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ASTERICS-OBELICS PyGamma19 - Python and open data for gamma-ray astronomy

18–22 Mar 2019
MPI for Nuclear Physics
Europe/Berlin timezone

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Session

Thursday

21 Mar 2019, 09:30
MPI for Nuclear Physics

MPI for Nuclear Physics

Saupfercheckweg 1, 69117 Heidelberg

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  1. 36. Archiving data from a software telescope
    Catherine Boisson (Observatoire de Paris)
    21/03/2019, 09:30
    Talk

    In the context of Cherenkov astronomy, the data processing stages imply both assumptions and comparison to dedicated simulation. Those data can be misleading if not documented. This has implications on the format specification for the data that will be exposed to be used in relation with other frequencies for modeling.

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  2. 21. Provenance for multi-messenger astronomy
    Mathieu Servillat (LUTH - Observatoire de Paris)
    21/03/2019, 09:45
    Talk

    Provenance information in astronomy is important to enable scientists to trace back the origin of a dataset, a document or a device, learn about the people and organizations involved in a project and assess the quality as well as the usefulness of the dataset, document or device for their scientific work. Current efforts to model the Provenance information in Astronomy led to the development...

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  3. 19. A legacy portal of MAGIC published results
    Michele Doro (University of Padua and INFN Padua)
    21/03/2019, 10:15
    Talk

    Since the begin of its operation in 2003, the MAGIC telescopes collected data from more than 60 TeV emitters. The collaboration distributes public FITS files with high-level data such as spectral energy distributions, light curves and skymaps for every published result. Here we report on the efforts to complement this products with more ample information (data quality, fit models, etc) and...

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  4. 37. Plenary Discussion
    21/03/2019, 10:45
    Talk
  5. 6. JetSeT: a C/Python framework for numerical modeling of acceleration/radiation processes in relativistic jets and SED fitting
    Dr Andrea Tramacere (Université de Genève)
    21/03/2019, 11:30
    Talk

    JetSeT is an open source Python framework with a C numerical engine, to reproduce radiative and accelerative processes acting in relativistic jets, allowing to fit the numerical models to observed data([https://jetset.readthedocs.io/en/latest/][1] [https://github.com/andreatramacere/jetset][2])

    The main features of this framework are:

    1. handling observed data (rebinning, definition of...
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  6. 16. GAMERA toolkit, more than a Kaiju
    Carlo Romoli (MPI-K)
    21/03/2019, 12:00
    Talk

    GAMERA is a toolbox developed at MPIK for the modelling of gamma-ray emission from fundamental processes in relativistic astrophysics. Beside the emission in steady states, it allows the possibility to compute the time evolution of particle distributions in different astrophysical environments and the consequent photon spectrum detectable from an observer.
    Written in C++ and wrapped in Python,...

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  7. 34. Thursday lightning talks
    21/03/2019, 12:30
    Talk
    • Tamas Gal - Julia - feels like Python, runs like C
    • Toby Burnett - A framework to create Web documents from Python code
    • Jeremy Perkins - Future Gamma-ray Missions of Various Shapes (mostly square) and Sizes
    • Eric Charles - Clever tricks for fast Fermi likelihood
    • Francesco Berlato - Improved GBM GRB localizations with BALROG
    • Simon Steinmaßl - Fitting a Light Curve in 3ML with...
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  8. 25. Python in High-Energy Physics
    Dr Hans Peter Dembinski (Max-Planck-Institute for Nuclear Physics, Heidelberg)
    21/03/2019, 14:30
    Talk

    Python is now the dominant language in scientific computing. Especially astroparticle physics and astronomy experiments have embraced Python enthusiastically, for example, the IceCube Neutrino Observatory. CERN experiments are also moving analysis steadily towards Python. ROOT is the foundational library in many HEP experiments, so I will give a brief summary on current developments in ROOT....

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  9. 32. High performance computing with Python
    Dr Florian Gaté (LAPP - CNRS)
    21/03/2019, 15:00
    Talk

    I will present examples of python code optimization. First we will focus on wrapped C++ code to speed up significantly analysis. Secondly, I will talk about function minimization with PyTorch and how it can be used through two examples: likelihood minimization on GPU for ImPACT optimization and deep learning with the gamma-learn project.

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  10. 41. Numba: Speeding up Python
    Jason Watson (University of Oxford)
    21/03/2019, 15:30
    Talk

    An introduction to Numba, a Python package which compiles Python code on-the-fly to produce efficient machine code, potentially providing huge improvements in execution time.

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  11. 42. Plenary Discussion
    21/03/2019, 15:45

    What are good choices / pros and cons of the various high-performance computing options in Python for our codes?

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  12. 44. PINT - Pulsar timing with Python
    Jing Luo
    21/03/2019, 16:30
    Talk

    https://github.com/nanograv/PINT

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  13. 46. JWST Data Analysis Tools: Open Development of Community Software
    Erik Tollerud (STScI)
    21/03/2019, 17:00
    Talk
  14. 47. The Advanced Scientific Data Format
    Perry Greenfield (STScI)
    21/03/2019, 17:15
    Talk
  15. 43. Sherpa overview
    Aneta Siemiginowska (Chandra X-ray Center)
    21/03/2019, 17:30
    Talk

    Remote presentation

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  16. 48. Sherpa technical notes
    Omar Laurino (Chandra X-ray Center)
    21/03/2019, 17:45
    Talk
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