Curvature-bias corrections using a pseudomass method

Momentum measurements for very high momentum charged particles, such as muons from electroweak vector boson decays, are particularly susceptible to charge-dependent curvature biases that arise from misalignments of tracking detectors. Low momentum charged particles used in alignment procedures have limited sensitivity to coherent displacements of such detectors, and therefore are unable to fully constrain these misalignments to the precision necessary for studies of electroweak physics. Additional approaches are therefore required to understand and correct for these effects. In this paper the curvature biases present at the LHCb detector are studied using the pseudomass method in proton-proton collision data recorded at centre of mass energy √(s)=13 TeV during 2016, 2017 and 2018. The biases are determined using Z→μ + μ - decays in intervals defined by the data-taking period, magnet polarity and muon direction. Correcting for these biases, which are typically at the 10-4 GeV-1 level, improves the Z→μ + μ - mass resolution by roughly 18% and eliminates several pathological trends in the kinematic-dependence of the mean dimuon invariant mass

Momentum scale calibration of the LHCb spectrometer

For accurate determination of particle masses accurate knowledge of the momentum scale of the detectors is crucial. The procedure used to calibrate the momentum scale of the LHCb spectrometer is described and illustrated using the performance obtained with an integrated luminosity of 1.6 fb-1 collected during 2016 in pp running. The procedure uses large samples of J/ψ → μ + μ - and B+ → J/ψ K + decays and leads to a relative accuracy of 3 × 10-4 on the momentum scale

Fire Danger and Fire Behavior Modeling Systems in Australia, Europe, and North America

Wildland fire occurrence and behavior are complex phenomena involving essentially fuel (vegetation), topography, and weather. Fire managers around the world use a variety of systems to track and predict fire danger and fire behavior, at spatial scales that span from local to global extents, and temporal scales ranging from minutes to seasons. The fire management application determines the makeup of the planning tool, which usually incorporates one or more computer models. Advanced computing technology has spawned a new generation of fire planning tools to predict fire occurrence and fire behavior. We reviewed fire danger and fire behavior modeling systems from Australia, Europe, and North America, including operational tools that have been in use for decades, and newer models that profoundly enhance the spatial and temporal resolution of the resultant predictions. Linkages between these models and ai