96 citations · 199 across the 9 of their papers we have counts for
9 papers · 1 filter
Collider-Flavour Complementarity from the bottom to the top
Oliver Atkinson, Christoph Englert, Matthew Kirk +1
Motivated by recently observed anomalies in the flavour sector, we analyse the potential of measurements of top quarks at the Large Hadron Collider (LHC) to provide complementary c…
On the BSM reach of four top production at the LHC
Anisha, Oliver Atkinson, Akanksha Bhardwaj +3
Many scenarios of beyond the Standard Model (BSM) physics give rise to new top-philic interactions that can be probed at proton machines such as the Large Hadron Collider through a…
Quartic Gauge-Higgs couplings: Constraints and Future Directions
Anisha, Oliver Atkinson, Akanksha Bhardwaj +2
Constraints on quartic interactions of the Higgs boson with gauge bosons have been obtained by the experimental LHC collaborations focussing on the so-called framework of flat…
MUonE, muon and electroweak precision constraints within 2HDMs
Oliver Atkinson, Matthew Black, Christoph Englert +2
Two Higgs doublet models are attractive scenarios for physics beyond the Standard Model. In particular, lepton-specific manifestations remain contenders to explain the observed dis…
IRC-safe Graph Autoencoder for unsupervised anomaly detection
Oliver Atkinson, Akanksha Bhardwaj, Christoph Englert +3
Anomaly detection through employing machine learning techniques has emerged as a novel powerful tool in the search for new physics beyond the Standard Model. Historically similar t…
The Flavourful Present and Future of 2HDMs at the Collider Energy Frontier
Oliver Atkinson, Matthew Black, Christoph Englert +3
We study the intersection of flavour and collider physics for Two-Higgs-Doublet models of Type I and II. Drawing from the flavour precision-LHC exotics search complementarity, we a…
Improved Constraints on Effective Top Quark Interactions using Edge Convolution Networks
Oliver Atkinson, Akanksha Bhardwaj, Stephen Brown +3
We explore the potential of Graph Neural Networks (GNNs) to improve the performance of high-dimensional effective field theory parameter fits to collider data beyond traditional re…
Anomaly detection with Convolutional Graph Neural Networks
Oliver Atkinson, Akanksha Bhardwaj, Christoph Englert +2
We devise an autoencoder based strategy to facilitate anomaly detection for boosted jets, employing Graph Neural Networks (GNNs) to do so. To overcome known limitations of GNN auto…
On interference effects in top-philic decay chains
Oliver Atkinson, Christoph Englert, Panagiotis Stylianou
Sizeable exotic Higgs boson production through gluon fusion via top quark loops is correlated with large Higgs decay probabilities into top quark final states, if these are kinemat…