Related papers: Generative Artificial Intelligence for Air Shower …
While deep-learning downscaling algorithms can generate fine-scale climate projections cost-effectively, it is still unclear how well they will extrapolate to unobserved climates. We assess the extrapolation capabilities of a deterministic…
Testing new, innovative technologies is a crucial task for safety and acceptance. But how can new systems be tested if no historical real-world data exist? Simulation provides an answer to this important question. Classical simulation tools…
The precise simulation of particle transport through detectors remains a key element for the successful interpretation of high energy physics results. However, Monte Carlo based simulation is extremely demanding in terms of computing…
The program CORSIKA, usually used to simulate extensive cosmic ray air showers, has been adapted to a water medium in order to study the acoustic detection of ultra high energy neutrinos. Showers in water from incident protons and from…
Recently, Generative Adversarial Networks (GANs) trained on samples of traditionally simulated collider events have been proposed as a way of generating larger simulated datasets at a reduced computational cost. In this paper we point out…
Heavy quarks are commonly produced in current accelerator experiments. Hence it is natural to think that they should be likewise created in collisions with larger center of mass energies like the ones involving ultra-high energy cosmic rays…
Climate models encapsulate our best understanding of the Earth system, allowing research to be conducted on its future under alternative assumptions of how human-driven climate forces are going to evolve. An important application of climate…
The CORSIKA program, usually used to simulate extensive cosmic ray air showers, has been adapted to work in a water or ice medium. The adapted CORSIKA code was used to simulate hadronic showers produced by neutrino interactions. The…
We present a method to generate renewable scenarios using Bayesian probabilities by implementing the Bayesian generative adversarial network~(Bayesian GAN), which is a variant of generative adversarial networks based on two interconnected…
High-energy neutrino astronomy represents an open window both on astrophysical mechanisms of particle acceleration and on fundamental interactions. The possibility of detecting them in large earth-based apparatus, like AUGER, AMANDA,…
The particle showers produced in the atmosphere due to the interactions of primary cosmic particles require a thorough understanding in the backdrop of searches for rare interactions. In this work, we made a comprehensive study of air…
When high-energy cosmic rays (gamma's, protons, or heavy nuclei) impinge onto the Earth's atmosphere, they interact at high altitude with the air nuclei as targets. By repeated interaction of the secondaries an `extensive air shower' (EAS)…
New experiments, exploring the ultra-high energy tail of the cosmic ray spectrum with unprecedented detail, are exerting a severe pressure on extensive air hower modeling. Detailed fast codes are in need in order to extract and understand…
Generating wind power scenarios is very important for studying the impacts of multiple wind farms that are interconnected to the grid. We develop a graph convolutional generative adversarial network (GCGAN) approach by leveraging GAN's…
Spatial distributions of energy deposited by an extensive air shower in the atmosphere through ionization, as obtained from the CORSIKA simulation program, are used to find the fluorescence light distribution in the optical image of the…
Extracting non-Gaussian information from the next generation weak lensing surveys will require fast and accurate full-sky simulations. This is difficult to achieve in practice with existing simulation methods: ray-traced $N$-body…
Motivated by the computational limitations of simulating interactions of particles in highly-granular detectors, there exists a concerted effort to build fast and exact machine-learning-based shower simulators. This work reports progress on…
The Cloud paradigm is at a critical point in which the existing energy-efficiency techniques are reaching a plateau, while the computing resources demand at Data Center facilities continues to increase exponentially. The main challenge in…
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…
In this work, we provide an efficient and realistic data-driven approach to simulate astronomical images using deep generative models from machine learning. Our solution is based on a variant of the generative adversarial network (GAN) with…