Related papers: Geometry-aware Autoregressive Models for Calorimet…
In this paper we propose a geometry-aware model for video object detection. Specifically, we consider the setting that cameras can be well approximated as static, e.g. in video surveillance scenarios, and scene pseudo depth maps can…
Machine learning techniques are essential tools to compute efficient, yet accurate, force fields for atomistic simulations. This approach has recently been extended to incorporate quantum computational methods, making use of variational…
Dark matter direct detection experiments are designed to look for the scattering of dark matter particles that are assumed to move with virial velocities $\sim 10^{-3}$. At these velocities, the energy deposition in the detector is large…
Autoregressive generative models of images tend to be biased towards capturing local structure, and as a result they often produce samples which are lacking in terms of large-scale coherence. To address this, we propose two methods to learn…
Correctly capturing the symmetry transformations of data can lead to efficient models with strong generalization capabilities, though methods incorporating symmetries often require prior knowledge. While recent advancements have been made…
We introduce a new Monte Carlo template-based reconstruction method for air shower arrays, with a focus on shower core and energy reconstruction of $\gamma$-ray induced air showers. The algorithm fits an observed lateral amplitude…
In recent years, molecular representation learning has emerged as a key area of focus in various chemical tasks. However, many existing models fail to fully consider the geometric information of molecular structures, resulting in less…
Neural density estimators are flexible families of parametric models which have seen widespread use in unsupervised machine learning in recent years. Maximum-likelihood training typically dictates that these models be constrained to specify…
In this study, a novel approach is demonstrated for converting calorimeter images from fast simulations to those akin to comprehensive full simulations, utilizing conditional Generative Adversarial Networks (GANs). The concept of pix2pix is…
We describe the design and performance the calorimeter systems used in the ECCE detector design to achieve the overall performance specifications cost-effectively with careful consideration of appropriate technical and schedule risks. The…
The possibility to use Neural Networks for reconstruction of the energy deposited in the calorimetry system of the CMS detector is investigated. It is shown that using feed - forward neural network, good linearity, Gaussian energy…
The Analog Hadron Calorimeter (AHCAL) concept developed by the CALICE collaboration is a highly granular sampling calorimeter with \SI{3x3}{\square\centi\meter} plastic scintillator tiles individually read out by silicon photomultipliers…
The next generation of collider detectors will make full use of Particle Flow Algorithms, requiring high-precision tracking and full imaging calorimeters. The latter, thanks to granularity improvements by two to three orders of magnitude…
There are a large number of physics programs one can explore in electron-nucleus collisions at a future electron-ion collider. Collision geometry is very important in these studies, while the measurement for an event-by-event geometric…
The success of high energy physics programs relies heavily on accurate detector simulations and beam interaction modeling. The increasingly complex detector geometries and beam dynamics require sophisticated techniques in order to meet the…
The computational cost for high energy physics detector simulation in future experimental facilities is going to exceed the current available resources. To overcome this challenge, new ideas on surrogate models using machine learning…
Deep generative models are universal tools for learning data distributions on high dimensional data spaces via a mapping to lower dimensional latent spaces. We provide a study of latent space geometries and extend and build upon previous…
The analytical representation of the longitudinal hadronic shower development from the face of a calorimeter is presented and compared with experimental data. The suggested formula is particularly useful at designing, testing and…
A forward electromagnetic calorimeter (FoCal) based on SiW technology is being considered as a possible upgrade to the ALICE detector. This device should in particular feature an extremely high granularity allowing gamma/pi0 discrimination…
We provide a bridge between generative modeling in the Machine Learning community and simulated physical processes in High Energy Particle Physics by applying a novel Generative Adversarial Network (GAN) architecture to the production of…