\documentclass[10pt]{article}

\usepackage{fullpage}
\usepackage{setspace}
\usepackage{parskip}
\usepackage{titlesec}
\usepackage[section]{placeins}
\usepackage{xcolor}
\usepackage{breakcites}
\usepackage{lineno}
\usepackage{hyphenat}





\PassOptionsToPackage{hyphens}{url}
\usepackage[colorlinks = true,
            linkcolor = blue,
            urlcolor  = blue,
            citecolor = blue,
            anchorcolor = blue]{hyperref}
\usepackage{etoolbox}
\makeatletter
\patchcmd\@combinedblfloats{\box\@outputbox}{\unvbox\@outputbox}{}{%
  \errmessage{\noexpand\@combinedblfloats could not be patched}%
}%
\makeatother


\usepackage{natbib}




\renewenvironment{abstract}
  {{\bfseries\noindent{\abstractname}\par\nobreak}\footnotesize}
  {\bigskip}

\titlespacing{\section}{0pt}{*3}{*1}
\titlespacing{\subsection}{0pt}{*2}{*0.5}
\titlespacing{\subsubsection}{0pt}{*1.5}{0pt}


\usepackage{authblk}


\usepackage{graphicx}
\usepackage[space]{grffile}
\usepackage{latexsym}
\usepackage{textcomp}
\usepackage{longtable}
\usepackage{tabulary}
\usepackage{booktabs,array,multirow}
\usepackage{amsfonts,amsmath,amssymb}
\providecommand\citet{\cite}
\providecommand\citep{\cite}
\providecommand\citealt{\cite}
% You can conditionalize code for latexml or normal latex using this.
\newif\iflatexml\latexmlfalse
\providecommand{\tightlist}{\setlength{\itemsep}{0pt}\setlength{\parskip}{0pt}}%

\AtBeginDocument{\DeclareGraphicsExtensions{.pdf,.PDF,.eps,.EPS,.png,.PNG,.tif,.TIF,.jpg,.JPG,.jpeg,.JPEG}}

\usepackage[utf8]{inputenc}
\usepackage[ngerman,english]{babel}



\usepackage{float}






% Edit this header.tex file to include frontmatter definitions and global macros

% Add here any LaTeX packages you would like to load in all document blocks
% \usepackage{xspace}

% Add here any LaTeX macros you would like to load in all document blocks
% \def\example{This is an example macro.}

% -----

\iflatexml
% Add here any LaTeXML-specific commands

% -----

\else
% Add here any export style-specific LaTeX commands. These will only be loaded upon document export.
% \paperfield{Subject domain of my document}
% \keywords{keyword1, keyword2}
% \corraddress{Author One PhD, Department, Institution, City, State or Province, Postal Code, Country}
% \fundinginfo{Funder One, Funder One Department, Grant/Award Number: 123456.}
\fi


\begin{document}

\title{Limits of solar flare forecasting models and new deep learning approach}



\author[1]{Grégoire Francisco}%
\author[2]{Michele Berretti}%
\author[3]{Simone Chierichini}%
\author[1]{Ronish Mugatwala}%
\author[4]{João Manuel Fernandes}%
\author[4]{Teresa Barata}%
\author[5]{Dario Del Moro}%
\affil[1]{University of Rome Tor Vergata}%
\affil[2]{University of Trento}%
\affil[3]{University of Sheffield}%
\affil[4]{University of Coimbra}%
\affil[5]{University of Rome}%


\vspace{-1em}



  
  \date{May 27, 2024}


\begingroup
\let\center\flushleft
\let\endcenter\endflushleft
\maketitle
\endgroup





\selectlanguage{english}
\begin{abstract}
Reliable forecasting models are necessary to mitigate the risks posed by
solar flares to human technology. This study introduces a novel deep
learning forecasting approach while emphasizing the need for performance
evaluation methods tailored to better highlight current models'
limitations. In particular, we show that models reaching
state-of-the-art performances with traditional metrics have similar
explanatory power to no-skill persistence models and notably struggle to
forecast change in activity significantly better than random guesses. We
also discuss other shortcomings~ in traditional evaluation metric like
the True Skill Statistic (TSS) that we prove for the first time to be
mathematically dependent to the class balance. We introduce
Patch-Distributed-CNNs (P-CNN), which allow to perform full-disk
forecasts while providing event probabilities in solar sub-regions and
position predictions. This new framework offers similar information to
Active-Region-based forecasting models while bypassing the problem of
unrecorded and misattributed flares that are detrimental to machine
learning training. As a result, the model also operates independently of
prior feature extraction and AR detection, thus offering promising
operational utility with minimal external dependencies. Finally, a
method is proposed for constructing balanced and independent
Cross-Validation folds for full-disk models. Models combining SDO/AIA
EUV images as inputs show improved performances compared to employing
SDO/HMI photospheric magnetograms, with a TSS of 0.74 for the C+ model
and 0.62 for the M+ model.%
\end{abstract}%



\sloppy




\selectlanguage{english}
\FloatBarrier
\end{document}

