Zobrazeno 1 - 10
of 67
pro vyhledávání: '"Aydin, Berkay"'
In this paper, we propose a novel loss function aimed at optimizing the binary flare prediction problem by embedding the intrinsic ordinal flare characteristics into the binary cross-entropy (BCE) loss function. This modification is intended to provi
Externí odkaz:
http://arxiv.org/abs/2408.11768
Solar energetic particle (SEP) events are one of the most crucial aspects of space weather that require continuous monitoring and forecasting using robust methods. We demonstrate a proof of concept of using a data-driven supervised classification fra
Externí odkaz:
http://arxiv.org/abs/2408.05590
In this paper, we introduce a novel methodology for leveraging shape-based characteristics of magnetograms of active region (AR) patches and provide a novel capability for predicting solar flares covering the entirety of the solar disk (AR patches sp
Externí odkaz:
http://arxiv.org/abs/2406.11054
Solar energetic particle (SEP) events are one of the most crucial aspects of space weather that require continuous monitoring and forecasting. Their prediction depends on various factors including source eruptions. In the present work, we use the Geo
Externí odkaz:
http://arxiv.org/abs/2403.17418
Autor:
Ji, Anli, Aydin, Berkay
Over the past few decades, many applications of physics-based simulations and data-driven techniques (including machine learning and deep learning) have emerged to analyze and predict solar flares. These approaches are pivotal in understanding the dy
Externí odkaz:
http://arxiv.org/abs/2402.03474
This study aims to evaluate the performance of deep learning models in predicting $\geq$M-class solar flares with a prediction window of 24 hours, using hourly sampled full-disk line-of-sight (LoS) magnetogram images, particularly focusing on the oft
Externí odkaz:
http://arxiv.org/abs/2309.14483
Solar flare prediction is a central problem in space weather forecasting and recent developments in machine learning and deep learning accelerated the adoption of complex models for data-driven solar flare forecasting. In this work, we developed an a
Externí odkaz:
http://arxiv.org/abs/2309.04558
This study progresses solar flare prediction research by presenting a full-disk deep-learning model to forecast $\geq$M-class solar flares and evaluating its efficacy on both central (within $\pm$70$^\circ$) and near-limb (beyond $\pm$70$^\circ$) eve
Externí odkaz:
http://arxiv.org/abs/2308.15712
This paper presents a post hoc analysis of a deep learning-based full-disk solar flare prediction model. We used hourly full-disk line-of-sight magnetogram images and selected binary prediction mode to predict the occurrence of $\geq$M1.0-class flare
Externí odkaz:
http://arxiv.org/abs/2308.02682
This paper contributes to the growing body of research on deep learning methods for solar flare prediction, primarily focusing on highly overlooked near-limb flares and utilizing the attribution methods to provide a post hoc qualitative explanation o
Externí odkaz:
http://arxiv.org/abs/2307.15878