Active Region Classification and Flare Forecasting

Informazioni relative al progetto

ARCAFF

ID dell’accordo di sovvenzione: 101082164

DOI

10.3030/101082164

Data della firma CE 11 Ottobre 2022

Data di avvio 1 Dicembre 2022

Data di completamento 28 Febbraio 2026

Finanziato da

• Digital, Industry and Space

Costo totale

€ 604 111,25

Contributo UE

€ 604 111,25

Coordinato da

DUBLIN INSTITUTE FOR ADVANCED STUDIES
Ireland

CORDIS fornisce collegamenti ai risultati finali pubblici e alle pubblicazioni dei progetti ORIZZONTE.

I link ai risultati e alle pubblicazioni dei progetti del 7° PQ, così come i link ad alcuni tipi di risultati specifici come dataset e software, sono recuperati dinamicamente da .OpenAIRE .

Risultati finali

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.

Pubblicazioni

Peer reviewed articles (6)

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

Autori: Edoardo Legnaro, Sabrina Guastavino, Michele Piana, Anna Maria Massone
Pubblicato in: The Astrophysical Journal, Numero 981, 2025, ISSN 0004-637X
Editore: American Astronomical Society
DOI: 10.3847/1538-4357/ADB41A

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

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

Unbiased CLEAN for STIX in Solar Orbiter

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

Prediction of Solar Energetic Events Impacting Space Weather Conditions

Autori: 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
Pubblicato in: Advances in Space Research, 2024, ISSN 0273-1177
Editore: 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

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

Physically Motivated Deep Learning to Superresolve and Cross Calibrate Solar Magnetograms

Autori: 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
Pubblicato in: The Astrophysical Journal Supplement Series, 2024, ISSN 1538-4365
Editore: Institute of Physics Publishing
DOI: 10.3847/1538-4365/AD12C2

Other (1)

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

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