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OBJECTIVES AND DELIVERABLES

DELIVERABLE 1 (M6)

Prepare a plan for dissemination and outreach activities for the entire lifetime of the Action, with regular undates each 12 months.

DELIVERABLE 2 (M12)

Prepare a plan and its regular update for the involvement of external stakeholders interestes in the results of the Action .

DELIVERABLE 3 (M15)

Setup a github repository with python code example and documentation, developed in the WGs, using Machine Learning solutions.

One of the goals of the Action is to uncover the most suitable Machine Learning techniques for detecting and analysing gravitational waves and for managing detector noise and seismic disturbances. While we work on different research directions to achieve this, we collect both python code we used and experimented with as well as multimedia documentation on the Machine Learning techniques of interest. This will contribute to the dissemination of our concrete Machine Learning findings to the scientific community at large and will provide a tried-and-tested collection of tools for our own future collaborations.

The collection of tutorials held at the 2nd Training School in Malta, March 2020 is available temporarily: https://github.com/zerafachris/g2net_2nd_training_school_malta_mar_2020. We will integrate this collection in the parent repository here: https://gitlab.com/g2net. While at milestone M15 we will have a working and complete to-date repository, we will continue to update this throughout the lifetime of the Action.

DELIVERABLE 4 (M18)

Report on the monitoring of the seismic noise for GW detector and on its impact and impact on Newtonian Noise.

The analysis of low-frequency gravitational waves (GWs) requires multidisciplinary research in the fields of seismic sensing, signal processing, robotics, machine learning and mathematical modeling

So far, we have:

(1) Initiated the interdisciplinary community and identified a common research ground.

(2) Drafted a general framework for multisensory fusion of mobile spatially distributed sensors for low frequency geographic dynamical signals which goes beyond the state of the art.

(3) Performed initial tests on off-the shelf irobot create 2 robot which have confirmed the viability of the approach and suggested the development of ad-hoc platforms (the seismic sensors work well on a create 2 in the bandwidth 1-10 hz).

(4) Triggered a cooperation between Heron Robots (team led by F. Bonsignorio) and Astrocent (team led by T. Bulik) that is developing a physical ad hoc platform for seismic noise sensing. It is worth notice that this cooperation was triggered by the G2 meeting in Rijeka. The level of advancement can be estimated at 80% at the moment of the submission of this report. The platform will be made available to G2net and Virgo members in the appropriate forms.

(5) A master thesis summarizing the state of the art has in parallel be completed: https://www.ge.infn.it/~chinca/INFN/Pulze_thesis_complete.pdf

DELIVERABLE 5 (M24)

Reports on possible HPC splution based on Deep Learning strategy for Gravitational Wave glitches classification.

Some selected publications from the ones reported by our G2net Action, https://www.g2net.eu/publications-and-talks, address this deliverable:

(1) 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

(2) Core-Collapse supernova gravitational-wave search and deep learning classification.
Alberto Iess, Elena Cuoco, Filip Morawski, Jade Powell.
Machine Learning: Science and Technology 1, 025014 (2020). arxiv:2001.00279 DOI: 10.1088/2632-2153/ab7d31

(3) Predicting the Properties of Black-Hole Merger Remnants with Deep Neural Networks.
Haegel, S. Husa.
Classical and Quantum Gravity 37, 13 (2020). arXiv:1911.01496. DOI: 10.1088/1361-6382/ab905c

DELIVERABLE 6 (M24)

Report on suitable ML pattern-recognition techniques for identification of non-stationary spectral lines and non-stationary noise sources.

Some selected publications address this deliverable:

(1) 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

(2) Application of dictionary learning to denoise LIGO’s blip noise transients.
Alejandro Torres-Forné, Elena Cuoco, José A. Font, Antonio Marquina.
Physical Review D 102, 023011 (2020). arXiv:2002.11668. DOI: 10.1103/PhysRevD.102.023011

DELIVERABLE 7 (M24)

Report on Design study for application for robots to adaptively monitor seismic noise around GW detectors.

Not delivered yet.

We have drafted an operational roadmap identifying the actions to be performed and the technical/scientific gaps to be overcome to achieve the objective of significant newtonian noise suppression and in general of low-frequency noise characterization, drafted a general framework for multisensory fusion of mobile spatially distributed sensors for low frequency geographic dynamical signals.

Performed initial tests on off-the shelf irobot create 2 robots which have confirmed the viability of the approach and suggested the development of ad-hoc platforms (the seismic sensors work well on a create 2 in the bandwidth 1-10 hz). The development of the prototype ad-hoc platform is ongoing, estimated advancement is 80%.

A preprint and the D7 report will be made available by April 2021.

DELIVERABLE 8 (M30)

Report on suitable machine learning techniques and solution for the search of Gravitational Wave signals from pulsars.

Some selected publications address this deliverable:

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

(2) 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

(3) 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: 10.1103/PhysRevD.100.023006

(4) 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: 10.1103/PhysRevD.100.044009

(5) First all-sky search for continuous gravitational-wave signals from unknown neutron stars in binary systems using Advanced LIGO data.
B. Covas, Alicia M. Sintes.
Physical Review Letters 124, 191102 (2020). arXiv:2001.08411. DOI: 10.1103/PhysRevLett.124.191102

DELIVERABLE 9 (M30)

Report on solution to be adopted to address the impact on Newtonian Noise in GW data records.

Not delivered yet.

A preprint summarising the results already achieved and D9 report will be made available by April 2021.

DELIVERABLE 10 (M30)

Report on the application of ML methods in the control systems for GW detectors.

Not delivered yet.

In WG3 we started addressing this deliverable with a study on non-linear substraction carried out at the GEO600 GW Observatory. The method relies on Bayesian optimization and periodic system identification to carry out the noise substraction.

DELIVERABLE 11 (M36)

Report on new ML method application in seismological problems for GW detection and related experiments.

Some selected publications address this deliverable:

(1) Rapid Prediction of Earthquake Ground Shaking Intensity Using Raw Waveform Data and a Convolutional Neural Network.
https://paperswithcode.com/paper/rapid-prediction-of-earthquake-ground-shaking

(2) A Seismological Study of the Sos Enattos Area—the Sardinia Candidate Site for the Einstein Telescope.
https://doi.org/10.1785/0220200186

JOIN THE ACTION

The inclusion of further partners from currently participating COST Countries, or other countries within or outside the COST Network, is welcome and strongly encouraged during the entire duration of the Action.