LISA data analysis: from classical methods to machine learning

Session

Conference session 2

24 nov. 2022, 09:00

L2IT Toulouse

7. Towards a complete L0-to-L2 pipeline – Progress in simulation, processing and analysis (part 1)

Jean-Baptiste Bayle (University of Glasgow), Olaf Hartwig (SYRTE, Observatoire de Paris)

24/11/2022 09:00

LISA will allow the simultaneous observation and characterization of thousands of gravitational-wave sources, which presents unique data analysis challenges. Preparing for these challenges requires realistic simulations of the data streams entering the final astrophysical data analysis (L1 data).

First steps towards such demonstrations have been made as part of the LISA Data challenges...

25. Towards a complete L0-to-L2 pipeline – Progress in simulation, processing and analysis (part 2)

Jean-Baptiste Bayle (University of Glasgow), Olaf Hartwig (SYRTE, Observatoire de Paris)

24/11/2022 09:30

In person talk

This is the second part of the talk 'Towards a complete L0-to-L2 pipeline – Progress in simulation, processing and analysis' proposed by J.-B. Bayle.

12. On the effectiveness of null TDI channels as instrument noise monitors in LISA

Martina Muratore (AEI (Potsdam))

24/11/2022 10:00

In person talk

I will present a recent paper we submitted on the use and limits of the Time-Delay Interferometry null channels for in flight estimation of the Laser Interferometer Space Antenna instrumental noise. In the talk I will consider how the two main limiting noise sources, test-mass acceleration noise and interferometric phase measurement noise, propagate through different Time-Delay Interferometry...

33. The impact data gaps have on parameter estimation

Ollie Burke (L2I Toulouse, CNRS/IN2P3, UT3)

24/11/2022 10:15

In person talk

For the LISA instrument, data gaps are entirely unavoidable. We will expect to have scheduled data gaps due to antennae repointing or enforced data gaps to mask out instrumental artefacts. It is absolutely essential that the probabilistic models used to describe the data must be consistent with the data generating process itself. By including gaps in the data stream, the resultant noise...

27. Extracting gravitational-wave backgrounds in noise of unknown spectral shape

Quentin Baghi (CEA Saclay)

24/11/2022 11:00

In person talk

Measuring stochastic gravitational-wave backgrounds (SGWBs), particularly primordial ones, is one of LISA’s most hoped-for outcomes. However, this task is difficult with a single flying detector because it requires an accurate characterisation of the instrumental noise. Assuming that its shape is known or highly constrained is a dangerous bet. In order to make any discovery possible, it is...

50. LISA data analysis: status and challenges

Stanislav Babak (APC)

24/11/2022 11:30

In person talk

22. Inference for LISA with Normalising flows and data representation through source separation

Natalia Korsakova (APC)

24/11/2022 12:00

In person talk

In my talk I am going to concentrate on the models of machine learning which allow us to learn the probability distributions and apply it to the important unsolved problems in the LISA data analysis.
The most common approach to do parameter estimation is through defining the likelihood function and producing posterior samples with some form of sampling technique. The disadvantage of sampling...