Dethinning Extensive Air Shower Simulations
arXiv:1104.3182 · doi:10.1016/j.astropartphys.2012.03.004
Abstract
We describe a method for restoring information lost during statistical thinning in extensive air shower simulations. By converting weighted particles from thinned simulations to swarms of particles with similar characteristics, we obtain a result that is essentially identical to the thinned shower, and which is very similar to non-thinned simulations of showers. We call this method dethinning. Using non-thinned showers on a large scale is impossible because of unrealistic CPU time requirements, but with thinned showers that have been dethinned, it is possible to carry out large-scale simulation studies of the detector response for ultra-high energy cosmic ray surface arrays. The dethinning method is described in detail and comparisons are presented with parent thinned showers and with non-thinned showers.
References in corpus (1)
Cited by in corpus (17)
- Study of muons from ultra-high energy cosmic ray air showers measured with the Telescope Array experiment
- Constraints on the diffuse photon flux with energies above eV using the surface detector of the Telescope Array experiment
- Mass composition of ultra-high-energy cosmic rays with the Telescope Array Surface Detector Data
- Measurement of the Proton-Air Cross Section with Telescope Array's Middle Drum Detector and Surface Array in Hybrid Mode
- Upper limit on the flux of photons with energies above 10^19 eV using the Telescope Array surface detector
- Search for EeV Protons of Galactic Origin
- A Northern Sky Survey for Point-Like Sources of EeV Neutral Particles with the Telescope Array Experiment
- Using Deep Learning to Enhance Event Geometry Reconstruction for the Telescope Array Surface Detector
- Search for Ultra-High-Energy Neutrinos with the Telescope Array Surface Detector
- Search for point sources of ultra-high energy photons with the Telescope Array surface detector
- Air Shower Simulation and Hadronic Interactions
- Constraining strongly coupled new physics from cosmic rays with machine learning techniques
- Deep learning method for identifying mass composition of ultra-high-energy cosmic rays
- Implementing the De-thinning Method for High Energy Cosmic Rays Extensive Air Shower Simulations
- Cosmic ray mass composition measurement in the energy range from eV to eV observed with the TALE hybrid detector
- CORSIKA 8: A General Framework for Particle Cascade Simulations
- Astroparticle Physics at Eastern Colombia