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PPT code
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\documentclass{beamer}
\usetheme{Singapore}
\usepackage[english]{babel}
\usepackage[utf8]{inputenc}
\usepackage[T1]{fontenc}
\usepackage{eso-pic}
\usepackage[linguistics]{forest}
\usepackage{nameref}
\makeatletter
\newcommand*{\currentname}{\@currentlabelname}
\makeatother
\usepackage{caption}
\usepackage{media9}
\usepackage[locale=UK,per=slash,decimalsymbol=.,loctolang={DE:ngerman,UK:english}]{siunitx}
\usepackage[backend=bibtex]{biblatex}
\usepackage{booktabs}
\usepackage{fancybox}
\bibliography{lib.bib}
\renewcommand*{\bibfont}{\tiny}
%\addtobeamertemplate{navigation symbols}{}{%
% \usebeamerfont{footline}%
% \usebeamercolor[fg]{footline}%
% \hspace{1em}%
% \insertframenumber/\inserttotalframenumber
%}
\setbeamersize{text margin left = 1em}
\setbeamersize{text margin right = 1em}
\setbeamertemplate{bibliography item}{}
\setbeamertemplate{bibliography entry title}{}
\setbeamertemplate{bibliography entry location}{}
\setbeamertemplate{bibliography entry note}{}
\setbeamertemplate{navigation symbols}{}
\newcommand{\intersec}{\begin{frame}\addtocounter{framenumber}{-1} \huge{\currentname}\end{frame}}
\graphicspath{{./img/}}
\begin{document}
\title{
Automated Source Classification with Random Forests
}
\subtitle{An Overview}
\author{Abhigna Revuru}
\date{10 July 2018}
\institute{Remeis Observatory \& IIT Kharagpur}
%\logo{\includegraphics[scale=0.1]{rflogo}}
\begin{frame}
\titlepage
\end{frame}
\section{Background}
\subsection{Intro}
\begin{frame}
\frametitle{What is this about?}
\begin{itemize}
\item Aim - Automate source classification for near-real time satellite data (eROSITA)
\item Input - clean satellite data from 130k sources (hardness ratios, multiwavelength data, spectral features, variability data...)
\item Output - source types (star, galaxy, black hole, AGN, binary...)
\end{itemize}
\end{frame}
\begin{frame}
\frametitle{eROSITA}
\centering
\underline{e}xtended \underline{RO}entgen \underline{S}urvey with an \underline{I}maging \underline{T}elescope \underline{A}rray
\newline
\newline
\begin{minipage}{0.4\textwidth}
\begin{itemize}
\item primary instrument aboard the Russian SRG mission
\item expected launch: Baikonur, March 2019
\item deep survey of x-ray sky in 0.5-10keV band
\end{itemize}
\end{minipage}\hfill
\begin{minipage}{0.6\textwidth}
\begin{figure}
\centering
\includegraphics[width=0.8\textwidth]{erosita.jpeg}
\caption*{eROSITA mirror modules}
\end{figure}
\end{minipage}
\end{frame}
\renewcommand{\arraystretch}{1.5}
\setlength{\tabcolsep}{18pt}
\begin{frame}
\frametitle{Technical Specifications}
\begin{center}
\begin{tabular}{ |c||c| }
\hline
Energy range & 0.2 - 10keV \\
\hline
Energy resolution & 138eV at 6keV \\
\hline
Focal length & 1.6m \\
\hline
Field of view & 61 arcsec \\
\hline
Effective area & 1400 sqcm at 1keV \\
\hline
Time resolution & 50ms \\
\hline
\end{tabular}
\end{center}
\end{frame}
\subsection{Source classes}
\begin{frame}
\frametitle{Source classes}
\begin{center}
\begin{forest}
[objects
[galaxies
[Seyfert
[S1, text=blue
]
[S2, text=blue
]
]
[BL Lac, text=blue
]
]
[ULX, text=blue
]
[star systems
[STAR, text=blue
]
[binary systems
[CV, text=blue
]
[HMXRB, text=blue
]
[LMXRB, text=blue
]
]
]
]
\end{forest}
