arXiv subjects
Publications and source records attributed to Andrzej Siodmok.
The LHC physics programme involves a vast amount of Monte Carlo event simulation. This paper reviews current efforts towards sharing the generated events as Open Data. Open Event Generation helps reduce duplication of effort and resource consumption, and benefits the whole High Energy Physics community. We give examples of use cases and user experiences, discuss financial and environmental savings, and suggest future directions.
The accurate identification of heavy-flavour jets, those which originate from bottom or charm quarks, is crucial for precision studies of the Standard Model and searches for new physics. However, assigning flavour to jets presents significant challenges, primarily due to issues with infrared and collinear (IRC) safety. This paper aims to address these challenges by evaluating recently-proposed jet algorithms designed to be IRC-safe and applicable in high-precision measurements. We compare these algorithms across benchmark heavy-flavour production processes and kinematic regimes that are relevant for LHC phenomenology. Exploiting both fixed-order calculations in QCD as well as parton shower simulations, we analyse the infrared sensitivity of these new algorithms at different stages of the event evolution and compare to flavour-labelling strategies currently adopted by LHC collaborations. The results highlight that, while all algorithms lead to more robust flavour-assignments compared to current techniques, they vary in performance depending on the observable and energy regime. The study lays groundwork for robust, flavour-aware jet analyses in current and future collider experiments to maximise the physics potential of experimental data by reducing discrepancies between theoretical and experimental methods.
We present an autodifferentiable rejection sampling algorithm termed Rejection Sampling with Autodifferentiation (RSA). In conjunction with reweighting, we show that RSA can be used for efficient parameter estimation and model exploration. Additionally, this approach facilitates the use of unbinned machine-learning-based observables, allowing for more precise, data-driven fits. To showcase these capabilities, we apply an RSA-based parameter fit to a simplified hadronization model.
In order to stimulate new engagement and trigger some concrete studies in areas where further work would be beneficial towards fully understanding the physics potential of an $e^+e^-$ Higgs / Top / Electroweak factory, we propose to define a set of focus topics. The general reasoning and the proposed topics are described in this document.
Hadronization models used in event generators are physics-inspired functions with many tunable parameters. Since we do not understand hadronization from first principles, there have been multiple proposals to improve the accuracy of hadronization models by utilizing more flexible parameterizations based on neural networks. These recent proposals have focused on the kinematic properties of hadrons, but a full model must also include particle flavor. In this paper, we show how to build a deep learning-based hadronization model that includes both kinematic (continuous) and flavor (discrete) degrees of freedom. Our approach is based on Generative Adversarial Networks and we show the performance within the context of the cluster hadronization model within the Herwig event generator.
Hadronization is a critical step in the simulation of high-energy particle and nuclear physics experiments. As there is no first principles understanding of this process, physically-inspired hadronization models have a large number of parameters that are fit to data. Deep generative models are a natural replacement for classical techniques, since they are more flexible and may be able to improve the overall precision. Proof of principle studies have shown how to use neural networks to emulate specific hadronization when trained using the inputs and outputs of classical methods. However, these approaches will not work with data, where we do not have a matching between observed hadrons and partons. In this paper, we develop a protocol for fitting a deep generative hadronization model in a realistic setting, where we only have access to a set of hadrons in data. Our approach uses a variation of a Generative Adversarial Network with a permutation invariant discriminator. We find that this setup is able to match the hadronization model in Herwig with multiple sets of parameters. This work represents a significant step forward in a longer term program to develop, train, and integrate machine learning-based hadronization models into parton shower Monte Carlo programs.
Hadronization is a complex quantum process whereby quarks and gluons become hadrons. The widely-used models of hadronization in event generators are based on physically-inspired phenomenological models with many free parameters. We propose an alternative approach whereby neural networks are used instead. Deep generative models are highly flexible, differentiable, and compatible with Graphical Processing Unit (GPUs). We make the first step towards a data-driven machine learning-based hadronization model by replacing a compont of the hadronization model within the Herwig event generator (cluster model) with a Generative Adversarial Network (GAN). We show that a GAN is capable of reproducing the kinematic properties of cluster decays. Furthermore, we integrate this model into Herwig to generate entire events that can be compared with the output of the public Herwig simulator as well as with $e^+e^-$ data.
This paper has been prepared by the HEP Software Foundation (HSF) Physics Event Generator Working Group (WG), as an input to the second phase of the LHCC review of High-Luminosity LHC (HL-LHC) computing, which is due to take place in November 2021. It complements previous documents prepared by the WG in the context of the first phase of the LHCC review in 2020, including in particular the WG paper on the specific challenges in Monte Carlo event generator software for HL-LHC, which has since been updated and published, and which we are also submitting to the November 2021 review as an integral part of our contribution.
