Combination of searches for heavy spin-1 resonances using 139 fb−1 of proton-proton collision data at s = 13 TeV with the ATLAS detector

A combination of searches for new heavy spin-1 resonances decaying into different pairings of W, Z, or Higgs bosons, as well as directly into leptons or quarks, is presented. The data sample used corresponds to 139 fb−1 of proton-proton collisions at = 13 TeV collected during 2015–2018 with the ATLAS detector at the CERN Large Hadron Collider. Analyses selecting quark pairs (qq, bb, , and tb) or third-generation leptons (τν and ττ) are included in this kind of combination for the first time. A simplified model predicting a spin-1 heavy vector-boson triplet is used. Cross-section limits are set at the 95% confidence level and are compared with predictions for the benchmark model. These limits are also expressed in terms of constraints on couplings of the heavy vector-boson triplet to quarks, leptons, and the Higgs boson. The complementarity of the various analyses increases the sensitivity to new physics, and the resulting constraints are stronger than those from any individual analysis considered. The data exclude a heavy vector-boson triplet with mass below 5.8 TeV in a weakly coupled scenario, below 4.4 TeV in a strongly coupled scenario, and up to 1.5 TeV in the case of production via vector-boson fusion

Bay of Bengal cyclone extreme water level estimate uncertainty

Accurate estimates of storm surge magnitude and frequency are essential to coastal flood risk studies. Much research has focused on tide–surge interaction and joint probability techniques to combine multiple cyclone characteristics. In the Bay of Bengal, extreme water levels are derived from numerical storm surge models based on an idealised cyclone event; however, uncertainty within such calculations for this region is poorly understood, especially when propagated through to the flood hazard. We use the IBTrACs data set to estimate natural variability in four key parameters used to describe an idealised cyclone and create a set of idealised but equally likely “1 in 50 year” recurrence interval cyclone events. Each idealised cyclone is then used to force a storm surge model to give predicted peak water levels along the northern Bay of Bengal coast. Finally, extreme water level uncertainty is propagated through an inundation model to predict flood extent and depth over inland coastal floodplains. The descriptive parameters of 18 cyclone events (between 1990 and 2008) appear to show no statistically significant variation (at the 5 % level) due to landfall location, which allows us to pool characteristics for the entire Bay of Bengal. We find that the natural variability of cyclone parameters translates into large uncertainty both for storm surge height (of the order of metres) and for coastal inundation (hundreds of km2). Using the variability estimates for a 1-in-50-year cyclone event making landfall at the 2007 Sidr location, cyclone central pressure drop uncertainty had the greatest effect upon simulated storm surge magnitude. However, uncertainty within cyclone track characteristics (track speed, landfall and genesis location) has greater influence on subsequent inundation extent. Storm surge hazard uncertainty due to cyclone parameter variability was found to be comparable to the inundation difference simulated when the peak surge coincided with either a mean spring high or low water. Our research indicates the importance of improving extreme water level estimates along the Bay of Bengal coastline for robust flood hazard management decisions in the Bay of Bengal