\end{center}
\end{frame}
\begin{frame}
\frametitle{Galaxies}
\begin{minipage}{0.4\textwidth}
\begin{itemize}
\item luminous nuclei
\item high surface brightness
\item high ionisation emission line spectra
\end{itemize}
\underline{Seyfert I galaxies}
\begin{itemize}
\item Broad H lines
\item Narrow forbidden lines from H, He, O
\end{itemize}
\underline{Seyfert II galaxies}
\begin{itemize}
\item No broad emission lines
\item Strong absorption
\end{itemize}
\end{minipage}\hfill
\begin{minipage}{0.6\textwidth}
\begin{figure}
\centering
\includegraphics[width=0.8\textwidth]{seyfert.jpg}
\caption*\centering{NGC 1068, one of the first Seyfert galaxies classified}
\end{figure}
\end{minipage}
\end{frame}
\begin{frame}
\frametitle{BL Lac Objects}
\begin{minipage}{0.5\textwidth}
galaxies with AGNs, named after its prototype, BL Lacertae\\
\begin{itemize}
\item rapid and large-amplitude flux variability
\item relatively featureless spectra
\end{itemize}
\end{minipage}\hfill
\begin{minipage}{0.5\textwidth}
\begin{figure}
\centering
\includegraphics[width=0.6\textwidth]{bllac.jpeg}
\caption*\centering{H 0323+022, a BL Lac object, with visible host galaxy and close companions}
\end{figure}
\end{minipage}
\end{frame}
\begin{frame}
\frametitle{X-Ray Binaries}
\begin{center}
stellar \textbf{\textit{donor}} + compact, black hole/neutron star \textbf{\textit{accretor}}
\end{center}
\underline{High Mass X-ray Binaries} (HMXRB)
\begin{itemize}
\item donor: main sequence star/white swarf/red giant
\item mass transfer from donor Roche lobe to accretor
\end{itemize}
\hfill\break
\underline{Low Mass X-ray Binaries} (LMXRB)
\begin{itemize}
\item donor: massive star/blue supergiant
\item mass transfer via donor's stellar wind captured by accretor
\end{itemize}
\end{frame}
\begin{frame}
\frametitle{Cataclysmic Variable Stars}
\begin{center}
\textbf{\textit{accreting}} white dwarf + mass transferring \textbf{\textit{donor}} star
\end{center}
\begin{itemize}
\item binary stars
\item irregular increase in brightness by a large factor, then drops down to inactive state
\item easy to classify - rapid variability, luminous, peculiar emission lines
\end{itemize}
\end{frame}
\begin{frame}
\frametitle{Ultraluminous x-ray sources}
\begin{minipage}{0.4\textwidth}
\begin{itemize}
\item less luminous than AGNs
\item but more luminous than any other known stellar process
\item luminosity exceeds that of neutron stars and stellar black holes
\end{itemize}
\end{minipage}\hfill
\begin{minipage}{0.6\textwidth}
\begin{figure}
\centering
\includegraphics[width=0.7\textwidth]{ulx.jpg}
\caption*\centering{Chandra image of NGC 4485 and NGC 4490: two potential ULXs}
\end{figure}
\end{minipage}
\end{frame}
\section{Using machine learning}
\intersec
\subsection{Methods}
\begin{frame}
\frametitle{How to classify an unknown source?}
\begin{minipage}{0.45\textwidth}
\begin{center}
Traditional way
\end{center}
\begin{itemize}
\item crossmatch source positions with catalogues of other wavelengths
\item workflow: spectral fitting and intuitive classification rules
\item consumes human effort, time
\end{itemize}
\end{minipage}\hfill
\begin{minipage}{0.45\textwidth}
\begin{center}
With machine learning
\end{center}
\begin{itemize}
\item each 'feature' of the data is mapped by an unknown function to the source class
