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Publications funded by COST must be a direct result of work performed by Action Participants and must be co-authored by Action Participants representing at least 3 different Participating COST Full Members / COST Cooperating Members. Whenever possible, publications should be made available under Open Access licences. Publications resulting from work performed by COST Action Participants fall under two distinct categories that are funded differently:

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Talks and Posters

LIGO – Virgo meeting, Copernicus Conference Centre (CNK), Warsaw, Poland
Date: 2-5/09/2019

        • Antoni Ramos, Maite Mateu, Geraint Pratten, Héctor Estellés, Rafel Jaume, Cecilio García, Marta Colleoni, Sascha Husa.
          Numerical relativity waveform catalog, hybridization and parameter estimation of eccentric spinning black-hole binaries.
        • Cecilio García, Marta Colleoni, Gerain Pratten, Héctor Estellés, Sascha Husa, Antoni Ramos, Rafel Jaume, Maite Mateu.
          IMRPhenomXHM: Waveform model calibrated to multimode Hybrids with accelerated evaluation.
          Poster. LAAC Poster Prize:
        • Pep B. Covas, Alicia M. Sintes.
          New method to search for continuous gravitational waves from unknown neutron stars in binary systems.
        • Héctor Estellés, Antoni Ramos, Cecilio García, Sascha Husa, Leïla Haegel, Rafel Jaume.
          PhenomT: a time domain phenomenological model for CBC signals.

GR22 – Amaldi13 conference:

        • Agata Trovato.
          Machine Learning to exploit LIGO Scientific Collaboration – EGO & the Virgo Collaboration single-detector data taking periods.
        • Alessio Cirone.
          Newtonian noise cancellation with Deep Learning.
        • Filip Morawski.
          Deep Learning classification of the gravitational – wave signal candidates from the time-domain F-statistic search.
        • Alba Romero Rodríguez.
          A convoluted neural network implementation for the search for compact binary signals at Virgo.
        • Alberto Less.
          Machine-Learning Classification of Core-Collapse Supernovae.
        • Rodrigo Tenorio, Miquel Oliver, Alicia M. Sintes.
          Noise-Robust Strategies For Continuous Gravitational Wave Searches: Improvements On The Skyhough All-Sky Search.

1st Real Time Analysis Workshop:
Elena Cuoco.
Real Time Classifier for Gravitational Wave Signals.

Artificial Intelligence in Astronomy Workshop at ESO:
Filip Morawski
Deep learning classification of the gravitational-wave signal candidates from the time-domain F-statistic search.

Hammers & Nails 2019. Machine Learning Meets Astro & Particle Physics:
Elena Cuoco
Gravitational Waves and ML.

SpliTech Conference
Jonatan Lerga.
Denoising Accuracy of Adaptive ICI-Based Estimators With Regards to Sampling Rate.
Talk [+].

PhD Thesis

Miquel Oliver Almiñana, Universitat de les Illes Balears
July 31st, 2019
Gravitational wave data analysis for the advanced detector era.
Supervisors: Dr. Alicia M. Sintes Olives, Dr. Sascha Husa.

Scientific Articles

BinarySkyHough: A new method to search for continuous gravitational waves from unknown neutron stars in binary systems.
P. B. Covas, Alicia M. Sintes.
Physical Review D 99, 124019 (2019). arXiv:1904.04873. DOI: 10.1103/PhysRevD.99.124019

The Adaptive Transient Hough method for long-duration gravitational wave transients.
Miquel Oliver, David Keitel, Alicia M. Sintes.
Physical Review D 99, 104067 (2019). arXiv:1901.01820. DOI: 10.1103/PhysRevD.99.104067

Matched-filter study and energy budget suggest no detectable gravitational-wave ‘extended emission’ from GW170817.
Miquel Oliver, David Keitel, Andrew L. Miller, Hector Estelles, Alicia M. Sintes.
Monthly Notices of the Royal Astronomical Society, Volume 485, Issue 1 (2019). arXiv:1812.06724. DOI: 10.1093/mnras/stz439

Generalized application of the Viterbi algorithm to searches for continuous gravitational-wave signals.
Joe Bayley, Chris Messenger, Graham Woan.
Physical Review D 100, 023006 (2019). arXiv:1903.12614. DOI:

Deep-learning continuous gravitational waves.
Christoph Dreissigacker, Rahul Sharma, Chris Messenger, Ruining Zhao, Reinhard Prix.
Physical Review D 100, 044009 (2019). arXiv:1904.13291. DOI:

Machine-Learning Nonstationary Noise Out of Gravitational-Wave Detectors.
Gabriele Vajente, Yiwen Huang, Maximiliano Isi, Jenne C. Driggers, Jeffrey S. Kissel, Marek J. Szczepanczyk, Salvatore Vitale.
Physical Review D 101, 042003 (2020). arXiv:1911.09083. DOI: 10.1103/PhysRevD.101.042003

Scalable auto-encoders for gravitational waves detection from time series data.
Roberto Corizzo, Michelangelo Ceci, Eftim Zdravevski, Nathalie Japkowicz.
Expert Systems with Applications 151, 113378 (2020). DOI: 10.1016/j.eswa.2020.113378

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