Active Region Classification and Flare Forecasting

Información del proyecto

ARCAFF

Identificador del acuerdo de subvención: 101082164

DOI

10.3030/101082164

Fecha de la firma de la CE 11 Octubre 2022

Fecha de inicio 1 Diciembre 2022

Fecha de finalización 28 Febrero 2026

Financiado con arreglo a

• Digital, Industry and Space

Coste total

€ 604 111,25

Aportación de la UE

€ 604 111,25

Coordinado por

DUBLIN INSTITUTE FOR ADVANCED STUDIES
Ireland

CORDIS proporciona enlaces a los documentos públicos y las publicaciones de los proyectos de los programas marco HORIZONTE.

Los enlaces a los documentos y las publicaciones de los proyectos del Séptimo Programa Marco, así como los enlaces a algunos tipos de resultados específicos, como conjuntos de datos y «software», se obtienen dinámicamente de OpenAIRE .

Resultado final

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.

Publicaciones

Peer reviewed articles (6)

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

Autores: Edoardo Legnaro, Sabrina Guastavino, Michele Piana, Anna Maria Massone
Publicado en: The Astrophysical Journal, Edición 981, 2025, ISSN 0004-637X
Editor: American Astronomical Society
DOI: 10.3847/1538-4357/ADB41A

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

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

Unbiased CLEAN for STIX in Solar Orbiter

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

Prediction of Solar Energetic Events Impacting Space Weather Conditions

Autores: 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
Publicado en: Advances in Space Research, 2024, ISSN 0273-1177
Editor: 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

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

Physically Motivated Deep Learning to Superresolve and Cross Calibrate Solar Magnetograms

Autores: 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
Publicado en: The Astrophysical Journal Supplement Series, 2024, ISSN 1538-4365
Editor: Institute of Physics Publishing
DOI: 10.3847/1538-4365/AD12C2

Other (1)

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

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