Probing the interaction of semi-hard quarks and gluons with the underlying event in light- and heavy-flavor triggered proton-proton collisions

We study underlying-event observables in inelastic proton-proton (pp) collisions at a centre-of-mass energy of $$\sqrt{s} = 13$$ s = 13 TeV with identified light and heavy-flavor triggers using the PYTHIA 8 event generator. The study is performed as a function of the transverse momentum of the leading particle ($$p_\mathrm {T}^\mathrm{trigger}$$ p T trigger ). While at high $$p_\mathrm {T}^\mathrm{trigger}$$ p T trigger ($$>10$$ > 10 GeV/c) the underlying-event activity is independent of the leading particle species, at intermediate $$p_\mathrm {T}^\mathrm{trigger}$$ p T trigger ($$2<p_\mathrm {T}^\mathrm{trigger} <8$$ 2 < p T trigger < 8 GeV/c) it is larger in pion-triggered events than in events triggered with B mesons. Moreover, the underlying event in pion-triggered events, the majority of which are initiated by gluons, shows a stronger effect of color reconnection than events triggered with B-hadrons, that are mostly initiated by quark jets. The effect is observed at both hadronic and partonic level. Given that color reconnection affects the interaction among final partons before the hadronization, and that in the string model quarks (gluons) are connected to one (two) string piece(s), we conclude that the observed effect can be attributed to differences in the interactions of gluon and quark jets with the underlying event.

Beautiful and charming chromodipole moments

In the context of the Standard Model effective field theory we derive direct and indirect bounds on chromodipole operators involving the bottom and charm quark. We find that the experimental upper limit on the neutron electric dipole moment puts severe constraints on the imaginary parts of the Wilson coefficients of both chromodipole operators. The magnitudes of the Wilson coefficients are instead only weakly constrained by dijet searches and Z-boson production in association with bottom-quark jets. Flavour physics does not provide meaningful bounds.

Combine and conquer: event reconstruction with Bayesian Ensemble Neural Networks

Ensemble learning is a technique where multiple component learners are combined through a protocol. We propose an Ensemble Neural Network (ENN) that uses the combined latent-feature space of multiple neural network classifiers to improve the representation of the network hypothesis. We apply this approach to construct an ENN from Convolutional and Recurrent Neural Networks to discriminate top-quark jets from QCD jets. Such ENN provides the flexibility to improve the classification beyond simple prediction combining methods by linking different sources of error correlations, hence improving the representation between data and hypothesis. In combination with Bayesian techniques, we show that it can reduce epistemic uncertainties and the entropy of the hypothesis by simultaneously exploiting various kinematic correlations of the system, which also makes the network less susceptible to a limitation in training sample size.

Improving heavy dijet resonance searches using jet substructure at the LHC

The search for new physics at high energy accelerators has been at the crossroads with very little hint of signals suggesting otherwise. The challenges at a hadronic machine such as the LHC is compounded by the fact that final states are swamped with jets which one needs to understand and unravel. A positive step in this direction would be to separate the jets in terms of their gluonic and quark identities, much in a similar spirit of distinguishing heavy quark jets from light quark jets that has helped in improving searches for both neutral and charged Higgs bosons at the LHC. In this work, we utilise this information using the jet substructure techniques to comment on possible improvements in sensitivity as well as discrimination of new resonances in the all hadronic mode that would be crucial in pinning down new physics signals at HL-LHC, HE-LHC and any future 100 TeV hadron collider.