\item workflow: optimizing this unknown function for highest accuracy
\item reproducible, efficient, scalable
\end{itemize}
\end{minipage}
\end{frame}
\begin{frame}
\frametitle{Examples of machine learning}
1. Supervised
\begin{itemize}
\item me telling you the difference
\item you 'teach' the algorithm what conclusions it should come up with
\item eg: predicting world cup outcomes based on old football statistics
\end{itemize}
2. Unsupervised
\begin{itemize}
\item you figuring out the difference for yourself
\item you let the algorithm look for patterns in data by itself
\item eg: distinguishing between pictures of chairs, landscapes and unicorns
\end{itemize}
\end{frame}
\begin{frame}
\frametitle{What is this black magic?}
\begin{center}
Goal: to generalise beyond the training set
\end{center}
\begin{center}
\ \textbf{y = F(a,b,c,...)}
\end{center}
\begin{itemize}
\item \textbf{y} is a response variable
\item \textbf{a,b,c,...} are features of the data
\item What is \textbf{F}?\\
$\rightarrow$ don't know, don't care\\
$\rightarrow$ our job to find a blackbox that performs the best
\end{itemize}
\begin{figure}
\centering
\includegraphics[width=0.75\textwidth]{ml.png}
\end{figure}
\end{frame}
\begin{frame}
\frametitle{Blackbox example 1}
\centering
Survival on the Titanic - predictor/classifier\\
\begin{minipage}{0.3\textwidth}
\begin{figure}
\includegraphics[width=\textwidth]{titanic1.png}
\end{figure}
\end{minipage}\hfill
\begin{minipage}{0.33\textwidth}
\begin{figure}
\includegraphics[width=\textwidth]{titanic2.png}
\end{figure}
\end{minipage}\hfill
\begin{minipage}{0.36\textwidth}
\begin{figure}
\includegraphics[width=\textwidth]{titanic3.png}
\end{figure}
\end{minipage}
\centering
"decision tree"
\end{frame}
\begin{frame}
\frametitle{Blackbox example 2}
\centering
What if we use multiple instances of a decision tree?\\
$\Rightarrow$ It becomes a forest!
\begin{figure}
\includegraphics[width=0.85\textwidth]{rf.jpg}
\end{figure}
\end{frame}
\begin{frame}
\frametitle{Random forest algorithm}
\begin{itemize}
\item an \textit{ensemble} = complementary trees that contribute to a single effect
\item 'random' $\rightarrow$ each tree is a randomised sample/subset of the training set [\textit{bagging}]\\
$\Rightarrow$ Unique trees, so different classifications, votes are tallied at the end
\item multiple instances reduce noise and average out errors (overfitting)
\item Classifier parameters - number of trees, minimum split, split criteria, etc
\end{itemize}
\end{frame}
\section{Implementing random forests}
\intersec
\subsection{datasets}
\begin{frame}
\frametitle{Case in point: the EXTraS project}
\begin{minipage}{0.5\textwidth}
\begin{itemize}
\item Maintainer: ESA
\item Launch: December 1999
\item Focal length: $\SI{7.4}{\meter}$
\item Range: $\SI{0.15}{\kilo\electronvolt} - \SI{12}{\kilo\electronvolt}$
\item Resolution: $\SI{150}{\electronvolt}$
\end{itemize}
\end{minipage}\hfill
\begin{minipage}{0.5\textwidth}
\begin{figure}
\centering
\includegraphics[width=\textwidth]{xmm.pdf}
\caption*{XMM-Newton (Jansen et al., 2001)}
\end{figure}
\end{minipage}
\begin{center}
\underline{\textbf{E}xploring the \textbf{X}-ray and \textbf{Tra}nsient Variable \textbf{S}ky}\\
to investigate unexplored archival data from the cameras onboard XMM-Newton\\
\end{center}
