Azimuthal Correlations of D Mesons with Charged Particles in Simulations with the ALICE Experiment

In this work, we present the results of a component-level analysis with Monte Carlo simulations, which aid the interpretation of recent ALICE results of the azimutal correlation distribution of prompt D mesons with charged hadrons in pp and p–Pb collisions at sNN = 5.02 TeV. Parton-level contributions and fragmentation properties are evaluated. Charm and beauty contributions are compared in order to identify the observables that serve as sensitive probes of the production and hadronisation of heavy quarks.

On next to soft threshold corrections to DIS and SIA processes

We study the perturbative structure of threshold enhanced logarithms in the coefficient functions of deep inelastic scattering (DIS) and semi-inclusive e+e− annihilation (SIA) processes and setup a framework to sum them up to all orders in perturbation theory. Threshold logarithms show up as the distributions ((1−z)−1 logi(1−z))+ from the soft plus virtual (SV) and as logarithms logi(1−z) from next to SV (NSV) contributions. We use the Sudakov differential and the renormalisation group equations along with the factorisation properties of parton level cross sections to obtain the resummed result which predicts SV as well as next to SV contributions to all orders in strong coupling constant. In Mellin N space, we resum the large logarithms of the form logi(N) keeping 1/N corrections. In particular, the towers of logarithms, each of the form $$ {a}_s^n/{N}^{\alpha }{\log}^{2n-\alpha }(N),{a}_s^n/{N}^{\alpha }{\log}^{2n-1-\alpha }(N)\cdots $$ a s n / N α log 2 n − α N , a s n / N α log 2 n − 1 − α N ⋯ etc for α = 0, 1, are summed to all orders in as.

Precise predictions for photon pair production matched to parton showers in GENEVA

We present a new calculation for the production of isolated photon pairs at the LHC with $$ {\mathrm{NNLL}}_{{\mathcal{T}}_0}^{\prime } $$ NNLL T 0 ′ +NNLO accuracy. This is the first implementation within the Geneva Monte Carlo framework of a process with a nontrivial Born-level definition which suffers from QED singularities. Throughout the computation we use a smooth-cone isolation algorithm to remove such divergences. The higher-order resummation of the 0-jettiness resolution variable $$ {\mathcal{T}}_0 $$ T 0 is based on a factorisation formula derived within Soft-Collinear Effective Theory which predicts all of the singular, virtual and real NNLO corrections. Starting from this precise parton-level prediction and by employing the Geneva method, we provide fully showered and hadronised events using Pythia8, while retaining the NNLO QCD accuracy for observables which are inclusive over the additional radiation. We compare our final predictions to LHC data at 7 TeV and find good agreement.

Invertible networks or partons to detector and back again

For simulations where the forward and the inverse directions have a physics meaning, invertible neural networks are especially useful. A conditional INN can invert a detector simulation in terms of high-level observables, specifically for ZW production at the LHC. It allows for a per-event statistical interpretation. Next, we allow for a variable number of QCD jets. We unfold detector effects and QCD radiation to a pre-defined hard process, again with a per-event probabilistic interpretation over parton-level phase space.

How to GAN away detector effects

LHC analyses directly comparing data and simulated events bear the danger of using first-principle predictions only as a black-box part of event simulation. We show how simulations, for instance, of detector effects can instead be inverted using generative networks. This allows us to reconstruct parton level information from measured events. Our results illustrate how, in general, fully conditional generative networks can statistically invert Monte Carlo simulations. As a technical by-product we show how a maximum mean discrepancy loss can be staggered or cooled.