Quenching effects in the cumulative jet spectrum

The steeply falling jet spectrum induces a bias on the medium modifications of jet observables in heavy-ion collisions. To explore this effect, we develop a novel analytic framework to study the quenched jet spectrum and its cumulative. We include many energy-loss-related effects, such as soft and hard medium induced emissions, broadening, elastic scattering, jet fragmentation, cone size dependence, and coherence effects. We show that different observables, based on the jet spectrum, are connected, e.g., the nuclear modification, spectrum shift, and the quantile procedure. We present the first predictions for the nuclear modification factor and the quantile procedure with cone size dependence. As a concrete example, we compare dijet and boson+jet events to unfold the spectrum bias effects, and improve quark-, and gluon-jet classification using arguments based on the cumulative. Besides pointing out its flexibility, finally, we apply our framework to other energy loss models such as the hybrid weak/strong-coupling approach.

Spin correlations in final-state parton showers and jet observables

As part of a programme to develop parton showers with controlled logarithmic accuracy, we consider the question of collinear spin correlations within the PanScales family of parton showers. We adapt the well-known Collins–Knowles spin-correlation algorithm to PanScales antenna and dipole showers, using an approach with similarities to that taken by Richardson and Webster. To study the impact of spin correlations, we develop Lund-declustering based observables that are sensitive to spin-correlation effects both within and between jets and extend the MicroJets collinear single-logarithmic resummation code to include spin correlations. Together with a 3-point energy correlation observable proposed recently by Chen, Moult and Zhu, this provides a powerful set of constraints for validating the logarithmic accuracy of our shower results. The new observables and their resummation further open the pathway to phenomenological studies of these important quantum mechanical effects.

Disentangling observable dependence in SCETI and SCETII anomalous dimensions: angularities at two loops

The resummation of radiative corrections to collider jet observables using soft collinear effective theory is encoded in differential renormalization group equations (RGEs), with anomalous dimensions depending on the observable under consideration. This observable dependence arises from the ultraviolet (UV) singular structure of real phase space integrals in the effective field theory. We show that the observable dependence of anomalous dimensions in SCETI problems can be disentangled by introducing a suitable UV regulator in real radiation integrals. Resummation in the presence of the new regulator can be performed by solving a two-dimensional system of RGEs in the collinear and soft sectors, and resembles many features of resummation in SCETII theories by means of the rapidity renormalization group. We study the properties of SCETI with the additional regulator and explore the connection with the system of RGEs in SCETII theories, highlighting some universal patterns that can be exploited in perturbative calculations. As an application, we compute the two-loop soft and jet anomalous dimensions for a family of recoil-free angularities and give new analytic results. This allows us to study the relations between the SCETI and SCETII limits for these observables. We also discuss how the extra UV regulator can be exploited to calculate anomalous dimensions numerically, and the prospects for numerical resummation.

Measuring QCD Splittings with Invertible Networks

QCD splittings are among the most fundamental theory concepts at the LHC. We show how they can be studied systematically with the help of invertible neural networks. These networks work with sub-jet information to extract fundamental parameters from jet samples. Our approach expands the LEP measurements of QCD Casimirs to a systematic test of QCD properties based on low-level jet observables. Starting with an toy example we study the effect of the full shower, hadronization, and detector effects in detail.

Deep learning jet modifications in heavy-ion collisions

Jet interactions in a hot QCD medium created in heavy-ion collisions are conventionally assessed by measuring the modification of the distributions of jet observables with respect to the proton-proton baseline. However, the steeply falling production spectrum introduces a strong bias toward small energy losses that obfuscates a direct interpretation of the impact of medium effects in the measured jet ensemble. Modern machine learning techniques offer the potential to tackle this issue on a jet-by-jet basis. In this paper, we employ a convolutional neural network (CNN) to diagnose such modifications from jet images where the training and validation is performed using the hybrid strong/weak coupling model. By analyzing measured jets in heavy-ion collisions, we extract the original jet transverse momentum, i.e., the transverse momentum of an identical jet that did not pass through a medium, in terms of an energy loss ratio. Despite many sources of fluctuations, we achieve good performance and put emphasis on the interpretability of our results. We observe that the angular distribution of soft particles in the jet cone and their relative contribution to the total jet energy contain significant discriminating power, which can be exploited to tailor observables that provide a good estimate of the energy loss ratio. With a well-predicted energy loss ratio, we study a set of jet observables to estimate their sensitivity to bias effects and reveal their medium modifications when compared to a more equivalent jet population, i.e., a set of jets with similar initial energy. Finally, we also show the potential of deep learning techniques in the analysis of the geometrical aspects of jet quenching such as the in-medium traversed length or the position of the hard scattering in the transverse plane, opening up new possibilities for tomographic studies.

Improving the understanding of jet grooming in perturbation theory

Jet grooming has emerged as a necessary and powerful tool in a precision jet physics program. In this paper, we present three results on jet grooming in perturbation theory, focusing on heavy jet mass in e+e−→ hadrons collisions, groomed with the modified mass drop tagger. First, we calculate the analytic cross section at leading-order. Second, using the leading-order result and numerical results through next-to-next-to-leading order, we show that cusps in the distribution on the interior of phase space at leading-order are softened at higher orders. Finally, using analytic and numerical results, we show that terms that violate the assumptions of the factorization theorem for groomed jet mass are numerically much smaller than expected from power counting. These results provide important information regarding the convergence of perturbation theory for groomed jet observables and reliable estimates for residual uncertainties in a precision calculation.