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Register a new userEmCa -- Electromagnetic-Cascades Simulation Package
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Added by
Stephan Meighen-Berger
Publication date
2019
fields
Physics
and research's language is
English
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Electromagnetic-Cascades (EmCa) is a Python package for the simulation of electromagnetic cascades in various materials. The showers are modeled using cascade equations and the relevant interactions, specifically pair production, Bremsstrahlung, Compton scattering and ionization. This methodology has the advantage of being computationally inexpensive and fast, unlike Monte Carlo methods. The code includes low and high energy material effects, allowing for a high range of validity of the simulation results. EmCa is easily extendable and offers a framework for testing different electromagnetic interaction models. In combination with MCEq, a Python package for hadronic particle showers using cascade equations, full simulations of atmospheric fluxes can be done.
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Atmospheric muons are one of the main backgrounds for current Water- and Ice-Cherenkov neutrino telescopes designed to detect astrophysical neutrinos. The inclusive fluxes of atmospheric muons and neutrinos from hadronic interactions of cosmic rays have been extensively studied with Monte Carlo and cascade equation methods, for example, CORSIKA and MCEq. However, the muons that are pair produced in electromagnetic interaction of high energy photons are quantitatively not well understood. We present new simulation results and assess the model dependencies of the high-energy atmospheric muon flux including those from electromagnetic interactions, using a new numerical electromagnetic cascade equation solver EmCa that can be easily coupled with the hadronic solver MCEq. Both codes are in active development with the particular aim to become part of the next generation CORSIKA 8 air shower simulation package. The combination of EmCa and MCEq accounts for material effects that have not been previously included in most of the available codes. Hence, the influence of these effects on the air showers will also be briefly discussed.
Using the analytic modeling of the electromagnetic cascades compared with more precise numerical simulations we describe the physical properties of electromagnetic cascades developing in the universe on CMB and EBL background radiations. A cascade is initiated by very high energy photon or electron and the remnant photons at large distance have two-component energy spectrum, $propto E^{-2}$ ($propto E^{-1.9}$ in numerical simulations) produced at cascade multiplication stage, and $propto E^{-3/2}$ from Inverse Compton electron cooling at low energies. The most noticeable property of the cascade spectrum in analytic modeling is strong universality, which includes the standard energy spectrum and the energy density of the cascade $omega_{rm cas}$ as its only numerical parameter. Using numerical simulations of the cascade spectrum and comparing it with recent Fermi LAT spectrum we obtained the upper limit on $omega_{rm cas}$ stronger than in previous works. The new feature of the analysis is $E_{max}$ rule. We investigate the dependence of $omega_{rm cas}$ on the distribution of sources, distinguishing two cases of universality: the strong and weak ones.
QED cascades in a strong electromagnetic field of optical range and arbitrary configuration are considered. A general expression for short-time dependence of the key electron quantum dynamical parameter is derived, allowing to generalize the effective threshold condition of QED cascade onset. The generalized theory is applied to selfsustained cascades in a single focused laser pulse. According to numerical simulations, if a GeV electron bunch is used as a seed, an ordinary cascade can be converted into the selfsustained one. As an application, it would be also possible to produce this way bright collimated photon beams with up to GeV photon energies.
Automated searches for strong gravitational lensing in optical imaging survey datasets often employ machine learning and deep learning approaches. These techniques require more example systems to train the algorithms than have presently been discovered, which creates a need for simulated images as training dataset supplements. This work introduces and summarizes deeplenstronomy, an open-source Python package that enables efficient, large-scale, and reproducible simulation of images of astronomical systems. A full suite of unit tests, documentation, and example notebooks are available at https://deepskies.github.io/deeplenstronomy/ .
Background. It is assumed that the introduction of stochastic in mathematical model makes it more adequate. But there is virtually no methods of coordinated (depended on structure of the system) stochastic introduction into deterministic models. Authors have improved the method of stochastic models construction for the class of one-step processes and illustrated by models of population dynamics. Population dynamics was chosen for study because its deterministic models were sufficiently well explored that allows to compare the results with already known ones. Purpose. To optimize the models creation as much as possible some routine operations should be automated. In this case, the process of drawing up the model equations can be algorithmized and implemented in the computer algebra system. Furthermore, on the basis of these results a set of programs for numerical experiment can be obtained. Method. The computer algebra system Axiom is used for analytical calculations implementation. To perform the numerical experiment FORTRAN and Julia languages are used. The method Runge--Kutta method for stochastic differential equations is used as numerical method. Results. The program compex for creating stochastic one-step processes models is constructed. Its application is illustrated by the predator-prey population dynamic system. Conclusions. Computer algebra systems are very convenient for the purposes of rapid prototyping in mathematical models design and analysis.
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