479 research outputs found

    PARSEC: A Parametrized Simulation Engine for Ultra-High Energy Cosmic Ray Protons

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    We present a new simulation engine for fast generation of ultra-high energy cosmic ray data based on parametrizations of common assumptions of UHECR origin and propagation. Implemented are deflections in unstructured turbulent extragalactic fields, energy losses for protons due to photo-pion production and electron-pair production, as well as effects from the expansion of the universe. Additionally, a simple model to estimate propagation effects from iron nuclei is included. Deflections in galactic magnetic fields are included using a matrix approach with precalculated lenses generated from backtracked cosmic rays. The PARSEC program is based on object oriented programming paradigms enabling users to extend the implemented models and is steerable with a graphical user interface.Comment: 10 pages, 6 figures, accepted for publication in Astroparticle Physic

    Public Policy and the Legislative Process

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    Cosmic ray propagation with CRPropa 3

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    Solving the question of the origin of ultra-high energy cosmic rays (UHECRs) requires the development of detailed simulation tools in order to interpret the experimental data and draw conclusions on the UHECR universe. CRPropa is a public Monte Carlo code for the galactic and extragalactic propagation of cosmic ray nuclei above 1017\sim 10^{17} eV, as well as their photon and neutrino secondaries. In this contribution the new algorithms and features of CRPropa 3, the next major release, are presented. CRPropa 3 introduces time-dependent scenarios to include cosmic evolution in the presence of cosmic ray deflections in magnetic fields. The usage of high resolution magnetic fields is facilitated by shared memory parallelism, modulated fields and fields with heterogeneous resolution. Galactic propagation is enabled through the implementation of galactic magnetic field models, as well as an efficient forward propagation technique through transformation matrices. To make use of the large Python ecosystem in astrophysics CRPropa 3 can be steered and extended in Python.Comment: 16th International workshop on Advanced Computing and Analysis Techniques in physics research (ACAT 2014) proceedings, 6 pages, 6 figure

    CRPropa 3.0 - a Public Framework for Propagating UHE Cosmic Rays through Galactic and Extragalactic Space

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    The interpretation of experimental data of ultra-high energy cosmic rays (UHECRs) above 10^17 eV is still under controversial debate. The development and improvement of numerical tools to propagate UHECRs in galactic and extragalactic space is a crucial ingredient to interpret data and to draw conclusions on astrophysical parameters. In this contribution the next major release of the publicly available code CRPropa (3.0) is presented. It reflects a complete redesign of the code structure to facilitate high performance computing and comprises new physical features such as an interface for galactic propagation using lensing techniques and inclusion of cosmological effects in a three-dimensional environment. The performance is benchmarked and first applications are presented.Comment: 4 pages, 3 figures. Proceedings of the 33rd International Cosmic Ray Conference (ICRC), Rio de Janeiro, Brazil, 2-9 July 201

    Tree ensemble kernels for Bayesian optimization with known constraints over mixed-feature spaces

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    Tree ensembles can be well-suited for black-box optimization tasks such as algorithm tuning and neural architecture search, as they achieve good predictive performance with little or no manual tuning, naturally handle discrete feature spaces, and are relatively insensitive to outliers in the training data. Two well-known challenges in using tree ensembles for black-box optimization are (i) effectively quantifying model uncertainty for exploration and (ii) optimizing over the piece-wise constant acquisition function. To address both points simultaneously, we propose using the kernel interpretation of tree ensembles as a Gaussian Process prior to obtain model variance estimates, and we develop a compatible optimization formulation for the acquisition function. The latter further allows us to seamlessly integrate known constraints to improve sampling efficiency by considering domain-knowledge in engineering settings and modeling search space symmetries, e.g., hierarchical relationships in neural architecture search. Our framework performs as well as state-of-the-art methods for unconstrained black-box optimization over continuous/discrete features and outperforms competing methods for problems combining mixed-variable feature spaces and known input constraints.Comment: 27 pages, 9 figures, 4 table
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