Related papers: Encoding off-shell effects in top pair production …
The production of dielectrons in reactions involving a_1 mesons and pions is studied. We compare results obtained with different phenomenological Lagrangians that have been used in connection with hadronic matter and finite nuclei. We…
Particle production in equilibrium and nonequilibrium quantum systems is calculated. The effects of the off-shell propagation of nucleons in medium on the particle production are discussed. Comparision to the semiclassical production rate…
Accurate and fast simulation of particle physics processes is crucial for the high-energy physics community. Simulating particle interactions with detectors is both time consuming and computationally expensive. With the proton-proton…
We present an implementation of $t\bar t \ell^+\ell^-$ ($\ell=e,\mu$) hadronic production at next-to-leading order in QCD matched to parton-shower event generators in the POWHEG BOX framework. The program we developed includes all…
We present results from a recent calculation of prompt photon-pair production in association with two jets to next-to-leading order (NLO) at the LHC. The virtual contribution is evaluated using the BlackHat library, a numerical…
Between the Tevatron and LHC, top-quark physics is now becoming an area for precision physics. This has lead to an increase in theoretical activity to match the experimental accuracy of top anti-top production. We discuss the difficulty in…
Triggered by ongoing dark matter searches in the top quark sector at the Large Hadron Collider we report on the calculation of the next-to-leading order QCD corrections to the Standard Model process $pp\to t\bar{t}+ Z(\to \nu_\ell…
In this work we perform the first ever calculation of jet event shapes at hadron colliders at next-to-next-to leading order (NNLO) in QCD. The inclusion of higher order corrections removes the shape difference observed between data and…
Capsule networks are ideal tools to combine event-level and subjet information at the LHC. After benchmarking our capsule network against standard convolutional networks, we show how multi-class capsules extract a resonance decaying to top…
We discuss top-quark pair production at hadron colliders and review available calculations of differential top-pair production cross section in perturbative QCD at approximate next-to-next-to-leading order (NNLO) within the threshold…
New physics and SM parameters can be studied and constrained by looking at the modifications to top-pair differential kinematical distributions due to off-shell effects. I present here three case studies: the determination of the Higgs…
In this proceeding we present predictions for the complete NLO QCD corrections to the production of $t\bar{t}b\bar{b}$ in the dilepton decay channel of the top quark at the LHC with $\sqrt{s}=13$ TeV. The calculation is performed…
Echo state networks are computationally lightweight reservoir models inspired by the random projections observed in cortical circuitry. As interest in reservoir computing has grown, networks have become deeper and more intricate. While…
Deep neural networks have proven to be particularly effective in visual and audio recognition tasks. Existing models tend to be computationally expensive and memory intensive, however, and so methods for hardware-oriented approximation have…
Neural Networks have been proved to work as decoders in telecommunications, so the ways of making it efficient will be investigated in this thesis. The different parameters to maximize the Neural Network Decoder's efficiency will be…
Many practical systems can be described by dynamic networks, for which modern technique can measure their output signals, and accumulate extremely rich data. Nevertheless, the network structures producing these data are often deeply hidden…
Neural networks have dramatically increased our capacity to learn from large, high-dimensional datasets across innumerable disciplines. However, their decisions are not easily interpretable, their computational costs are high, and building…
Many analyses in particle and nuclear physics use simulations to infer fundamental, effective, or phenomenological parameters of the underlying physics models. When the inference is performed with unfolded cross sections, the observables…
Deep Neural Networks are powerful models that attained remarkable results on a variety of tasks. These models are shown to be extremely efficient when training and test data are drawn from the same distribution. However, it is not clear how…
We present the Monte Carlo event generator {\tt WOPPER} for pair production of $W$'s and their decays at high energy $e^+e^-$ colliders. {\tt WOPPER} includes the effects from finite $W$ width and focusses on the calculation of higher order…