Jet grooming through reinforcement learning

We introduce a novel implementation of a reinforcement learning (RL) algorithm which is designed to find an optimal jet grooming strategy, a critical tool for collider experiments. The RL agent is trained with a reward function constructed to optimize the resulting jet properties, using both signal and background samples in a simultaneous multi-level training. We show that the grooming algorithm derived from the deep RL agent can match state-of-the-art techniques used at the Large Hadron Collider, resulting in improved mass resolution for boosted objects. Given a suitable reward function, the agent learns how to train a policy which optimally removes soft wide-angle radiation, allowing for a modular grooming technique that can be applied in a wide range of contexts. These results are accessible through the corresponding GroomRL framework.Comment: 11 pages, 10 figures, code available at https://github.com/JetsGame/GroomRL, updated to match published versio

Fully differential Vector-Boson Fusion Higgs Pair Production at Next-to-Next-to-Leading Order

We calculate the fully differential next-to-next-to-leading order (NNLO) QCD corrections to vector-boson fusion (VBF) Higgs pair production. This calculation is achieved in the limit in which there is no colored cross-talk between the colliding protons, using the projection-to-Born method. We present differential cross sections of key observables, showing corrections of up to 3-4% at this order after typical VBF cuts, with the total cross section receiving contributions of about 2%. In contrast to single Higgs VBF production, we find that the NNLO corrections are for the most part within the next-to-leading order scale uncertainty bands.Comment: 5 pages, 3 figures, updated to match published versio

Owners of developed land versus owners of undeveloped land: why land use is more constrained in the Bay Area than in Pittsburgh

We model residential land use constraints as the outcome of a political economy game between owners of developed and owners of undeveloped land. Land use constraints are interpreted as shadow taxes that increase the land rent of already developed plots and reduce the amount of new housing developments. In general equilibrium, locations with nicer amenities are more developed and, as a consequence, more regulated. We test our model predictions by geographically matching amenity, land use, and historical Census data to metropolitan area level survey data on regulatory restrictiveness. Following the predictions of the model, we use amenities as instrumental variables and demonstrate that metropolitan areas with better amenities are more developed and more tightly regulated than other areas. Consistent with theory, metropolitan areas that are more regulated also grow more slowly