Related papers: Road map for the tuning of hadronic interaction mo…
Future linear $e^+e^-$ colliders aim for extremely high precision measurements. To achieve this, not only excellent detectors and well controlled machine conditions are needed, but also the best possible estimate of backgrounds. To avoid…
To analyse ultrarelativistic nuclear interactions, usually either dynamical models like the string model are employed, or a thermal treatment based on hadrons or quarks is applied. String models encounter problems due to high string…
High-Energy Physics experiments are rapidly escalating in generated data volume, a trend that will intensify with the upcoming High-Luminosity LHC upgrade. This surge in data necessitates critical revisions across the data processing…
The multihadron production in nucleus-nucleus collisions and its interrelation with that in (anti)proton-proton interactions are studied by exploring the charged particle mean multiplicity collision-energy and centrality dependencies in the…
A web-based tool called ADFilter was developed to process collision events using autoencoders based on a deep unsupervised neural network. The autoencoders are trained on a small fraction of either collision data or Standard Model Monte…
Dynamical systems with high intrinsic dimensionality are often characterized by extreme events having the form of rare transitions several standard deviations away from the mean. For such systems, order-reduction methods through projection…
The rapid deployment of computing hardware different from the traditional CPU+RAM model in data centers around the world mandates a change in the design of event generators for the Large Hadron Collider, in order to provide economically and…
Hard scattering events in high-energy collisions produce highly virtual partons that subsequently fragment into collimated hadronic cascades. When such partonic showers evolve in a QCD medium, as in deep-inelastic scattering or heavy-ion…
Generative AI is a fast-growing area of research offering various avenues for exploration in high-energy nuclear physics. In this work, we explore the use of generative models for simulating electron-proton collisions relevant to…
Non-significant randomized control trials can hide subgroups of good responders to experimental drugs, thus hindering subsequent development. Identifying such heterogeneous treatment effects is key for precision medicine and many post-hoc…
Autoencoders and generative neural network models have recently gained popularity in fluid mechanics due to their spontaneity and low processing time instead of high fidelity CFD simulations. Auto encoders are used as model order reduction…
Models for vehicle dynamics play an important role in maneuver planning for automated driving. They are used to derive trajectories from given control inputs, or to evaluate a given trajectory in terms of constraint violation or optimality…
We propose a method to measure the hadronic matter compressibility by means of the event-by-event analysis of heavy-ion collisions at high energies. The method, which utilizes the thermodynamical relation between the compressibility and the…
Event generators like Pythia play an important role in physics studies at the Large Hadron Collider (LHC). While they make accurate predictions in the central region, i.e. at pseudorapidities $\eta<5$, a disagreement between Pythia and…
Molecular dynamics (MD) is a powerful technique for studying microscopic phenomena, but its computational cost has driven significant interest in the development of deep learning-based surrogate models. We introduce generative modeling of…
With the High Luminosity LHC coming online in the near future, event generators will need to provide very large event samples to match the experimental precision. Currently, the estimated cost to generate these events exceeds the computing…
The thesis arises in the context of deep learning applications to particle physics. The dissertation follows two main parallel streams: the development of hardware-accelerated tools for event simulation in high-energy collider physics, and…
Adaptable models could greatly benefit robotic agents operating in the real world, allowing them to deal with novel and varying conditions. While approaches such as Bayesian inference are well-studied frameworks for adapting models to…
At high-energy collider experiments, generative models can be used for a wide range of tasks, including fast detector simulations, unfolding, searches of physics beyond the Standard Model, and inference tasks. In particular, it has been…
In the realm of driving technologies, fully autonomous vehicles have not been widely adopted yet, making advanced driver assistance systems (ADAS) crucial for enhancing driving experiences. Adaptive Cruise Control (ACC) emerges as a pivotal…