Active Region Classification and Flare Forecasting

Informacje na temat projektu

ARCAFF

Identyfikator umowy o grant: 101082164

DOI

10.3030/101082164

Data podpisania przez KE 11 Października 2022

Data rozpoczęcia 1 Grudnia 2022

Data zakończenia 28 Lutego 2026

Finansowanie w ramach

• Digital, Industry and Space

Koszt całkowity

€ 604 111,25

Wkład UE

€ 604 111,25

Koordynowany przez

DUBLIN INSTITUTE FOR ADVANCED STUDIES
Ireland

CORDIS oferuje możliwość skorzystania z odnośników do publicznie dostępnych publikacji i rezultatów projektów realizowanych w ramach programów ramowych HORYZONT.

Odnośniki do rezultatów i publikacji związanych z poszczególnymi projektami 7PR, a także odnośniki do niektórych konkretnych kategorii wyników, takich jak zbiory danych i oprogramowanie, są dynamicznie pobierane z systemu OpenAIRE .

Rezultaty

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.

Publikacje

Peer reviewed articles (6)

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

Autorzy: Edoardo Legnaro, Sabrina Guastavino, Michele Piana, Anna Maria Massone
Opublikowane w: The Astrophysical Journal, Numer 981, 2025, ISSN 0004-637X
Wydawca: American Astronomical Society
DOI: 10.3847/1538-4357/ADB41A

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

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

Unbiased CLEAN for STIX in Solar Orbiter

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

Prediction of Solar Energetic Events Impacting Space Weather Conditions

Autorzy: 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
Opublikowane w: Advances in Space Research, 2024, ISSN 0273-1177
Wydawca: 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

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

Physically Motivated Deep Learning to Superresolve and Cross Calibrate Solar Magnetograms

Autorzy: 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
Opublikowane w: The Astrophysical Journal Supplement Series, 2024, ISSN 1538-4365
Wydawca: Institute of Physics Publishing
DOI: 10.3847/1538-4365/AD12C2

Other (1)

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

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