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Bonamia(Haplosporida) are oyster parasites capable of devastating oyster populations. The near-circumglobal distribution of the host generalistB. exitiosahas previously been associated with the natural and anthropogenic dispersal of broadly distributed non-commercial oysters in theOstrea stentinaspecies complex. Here, we took a global snapshot approach to explore the role of the widely introduced Pacific oysterMagallana gigas, a commercially important species that can be found on every continent except Antarctica, in transportingBonamia.We screened 938M. gigasindividuals from 41 populations in this oyster’s native and non-native geographic range for presence ofBonamiaDNA using PCR.B. exitiosawas the only species detected and only within 2 of 5 populations from southern California, USA (10 and 42% PCR prevalence). Therefore,M. gigascould have played a role in transportingB. exitiosato California (if introduced) and/or maintainingB. exitiosapopulations within California, but morphological confirmation of infection needs to be done to better understand the host-parasite dynamics within this system. We detected noBonamiaDNA within any other non-nativeM. gigaspopulations (n = 302) nor within nativeM. gigaspopulations in Japan and Korea (n = 582) and thus found no evidence to support the co-dispersal ofM. gigasand otherBonamiaspecies. Lower sample sizes within some populations and the non-systematic nature of our sampling design may have led to false negatives, especially in areas whereBonamiaare known to occur. Nevertheless, this global snapshot provides preliminary guidance for managing both natural and farmed oyster populations. 

Abstract A tau lepton identification algorithm,DeepTau, based on convolutional neural network techniques, has been developed in the CMS experiment to discriminate reconstructed hadronic decays of tau leptons (τ_h) from quark or gluon jets and electrons and muons that are misreconstructed as τ_hcandidates. The latest version of this algorithm, v2.5, includes domain adaptation by backpropagation, a technique that reduces discrepancies between collision data and simulation in the region with the highest purity of genuine τ_hcandidates. Additionally, a refined training workflow improves classification performance with respect to the previous version of the algorithm, with a reduction of 30–50% in the probability for quark and gluon jets to be misidentified as τ_hcandidates for given reconstruction and identification efficiencies. This paper presents the novel improvements introduced in theDeepTaualgorithm and evaluates its performance in LHC proton-proton collision data at √(s) = 13 and 13.6 TeV collected in 2018 and 2022 with integrated luminosities of 60 and 35 fb^-1, respectively. Techniques to calibrate the performance of the τ_h  identification algorithm in simulation with respect to its measured performance in real data are presented, together with a subset of results among those measured for use in CMS physics analyses. 

Abstract Measurements in the highly Lorentz-boosted regime provoke increased interest in probing the Higgs boson properties and in searching for particles beyond the standard model at the LHC. In the CMS Collaboration, various boosted-object tagging algorithms, designed to identify hadronic jets originating from a massive particle decaying to bb̅ or cc̅, have been developed and deployed across a range of physics analyses. This paper highlights their performance on simulated events, and summarizes novel calibration techniques using proton-proton collision data collected at √(s) = 13 TeV during the 2016–2018 LHC data-taking period. Three dedicated methods are used for the calibration in multijet events, leveraging either machine learning techniques, the presence of muons within energetic boosted jets, or the reconstruction of hadronically decaying high-energy Z bosons. The calibration results, obtained through a combination of these approaches, are presented and discussed.