Herwig 7 is a general-purpose Monte Carlo generator of particle collisions comprising both hard perturbative as well as soft phenomenological physics. Herwig is therefore capable to describe the entire final state of hadronized particles in a collision event. A spacetime topology of a parton system entering hadronization is fully described and tested for the first time. A combination of information from particles momenta and spacetime positions is utilized to minimize a boost-invariant distance measure of the parton system. We present a reasonable agreement of the model with a selection of experimental data and conclude that spacetime event topology can be meaningfully used in the further development.
A new release of the Monte Carlo event generator Herwig (version 7.2) is now available. This version introduces a number of improvements, notably: improvements to the simulation of multiple-parton interactions, including diffractive processes; a new model for baryonic colour re-connection; spin correlations in both the dipole and angular-ordered parton showers; improvements to strangeness production; an improved choice of evolution variable in the angular-ordered parton shower; support for generic Lorentz structures in BSM models.
This document reports the first year of activity of the VBSCan COST Action network, as summarised by the talks and discussions happened during the VBSCan Thessaloniki 2018 workshop. The VBSCan COST action is aiming at a consistent and coordinated study of vector-boson scattering from the phenomenological and experimental point of view, for the best exploitation of the data that will be delivered by existing and future particle colliders.
We propose and carry a detailed study of an observable sensitive to different mechanisms of minijet production. The class of observables measures how the transverse momenta of hadrons produced in association with various trigger objects are balanced as a function of rapidity. It is shown that the observables are sensitive to the model parameters relevant to the minijet production mechanisms: low-$p_{\rm T}$ cut-off regulating jet cross-section, transverse distribution of partons in protons, and parton distribution functions. We perform our test at different charge-particle multiplicities and collision energies. The MC models, which describe many features of the LHC data, are found to predict quite different results demonstrating high discriminating power of the proposed observables. We also review mechanisms and components of \sc{Herwig}, \sc{Pythia}, and \sc{Sherpa} Monte Carlo models relevant to the minijet production.
We consider soft gluon evolution at the amplitude level to expose the structure of colour reconnection from a perturbative point of view. Considering the cluster hadronization model and an universal Ansatz for the soft anomalous dimension we find strong support for geometric models considered earlier. We also show how reconnection into baryonic systems arises, and how larger cluster systems evolve. Our results provide the dynamic basis for a new class of colour reconnection models for cluster hadronization.
The properties of quark and gluon jets, and the differences between them, are increasingly important at the LHC. However, Monte Carlo event generators are normally tuned to data from $e^+e^-$ collisions which are primarily sensitive to quark-initiated jets. In order to improve the description of gluon jets we make improvements to the perturbative and the non-perturbative modelling of gluon jets and include data with gluon-initiated jets in the tuning for the first time. The resultant tunes significantly improve the description of gluon jets and are now the default in Herwig 7.1.
With the advent and recent extension of the BLHA standard to interface Monte Carlo event generators and one-loop matrix element providers, the Herwig++ event generator has expanded its range of applicability to a multitude of underlying hard processes at NLO QCD. The new NLO development is centered around the Matchbox framework, which turns fixed NLO QCD calculations into parton shower matched calculations - to be matched to the two parton shower variants of Herwig++. Matchbox provides thereby for the automated setup of the underlying fixed NLO QCD calculations and the interface to the one-loop matrix element providers, as well as for an efficient and automated multi-channel phase space sampling, and forms the basis for the NLO capabilities of the new release of Herwig++. Along with several other new features and developments, the new release marks the end of distinguishing Herwig++ and (Fortran) HERWIG, and constitutes the first major release of version 7 of the Herwig event generator.
We carry out simple analytical calculations and Monte Carlo studies to better understand the impact of QCD radiation on some well-known jet substructure methods for jets arising from the decay of boosted Higgs bosons. Understanding differences between taggers for these signal jets assumes particular significance in situations where they perform similarly on QCD background jets. As an explicit example of this we compare the Y-splitter method to the more recently proposed Y-pruning technique. We demonstrate how the insight we gain can be used to significantly improve the performance of Y-splitter by combining it with trimming and show that this combination outperforms the other taggers studied here, at high $p_T$. We also make analytical estimates for optimal parameter values, for a range of methods and compare to results from Monte Carlo studies.
We review the modelling of multiple interactions in the event generator Herwig++ and study implications of recent tuning efforts to Tevatron and LHC data. It is often said that measurements of the effective cross section for double-parton scattering, sigma_effective, are in contradiction with models of the final state of multi-parton interactions, but we show that the Herwig++ model is consistent with both and gives stable predictions for underlying event observables at 14 TeV.
We discuss various issues related to the definition of single and double open charm cross sections. We conclude that LHCb's extraction of sigma_effective, the effective cross section for double-parton scattering, is too large by a factor of two. This correction brings the data from open-charm pairs closer to that from J/psi plus open charm and jet production.