LISA data analysis: from classical methods to machine learning

Session

Conference session 4

25 nov. 2022, 09:00

L2IT Toulouse

21. A neoclassical approach to coherent gravitational-wave search

Alvin Chua (National University of Singapore)

25/11/2022 09:00

Remote talk

The low instantaneous signal-to-noise ratio for most classes of gravitational-wave source necessitates the use of coherent search, which looks for signals in some length of data through phase comparisons against modeled templates. Such comparisons are statistics of the data, defined as functions on the model space. Coherent statistics suffer from uncontrolled variations over the space, which...

11. Detecting gravitational waves from extreme mass ratio inspirals using convolutional neural networks

Xue-Ting Zhang (School of Physics and Astronomy, Sun Yat-sen University)

25/11/2022 09:30

Remote talk

Extreme mass ratio inspirals (EMRIs) are among the most interesting gravitational wave (GW) sources for space-borne GW detectors. However, successful GW data analysis remains challenging due to many issues, ranging from the difficulty of modeling accurate waveforms, to the impractically large template bank required by the traditional matched filtering search method. In this work, we introduce...

15. FastEMRIWaveforms: New tools for millihertz gravitational-wave data analysis

Lorenzo Speri (Max Planck Institute for Gravitational physics (Albert Einstein Institute, AEI Potsdam))

25/11/2022 09:45

In person talk

The observations of gravitational wave signals from extreme-mass-ratio inspirals (EMRIs) have a huge scientific potential for the LISA mission because the system's parameters will be constrained to unprecedented precision. However, high precision comes with new challenges. EMRIs are the only sources that combine the challenges of strong-field complexity with that of long-lived signals. The...

4. Progress towards a semi-coherent search for EMRI signals

Diganta Bandopadhyay (University of Birmingham)

25/11/2022 10:00

In person talk

The search for extreme mass ratio inspiral (EMRI) event signatures in the LISA data-stream necessitated new data analysis strategies to search for and to characterise EMRIs in parameter space. While elements of EMRI data analysis have been demonstrated before, they have typically involved simplifying assumptions and have tended to separate the 'search' and 'characterisation' parts of the...

29. Love and EMRIs in SPA

Gabriel Andres Piovano (University College Dublin)

25/11/2022 10:15

In person talk

The capture of stellar-mass compact objects by a supermassive black hole is one of the most exciting sources detectable by future space-borne interferometers like LISA. The parameters of these extreme-mass-ratio inspirals (EMRIs) binaries are expected to be inferred with excellent accuracy, allowing for unprecedented tests on the nature of their components. Measuring the tidal Love number of...

13. GPry - Fast Bayesian inference with Gaussian Processes

Jonas El Gammal (University of Stavanger)

25/11/2022 11:00

In person talk

LISA will offer a window into many different, simultaneously observed sources in a long, narrow band signal. Inferring source parameters will - for complex waveforms - require computationally expensive simulations, especially if those waveforms are generated in time domain.
This in turn makes Bayesian inference using such codes extremely expensive: typical sampling algorithms like Markov...

51. Infrastructure for Lisa Data Analysis

Antoine Tran (CNES), Hong-Nga Nguyen, Marseille Matthieu (CNES)

25/11/2022 11:15

In person talk

53. Final remarks

25/11/2022 11:45