SamBa TimeS

→ America/Sao_Paulo
Florian Eppel (JMU Würzburg), Luan Orion Baraúna (National Institute for Space Research), Sarah Wagner (Uni Wü), Sjoerd Bouma (ECAP)
Description

São Paulo meets Bavaria - A Time Series Workshop

Although the underlying physics may be very different, similar challenges arise in time series analysis in various areas of astrophysics. The SamBa TimeS workshop aims to facilitate an intensive and dynamic exchange of algorithmic tools developed for radio, multi-wavelength and radio neutrino astronomy. 

The workshop is hosted at the National Instituto of Space Research São Paulo (INPE). It is organized by a joint team from ECAP (Germany), JMU Würzburg (Germany) and INPE (Brazil) and is targeted to master and PhD students. Bachelor students and Post-Docs are also welcome to join. The one-day program consists of talks on basic principles of time series analysis (i.e. basic time series features, extractions, data denoising, forecasting, machine learning classical tools, deep learning classifications, bayesian block analysis), followed by hands-on sessions to share our knowledge and software tools within the participating institutions.

Some of the problems that will be discussed include:

  • Fast Radio Burst (FRB) detection methods and RFI classification: The detection and processing of transient radio data is a major challenge in radio astronomy and both sides of the collaboration will profit from joint analysis tools that can be deployed for the BINGO and Effelsberg telescopes to perform searches for new Fast Radio Bursts. We will improve existing tools and expand them using Machine Learning (ML) methods which will contribute to the on-going search for the progenitor of FRBs.
  • Multi-wavelength light curve analysis: Our team has immediate access to radio, X-ray and $\gamma$-ray data of variable AGN sources. We are aiming to study variability properties (modulation index, periodicity) of these sources and perform multi-wavelength cross-correlation studies to constrain emission models in AGN.
  • Pulse identification of radio neutrino signals: The identification of neutrino signals for in-ice radio neutrino observatories (RNO-G, IceCube-Gen2) in noisy data is a major challenge for both reconstruction and background rejection. We will attempt to use deep-learning methods to improve on existing approaches to this problem.
Registration
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