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LISA Data Generation and Analysis Workshop

7–10 oct. 2024
Online
Fuseau horaire Europe/Paris

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Liste des Contributions

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  1. 6. Introduction
    Eleonora Castelli (NASA GSFC | UMBC), Jean-Baptiste Bayle (University of Glasgow), Natalia Korsakova (APC), Dr Quentin Baghi (APC | Université Paris Cité)
    07/10/2024 14:40
  2. 1. Getting Started (1/2)
    Eleonora Castelli (NASA GSFC | UMBC)
    07/10/2024 15:00

    Learn how to use the time-domain simulation tools to generate a simple LDC-like dataset containing realistic instrumental noise and one MBHB signal. The goal of this session is to get familiar with the basic simulation tools (LISA Instrument, LISA Orbits, LISA GW Response, and the LDC toolbox).

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  3. 4. Getting Started (2/2)
    Natalia Korsakova (APC), Dr Nikolaos Karnesis (AUTh)
    07/10/2024 16:45

    Learn how to quickly setup and run an MCMC on the dataset produced during Getting Started (1/2) to estimate the MHBH source parameters. The goal of this session is to learn how to use Eryn in a simple setup.

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  4. 2. Simulation in depth (1/2)
    Dr Henri Inchauspé (Institute for Theoretical Physics, Universität Heidelberg)
    08/10/2024 16:00

    We will learn how to simulate a stochastic gravitational-wave background, using realistic instrumental setups.

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  5. 7. Simulation in depth (2/2)
    Martin Staab (SYRTE)
    08/10/2024 18:00

    We will learn how to parametrize the instrumental simulation to use realistic orbits, enable and disable instrumental noises and other instrumental effects; how to inject glitches; how to compute the various TDI combinations

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  6. 3. Towards a global fit (1/2)
    Dr Quentin Baghi (APC | Université Paris Cité)
    09/10/2024 16:00

    In this session, we will focus on fitting an unknown noise, as well as a stochastic gravitational-wave background (SGWB).

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  7. 5. Towards a global fit (2/2)
    Senwen Deng (APC)
    10/10/2024 16:00

    In this session, we will move towards fitting an ensemble of Galactic binaries. We will first start with a known number of signals in a narrow frequency band, with known noise; and then introduce reversible jump MCMC to also estimate the number of sources.

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  8. 8. Conclusion
    Jean-Baptiste Bayle (University of Glasgow)
    10/10/2024 18:15