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We report a search for a heavy neutral lepton (HNL) that mixes predominantly with${\nu }_{\tau }$. The search utilizes data collected with the Belle detector at the KEKB asymmetric energy$e^{+}{e}^{-}$collider. The data sample was collected at and just below the center-of-mass energies of the$\mathrm{\Upsilon }(4S)$and$\mathrm{\Upsilon }(5S)$resonances and has an integrated luminosity of$915{\mathrm{fb}}^{-1}$, corresponding to$(836\pm 12)\times {10}^6$$e^{+}{e}^{-}\to {\tau }^{+}{\tau }^{-}$events. We search for production of the HNL (denoted$N$) in the decay${\tau }^{-}\to {\pi }^{-}N$followed by its decay via$N\to {\mu }^{+}{\mu }^{-}{\nu }_{\tau }$. The search focuses on the parameter-space region in which the HNL is long-lived, so that the${\mu }^{+}{\mu }^{-}$originate from a common vertex that is significantly displaced from the collision point of the KEKB beams. Consistent with the expected background yield, one event is observed in the data sample after application of all the event-selection criteria. We report limits on the mixing parameter of the HNL with the$\tau$neutrino as a function of the HNL mass.

We report a search for the charged-lepton flavor violation in Υ(2S) →ℓ^∓τ^±(ℓ=e, μ) decays using a 25 fb⁻¹Υ(2S) sample collected by the Belle detector at the KEKBe⁺e⁻asymmetric-energy collider. We find no evidence for a signal and set upper limits on the branching fractions ($$ \mathcal{B} $$$B$) at 90% confidence level. We obtain the most stringent upper limits:$$ \mathcal{B} $$$B$(Υ(2S)→ μ^∓τ^±)<0.23×10⁻⁶and$$ \mathcal{B} $$$B$(Υ(2S)→ e^∓τ^±)<1.12×10⁻⁶.

Computing demands for large scientific experiments, such as the CMS experiment at the CERN LHC, will increase dramatically in the next decades. To complement the future performance increases of software running on central processing units (CPUs), explorations of coprocessor usage in data processing hold great potential and interest. Coprocessors are a class of computer processors that supplement CPUs, often improving the execution of certain functions due to architectural design choices. We explore the approach of Services for Optimized Network Inference on Coprocessors (SONIC) and study the deployment of this as-a-service approach in large-scale data processing. In the studies, we take a data processing workflow of the CMS experiment and run the main workflow on CPUs, while offloading several machine learning (ML) inference tasks onto either remote or local coprocessors, specifically graphics processing units (GPUs). With experiments performed at Google Cloud, the Purdue Tier-2 computing center, and combinations of the two, we demonstrate the acceleration of these ML algorithms individually on coprocessors and the corresponding throughput improvement for the entire workflow. This approach can be easily generalized to different types of coprocessors and deployed on local CPUs without decreasing the throughput performance. We emphasize that the SONIC approach enables high coprocessor usage and enables the portability to run workflows on different types of coprocessors.