Active Region Classification and Flare Forecasting

Informations projet

ARCAFF

N° de convention de subvention: 101082164

DOI

10.3030/101082164

Date de signature de la CE 11 Octobre 2022

Date de début 1 Decembre 2022

Date de fin 28 Février 2026

Financé au titre de

• Digital, Industry and Space

Coût total

€ 604 111,25

Contribution de l’UE

€ 604 111,25

Coordonné par

DUBLIN INSTITUTE FOR ADVANCED STUDIES
Ireland

CORDIS fournit des liens vers les livrables publics et les publications des projets HORIZON.

Les liens vers les livrables et les publications des projets du 7e PC, ainsi que les liens vers certains types de résultats spécifiques tels que les jeux de données et les logiciels, sont récupérés dynamiquement sur OpenAIRE .

Livrables

Documents, reports (3)

Project Reporting Year 1

This deliverable relates to the regular periodic reports for year 1

Project Reporting Year 2

This deliverable relates to the regular periodic reports for year 2

Progress report of user engagement in development process 1

This deliverable will include a report on progress so far to engage operational users of ARCAFF outputs. This includes (but not limited to) interactions with Advisory Board members, community workshop results and surveys, results of presentations at conferences etc. Month 24 has been chosen to allow time to reflect before any last changes are required in year 3 of the project to finalise the forecast service.

Demonstrators, pilots, prototypes (1)

ARCAFF DL services v1

This deliverable will be a report and a set of executable services published in GitHub and integrated with the PITHIA-e-Science Centre. It will include the cloud-based implementation of these services, their preliminary user interfaces integrated into the e-Science Centre, and related learning and research material supporting their execution and utilisations.

Data sets, microdata, etc (3)

Point-in-time based Flare forecast ML Dataset (magnetogram and multimodal)

Software and DL datasets (3D images cubes and labels or outputs) for the point-in-time flare forecast objectivesboth magnetgram (O3) and multimodal (O4).

AR Localisation and Classification ML Datasets

Software and DL datasets (images and label or outputs) for the AR Classification (O1) and AR Localisation andClassification Objectives (O2).

Trained AR Localisation and Classification DNNs and software

Release of the training pipeline and model software as well as the trained model weights for AR Classification(O1) and AR Localisation and Classification Objectives (O2).

Websites, patent fillings, videos etc. (1)

Project Web Site

This deliverable will establish the ARCAFF website where all information about the project aims, consortiummembers, progress etc will be found, and later will display project outputs.

Data Management Plan (1)

ARCAFF Data Management Plan

Creation of a Data Management Plan for the consortium to follow when delivering project outputs.

Publications

Peer reviewed articles (6)

Deep Learning for Active Region Classification: A Systematic Study from Convolutional Neural Networks to Vision Transformers

Auteurs: Edoardo Legnaro, Sabrina Guastavino, Michele Piana, Anna Maria Massone
Publié dans: The Astrophysical Journal, Numéro 981, 2025, ISSN 0004-637X
Éditeur: American Astronomical Society
DOI: 10.3847/1538-4357/ADB41A

Physics-driven Machine Learning for the Prediction of Coronal Mass Ejections’ Travel Times

Auteurs: Sabrina Guastavino; Valentina Candiani; Alessandro Bemporad; Francesco Marchetti; Federico Benvenuto; Anna Maria Massone; Salvatore Mancuso; Roberto Susino; Daniele Telloni; Silvano Fineschi; Michele, Piana
Publié dans: The Astrophysical Journal, Numéro 954-2, 2023, ISSN 1538-4357
Éditeur: IOP Publishing
DOI: 10.3847/1538-4357/ace62d

Unbiased CLEAN for STIX in Solar Orbiter

Auteurs: Emma Perracchione; Fabiana Camattari; Anna Volpara; Paolo Massa; Anna Maria Massone; Michele Piana
Publié dans: The Astrophysical Journal Supplement Series, 2023, ISSN 1538-4365
Éditeur: IOP
DOI: 10.48550/arxiv.2307.09991

Prediction of Solar Energetic Events Impacting Space Weather Conditions

Auteurs: Manolis K. Georgoulis, Stephanie L. Yardley, Jordan A. Guerra, Sophie A. Murray, Azim Ahmadzadeh, Anastasios Anastasiadis, Rafal Angryk, Berkay Aydin, Dipankar Banerjee, Graham Barnes, Alessandro Bemporad, Federico Benvenuto, D. Shaun Bloomfield, Monica B
Publié dans: Advances in Space Research, 2024, ISSN 0273-1177
Éditeur: Elsevier
DOI: 10.1016/j.asr.2024.02.030

Forecasting Geoffective Events from Solar Wind Data and Evaluating the Most Predictive Features through Machine Learning Approaches

Auteurs: Sabrina Guastavino, Katsiaryna Bahamazava, Emma Perracchione, Fabiana Camattari, Gianluca Audone, Daniele Telloni, Roberto Susino, Gianalfredo Nicolini, Silvano Fineschi, Michele Piana, Anna Maria Massone
Publié dans: The Astrophysical Journal, Numéro 971, 2024, ISSN 0004-637X
Éditeur: American Astronomical Society
DOI: 10.3847/1538-4357/AD5B57

Physically Motivated Deep Learning to Superresolve and Cross Calibrate Solar Magnetograms

Auteurs: Andrés Muñoz-Jaramillo, Anna Jungbluth, Xavier Gitiaux, Paul J. Wright, Carl Shneider, Shane A. Maloney, Atılım Güneş Baydin, Yarin Gal, Michel Deudon, and Freddie Kalaitzis
Publié dans: The Astrophysical Journal Supplement Series, 2024, ISSN 1538-4365
Éditeur: Institute of Physics Publishing
DOI: 10.3847/1538-4365/AD12C2

Other (1)

Snakes on a spaceship—an overview of python in space physics

Auteurs: Burrell, A. G., Coxon, J., Aye, K.-M., Lamarche, L., Murray, S. A., eds.
Publié dans: Frontiers in Astronomy and Space Science, 2023, ISSN 1664-8714
Éditeur: Lausanne: Frontiers Media SA
DOI: 10.3389/978-2-8325-2959-1