Updating note

note/main.tex

@@ -8,17 +8,17 @@
 % You could define the document class directly                                                                                                      
 %\documentclass[]{emulateapj}                                                                                                                       
 
-\input{macros}
+%\input{macros}
 
 \usepackage[outdir=./]{epstopdf}
 \usepackage{graphicx,verbatim}
 \usepackage{xspace}
+\usepackage{desc-tex/styles/lsstdesc_macros}
 
 \graphicspath{{./}{./figures/}}
 %\bibliographystyle{apj}
 \newcommand{\todo}[1]{\textcolor{magenta}{To do: #1}}
 \newcommand{\mrm}[1]{\mathrm{#1}}
-
 \newcommand{\augur}{{\tt Augur}\xspace}
 \newcommand{\CC}{C\nolinebreak\hspace{-.05em}\raisebox{.3ex}{\footnotesize +}\nolinebreak\hspace{-.10em}\raisebox{.3ex}{\footnotesize +}}
 
@@ -30,9 +30,10 @@
 \maketitlepre
 
 \begin{abstract}
-  This document describes the forecasting formalism underlying the {\tt Augur} code. This is used for determining modelling choices and constraining power of multi-probe combinations of LSST data.
-  Augur has two forecasting modalities: direct likelihood sampling or Fisher forecasting.
-  For the Fisher forecasting scenario, we describe the specific implementation of: expected uncertainties and ellise contours for pairs of parameters; expected biases in presence of unknown systematics; model choice citeria.
+  This document describes the forecasting formalism underlying the {\tt augur} code. This is used for determining modelling choices and constraining power of multi-probe combinations of LSST data.
+  {\tt augur} has two forecasting modalities: direct likelihood sampling or Fisher forecasting.
+  For the Fisher forecasting scenario, we describe the specific implementation of: expected uncertainties and ellipse contours for pairs of parameters; expected biases in presence of unknown systematics; model choice citeria.
+  For now, {\tt augur} only works for 3x2pt probes.
 \end{abstract}
 
 \maketitlepost
@@ -100,13 +101,14 @@ \subsection{Fisher Bias}
   \end{split}
   \end{align}
 
+
 \subsection{Model selection}
 One can apply model selection criteria in a forecasting phase in order to study an experiment's ability to carry out model selection tests.
 For instance, if we consider an experiment aimed at obtaining information on dark energy, we could use model selection techniques to predict whether or not,
 with our experiment, we will be able to distinguish, say, the $w$CDM from the $\Lambda$CDM model.
 The natural question that now arises regards the possible criteria to select models. Although there are quite a few, they are all expressed in terms of the likelihood,
 which is computationally demanding. Thus, we focus on the Bayes factor as it is possible to express it in terms of the Fisher matrix only. We would like to implement
-such expression in Augur as it is computationally inexpensive and can provide interesting information.\\
+such expression in {\tt augur} as it is computationally inexpensive and can provide interesting information.\\
 The Bayes factor is defined as the ratio of the evidences (marginzalized likelihoods) of the two models under consideration.
 In this way, although a complicated model will lead to a higher likelihood (or at least as high) with respect to a simpler/nested model, the evidence will favour
 the simpler model (provided that the fit is nearly as good), thanks to the smaller prior volume. 
@@ -115,12 +117,17 @@ \subsection{Model selection}
 on each parameter (encoded in the ranges $\Delta \theta_{1}$ and $\Delta \theta_{2}$, respectively). 
 \begin{equation}
   \langle B\rangle=\frac{\Delta \theta_{1} \Delta \theta_{2}}{2 \pi \sqrt{\operatorname{det} F^{-1}}} \mathrm{e}^{-(1 / 2) \sum_{\alpha \beta}\left(\theta_{\alpha}-\theta_{\alpha}^{*}\right) F_{\alpha \beta}\left(\theta_{\beta}-\theta_{\beta}^{*}\right)}
-  \end{equation}
+\end{equation}
+
+
 \section{Validation}
-We have written a Python code and prepared some examples of implementation of the above-mentioned equations. This code will allow
-us to benchmark Augur. 
 
-\input{acknowledgments}
+TBC
+
+
+%\input{acknowledgments}
+
+\input{desc-tex/ack/standard}
 
 \input{contributions}