Related papers: New Angles on Fast Calorimeter Shower Simulation
We present LEMURS: an extensive dataset of simulated calorimeter showers designed to support the development and benchmarking of fast simulation methods in high-energy physics, most notably providing a step towards the development of…
Precision measurement of hadronic final states presents complex experimental challenges. The study explores the concept of a gaseous Digital Hadronic Calorimeter (DHCAL) and discusses the potential benefits of employing Graph Neural Network…
Air shower simulations are essential for interpreting data from cosmic ray experiments. At highest energies though, a microscopic treatment of a whole shower is not possible any more, since it would require a huge amount of CPU-time. We…
Computer aided engineering of multi-time-scale plasma systems which exhibit a quasi-steady state solution are challenging due to the large number of time steps required to reach convergence. Machine learning techniques combined with…
Cryogenic buffer gas beam sources have become an essential tool for experiments requiring cold molecular beams with low forward velocities. Although recent experimental advances have led to significant progress in source optimization,…
Calorimeters for particle physics experiments with integration time of a few ns will substantially improve the capability of the experiment to resolve event pileup and to reject backgrounds. In this paper the time development of hadronic…
In materials science, the challenge of rapid prototyping materials with desired properties often involves extensive experimentation to find suitable microstructures. Additionally, finding microstructures for given properties is typically an…
Precise determination of thermodynamic parameters in ultracold Bose gases remains challenging due to the destructive nature of conventional measurement techniques and inherent experimental uncertainties. We demonstrate a machine learning…
Application Specific Integrated Circuits, ASICs, similar to those envisaged for the readout electronics of the central calorimeters of detectors for a future lepton collider have been exposed to high-energy electromagnetic showers. A…
We explore the use of normalizing flows to emulate Monte Carlo detector simulations of photon showers in a high-granularity electromagnetic calorimeter prototype for the International Large Detector (ILD). Our proposed method -- which we…
A complete, kinetic description of electron-seeded strong-field QED showers in crossed electromagnetic fields is derived. The kinetic structure of the shower and its temporal evolution are shown to be a function of two parameters: the…
Simulation models often have parameters as input and return outputs to understand the behavior of complex systems. Calibration is the process of estimating the values of the parameters in a simulation model in light of observed data from…
The ability to generate physically plausible ensembles of variable sources is critical to the optimization of time-domain survey cadences and the training of classification models on datasets with few to no labels. Traditional data…
A report on the characteristics of ultra-high energy air showers simulated with the current version of the AIRES program is presented. The AIRES system includes a fast simulating program, originally designed on the basis of the well-known…
The successful realization of the EIC scientific program requires the design and construction of high-performance particle detectors. Recent developments in the field of scientific computing and increased availability of high performance…
Approximate Bayes Computations (ABC) are used for parameter inference when the likelihood function of the model is expensive to evaluate but relatively cheap to sample from. In particle ABC, an ensemble of particles in the product space of…
The SENECA model, a new hybrid approach to air shower simulations, is presented. It combines the use of efficient cascade equations in the energy range where a shower can be treated as one-dimensional, with a traditional Monte Carlo method…
We introduce the Kernel-Elastic Autoencoder (KAE), a self-supervised generative model based on the transformer architecture with enhanced performance for molecular design. KAE is formulated based on two novel loss functions: modified…
The numerical simulation of granular systems of even moderate size is a challenging computational problem. In most investigations, either Molecular Dynamics or Event-driven Molecular Dynamics is applied. Here we show that in certain cases,…
Deep learning methods have gained popularity in high energy physics for fast modeling of particle showers in detectors. Detailed simulation frameworks such as the gold standard Geant4 are computationally intensive, and current deep…