides a comprehensive test of this hypothesis using Belle II's public model-agnostic likelihood. Posterior distributions are derived for the resonance mass $m_X$ and the branching fraction, and a modified frequentist upper-limit mass scan is performed. The data favor a resonance with mass $m_X = 2.1^{+0.2}_{-0.1}$ GeV and the product $\mathcal{B}(B^+\to K^+ X) \cdot \mathcal{P}_{X,\rm inv} = 9.2^{+1.8}_{-3.4} \cdot 10^{-6}$, where $\mathcal{P}_{X,\rm inv}$ is the probability that $X$ (and its decay products) are undetected. Bayes factors indicate a very strong preference for the Standard Model plus resonance over the Standard Model-only hypothesis. A frequentist likelihood-ratio test favors the Standard Model plus resonance hypothesis by $3.0σ$. A light invisible resonance plus the Standard Model therefore provides a compelling description of the Belle II data.

hep-ph↗

arXiv2025

End-to-End Multi-Track Reconstruction using Graph Neural Networks at Belle II

We present the study of an end-to-end multi-track reconstruction algorithm for the central drift chamber of the Belle II experiment at the SuperKEKB collider using Graph Neural Networks for an unknown number of particles. The algorithm uses detector hits as inputs without pre-filtering to simultaneously predict the number of track candidates in an event and their kinematic properties. In a second step, we cluster detector hits for each track candidate to pass to a track fitting algorithm. Using a realistic full detector simulation including beam-induced backgrounds and detector noise taken from actual collision data, we find significant improvements in track finding efficiencies for tracks in a variety of different event topologies compared to the existing baseline algorithm used in Belle II. For events with a hypothetical long-lived massive particle with a mass in the GeV-range decaying uniformly along its flight direction into two charged particles, the GNN achieves a combined track finding and fitting efficiency of 85.4% with a fake rate of 2.5%, compared to 52.2% and 4.1% for the baseline algorithm. This is the first end-to-end multi-track machine learning algorithm for a drift chamber detector that has been utilized in a realistic particle physics environment.

physics.ins-det↗

arXiv2024

Constructing model-agnostic likelihoods, a method for the reinterpretation of particle physics results

Experimental High Energy Physics has entered an era of precision measurements. However, measurements of many of the accessible processes assume that the final states' underlying kinematic distribution is the same as the Standard Model prediction. This assumption introduces an implicit model-dependency into the measurement, rendering the reinterpretation of the experimental analysis complicated without reanalysing the underlying data. We present a novel reweighting method in order to perform reinterpretation of particle physics measurements. It makes use of reweighting the Standard Model templates according to kinematic signal distributions of alternative theoretical models, prior to performing the statistical analysis. The generality of this method allows us to perform statistical inference in the space of theoretical parameters, assuming different kinematic distributions, according to a beyond Standard Model prediction. We implement our method as an extension to the pyhf software and interface it with the EOS software, which allows us to perform flavor physics phenomenology studies. Furthermore, we argue that, beyond the pyhf or HistFactory likelihood specification, only minimal information is necessary to make a likelihood model-agnostic and hence easily reinterpretable. We showcase that publishing such likelihoods is crucial for a full exploitation of experimental results.

hep-ph↗

arXiv2024

Real-time Graph Building on FPGAs for Machine Learning Trigger Applications in Particle Physics

We present a design methodology that enables the semi-automatic generation of a hardware-accelerated graph building architectures for locally constrained graphs based on formally described detector definitions. In addition, we define a similarity measure in order to compare our locally constrained graph building approaches with commonly used k-nearest neighbour building approaches. To demonstrate the feasibility of our solution for particle physics applications, we implemented a real-time graph building approach in a case study for the Belle~II central drift chamber using Field-Programmable Gate Arrays~(FPGAs). Our presented solution adheres to all throughput and latency constraints currently present in the hardware-based trigger of the Belle~II experiment. We achieve constant time complexity at the expense of linear space complexity and thus prove that our automated methodology generates online graph building designs suitable for a wide range of particle physics applications. By enabling an hardware-accelerated pre-processing of graphs, we enable the deployment of novel Graph Neural Networks~(GNNs) in first level triggers of particle physics experiments.

hep-ex↗

arXiv2021

Measurements of Beam Backgrounds in SuperKEKB Phase 2

The high design luminosity of the SuperKEKB electron-positron collider will result in challenging levels of beam-induced backgro\ unds in the interaction region. Understanding and mitigating these backgrounds is critical to the success of the Belle~II experi\ ment. We report on the first background measurements performed after roll-in of the Belle II detector, a period known as SuperKE\ KB Phase 2, utilizing both the BEAST II system of dedicated background detectors and the Belle II detector itself. We also repor\ t on first revisions to the background simulation made in response to our findings. Backgrounds measured include contributions f\ rom synchrotron radiation, beam-gas, Touschek, and injection backgrounds. At the end of Phase 2, single-beam backgrounds origina\ ting from the 4 GeV positron Low Energy Ring (LER) agree reasonably well with simulation, while backgrounds from the 7 GeV elect\ ron High Energy Ring (HER) are approximately one order of magnitude higher than simulation. We extrapolate these backgrounds for\ ward and conclude it is safe to install the Belle II vertex detector.

physics.ins-det↗