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

Inclusive jet spectrum for small-radius jets

Following on our earlier work on leading-logarithmic (LLR) resummations for the properties of jets with a small radius, R, we here examine the phenomenological considerations for the inclusive jet spectrum. We discuss how to match the NLO predictions with small-R resummation. As part of the study we propose a new, physically-inspired prescription for fixed-order predictions and their uncertainties. We investigate the R-dependent part of the next-to-next-to-leading order (NNLO) corrections, which is found to be substantial, and comment on the implications for scale choices in inclusive jet calculations. We also examine hadronisation corrections, identifying potential limitations of earlier analytical work with regards to their $p_t$-dependence. Finally we assemble these different elements in order to compare matched (N)NLO+LLR predictions to data from ALICE and ATLAS, finding improved consistency for the R-dependence of the results relative to NLO predictions.Comment: 42 pages, 24 figures, additional material at http://microjets.hepforge.org/, updated to match published versio

Jet tagging in the Lund plane with graph networks

The identification of boosted heavy particles such as top quarks or vector bosons is one of the key problems arising in experimental studies at the Large Hadron Collider. In this article, we introduce LundNet, a novel jet tagging method which relies on graph neural networks and an efficient description of the radiation patterns within a jet to optimally disentangle signatures of boosted objects from background events. We apply this framework to a number of different benchmarks, showing significantly improved performance for top tagging compared to existing state-of-the-art algorithms. We study the robustness of the LundNet taggers to non-perturbative and detector effects, and show how kinematic cuts in the Lund plane can mitigate overfitting of the neural network to model-dependent contributions. Finally, we consider the computational complexity of this method and its scaling as a function of kinematic Lund plane cuts, showing an order of magnitude improvement in speed over previous graph-based taggers.Comment: 23 pages, 12 figures, code available at https://github.com/fdreyer/lundne

Vector-Boson Fusion Higgs Pair Production at N3^3LO

We calculate the next-to-next-to-next-to-leading order (N$^3$LO) QCD corrections to vector-boson fusion (VBF) Higgs pair production in the limit in which there is no partonic exchange between the two protons. We show that the inclusive cross section receives negligible corrections at this order, while the scale variation uncertainties are reduced by a factor four. We present differential distributions for the transverse momentum and rapidity of the final state Higgs bosons, and show that there is almost no kinematic dependence to the third order corrections. Finally we study the impact of deviations from the Standard Model in the trilinear Higgs coupling, and show that the structure of the higher order corrections does not depend on the self-coupling. These results are implemented in the latest release of the proVBFH-incl program.Comment: 10 pages, 9 figures, updated to match published versio

Framing energetic top-quark pair production at the LHC

Top-quark pair production is central to many facets of LHC physics. At leading order, the top and anti-top are produced in a back-to-back topology, however this topology accounts only for a minority of $t \bar t$ events with TeV-scale momentum transfer. The remaining events instead involve the splitting of an initial or final-state gluon to $t \bar t$. We provide simple quantitative arguments that explain why this is the case and examine the interplay between different topologies and a range of variables that characterise the event hardness. We then develop a method to classify the topologies of individual events and use it to illustrate our findings in the context of simulated events, using both top partons and suitably defined fiducial tops. For events with large $t \bar t$ invariant mass, we comment on additional features that have important experimental and theoretical implications.Comment: 23 pages + 2 appendice

Inverse folding for antibody sequence design using deep learning

We consider the problem of antibody sequence design given 3D structural information. Building on previous work, we propose a fine-tuned inverse folding model that is specifically optimised for antibody structures and outperforms generic protein models on sequence recovery and structure robustness when applied on antibodies, with notable improvement on the hypervariable CDR-H3 loop. We study the canonical conformations of complementarity-determining regions and find improved encoding of these loops into known clusters. Finally, we consider the applications of our model to drug discovery and binder design and evaluate the quality of proposed sequences using physics-based methods.Comment: 2023 ICML Workshop on Computational Biology, model weights available at https://zenodo.org/record/816469