103 research outputs found
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 -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 NLO
We calculate the next-to-next-to-next-to-leading order (NLO) 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 events with
TeV-scale momentum transfer. The remaining events instead involve the splitting
of an initial or final-state gluon to . 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 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
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