\end{frame}
\begin{frame}
\frametitle{The training dataset}
\begin{itemize}
\item Requirement: must be representative of properties of various sources to ensure a faithful classification
\item \textbf{Farrell et al 2015} - one of the earliest attempts to use random forests
\item 7383 detections of 2911 sources over 8 source classes
\end{itemize}
\begin{figure}
\includegraphics[width=0.75\textwidth]{latvslong.jpeg}
\end{figure}
\end{frame}
\begin{frame}
\frametitle{Balancing the training data}
\begin{itemize}
\item Data set is heavily skewed towards galaxies
\item SMOTE algorithm - 'Synthetic minority over-sampling technique' from Chawla et al 2011
\item oversampling minority classes + undersampling majority class
\end{itemize}
\begin{minipage}{0.5\textwidth}
\begin{figure}
\includegraphics[width=\textwidth]{trainset.png}
\caption*{Farrell set}
\end{figure}
\end{minipage}\hfill
\begin{minipage}{0.5\textwidth}
\begin{figure}
\includegraphics[width=0.91\textwidth]{balancedtrainset.png}
\caption*{SMOTEd set}
\end{figure}
\end{minipage}
\end{frame}
\begin{frame}
\begin{figure}
\includegraphics[width=\textwidth]{latvslongsmoted.jpeg}
\caption*{29365 observations in the balanced training set with a coordinate bias}
\end{figure}
\end{frame}
\begin{frame}
\frametitle{Parameter optimization}
\begin{figure}
\includegraphics[width=\textwidth]{code.png}
\end{figure}
\begin{itemize}
\item Train on parameter \textbf{class}
\item Assesing importance of predictors
\item \textbf{ntree} - trees grown
\item \textbf{mtry} - variables randomly sampled as candidates at each split, close to usually sqrt(number of variables)
\item can optimise by hit/trial or with \textit{caret} package
\end{itemize}
\end{frame}
\begin{frame}
\frametitle{Feature selection}
\begin{center}
232 features available for every detection - which ones are important?
\end{center}
\begin{itemize}
\item Object coordinates
\item Hardness ratios
\item Spectral - parameters from 6 models (powerlaw, blackbody, ionised plasma, power law + instrinsically absorbed power law,..)
\item Multiwavelength indices
\item Timing - signal ffr, probability, power, exposure, count rates, fractional variability
\end{itemize}
\end{frame}
\section{Results}
\intersec
\subsection{results}
\begin{frame}
\frametitle{coordinates}
\begin{minipage}{0.4\textwidth}
\begin{itemize}
\item accuracy = 60\%
\item variable importance:
\begin{figure}
\includegraphics[width=0.7\textwidth]{coordImp.png}
\end{figure}
\end{itemize}
\end{minipage}\hfill
\begin{minipage}{0.6\textwidth}
\begin{figure}
\includegraphics[width=0.8\textwidth]{coordCF.png}
\end{figure}
\end{minipage}
\end{frame}
\begin{frame}
\frametitle{hardness ratios}
\begin{minipage}{0.4\textwidth}
\begin{itemize}
\item accuracy = 91.4\%
\item variable importance:
\begin{figure}
\includegraphics[width=0.7\textwidth]{hrImp.png}
\end{figure}
\end{itemize}
\end{minipage}\hfill
\begin{minipage}{0.6\textwidth}
\begin{figure}
\includegraphics[width=0.8\textwidth]{hrCF.png}
\end{figure}
\end{minipage}
\end{frame}
\begin{frame}
\frametitle{coordinates + HR}
\begin{minipage}{0.4\textwidth}
\begin{itemize}
\item accuracy = 92.5\%
\item variable importance:
\begin{figure}
\includegraphics[width=0.7\textwidth]{coordhrImp.png}
\end{figure}
\end{itemize}
\end{minipage}\hfill
\begin{minipage}{0.6\textwidth}