Deep Learning the Dark Universe with Gravitational Waves

Project Information

Deledda

Grant agreement ID: 101065440

DOI

10.3030/101065440

Project closed

EC signature date 18 May 2022

Start date 1 September 2023

End date 31 August 2026

Funded under

• Marie Skłodowska-Curie Actions (MSCA)

Total cost

No data

EU contribution

€ 288 859,20

Coordinated by

UNIVERSITA DI PISA
Italy

CORDIS provides links to public deliverables and publications of HORIZON projects.

Links to deliverables and publications from FP7 projects, as well as links to some specific result types such as dataset and software, are dynamically retrieved from OpenAIRE .

Deliverables

Data Management Plan (1)

Data Management Plan

The Data Management Plan describes the data management life cycle for all data sets that will be collected, processed or generated by the action. It is a document describing what data will be collected, processed or generated and following what methodology and standards, whether and how this data will be shared and/or made open, and how it will be curated and preserved.

Publications

Peer reviewed articles (4)

Fixing the dynamical evolution of self-interacting vector fields

Author(s): Rubio, Marcelo; Lara, Guillermo; Bezares, Miguel; Crisostomi, Marco; Barausse, Enrico
Published in: Physical Review D, 2024, ISSN 2470-0029
Publisher: American Physical Society
DOI: 10.48550/ARXIV.2407.08774

Rapid Parameter Estimation for Pulsar-Timing-Array Datasets with Variational Inference and Normalizing Flows

Author(s): Michele Vallisneri; Marco Crisostomi; Aaron D. Johnson; Patrick M. Meyers
Published in: Physical Review Letters, 2025, ISSN 1079-7114
Publisher: American Physical Society
DOI: 10.48550/ARXIV.2405.08857

Domain-optimized machine-learning tool for gravitational-wave inference

Author(s): Javier Roulet, Marco Crisostomi, Lucy M. Thomas, Katerina Chatziioannou
Published in: Physical Review D, Issue 114, 2026, ISSN 2470-0010
Publisher: American Physical Society (APS)
DOI: 10.48550/ARXIV.2604.08897

Bayesian evidence estimation from posterior samples with normalizing flows

Author(s): Srinivasan, Rahul; Crisostomi, Marco; Trotta, Roberto; Barausse, Enrico; Breschi, Matteo
Published in: Physical Review D, 2024, ISSN 2470-0029
Publisher: American Physical Society
DOI: 10.48550/ARXIV.2404.12294