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Added structure

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%!TEX root=thesis
%PDFLATEX
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\node[block ] (USR) at ( 1, 3) {\tt user};
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\node[block ] (INT) at (-1, 4) {\tt integrands};
\node[block ] (VEG) at ( 1, 4) {\tt vegas};
\node[block ] (XS) at (-1, 5) {\tt mcmule};
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%!TEX root=manual
\section{Structure of \mcmule{}}
\label{sec:structure}
\mcmule{} is written in Fortran 95 with helper and analysis tools
written in Mathematica and {\tt python2.7}. The code is written with
two kinds of applications in mind. First, several processes are
implemented, some at NLO, some at NNLO. For these the user can define
an arbitrary (infrared safe), fully differential observable and
compute cross sections and distributions. Second, the program is set
up such that additional processes can be implemented by supplying the
relevant matrix elements.
To just run \mcmule{} only a Fortran compiler such as {\tt gfortran}
is needed. The main executable can be compiled by running
\begin{lstlisting}[language=bash]
$ ./configure
$ make mcmule
\end{lstlisting}
When started, {\tt mcmule} reads options from {\tt stdin} as specified
in Table~\ref{tab:mcmuleinput} (cf. Section~\ref{sec:example}).
The structure of the code and the relation between the most important
Fortran modules is depicted in Figure~\ref{fig:structure}. A solid
arrow indicates ``using'' the full module, whereas a dotted arrow is
indicative of partial use. In what follows we give a brief description
of the various modules and mention some variables that play a
prominent role in the interplay between the modules.
\begin{figure}
\centering
\input{figures/tikz/fig:structure.tex}
\caption{The structure of \mcmule{}.}
\label{fig:structure}
\end{figure}
\begin{description}
\item[{\tt global\_def}:]
This module simply provides some parameters such as fermion masses
that are needed throughout the code. It also defines {\tt prec} as
a generic type for the precision used. Currently, this simply
corresponds to double precision.
\item[{\tt functions}:]
This module is a library of basic functions that are needed at
various points in the code. This includes dot products, eikonal
factors, the integrated eikonal, and an interface for scalar
integral functions among others.
\item[{\tt collier}:]
This is an external module~\cite{Denner:2016kdg}. It will be
linked to \mcmule{} during compilation and provides the numerical
evaluations of the scalar, and in rare cases tensor, integral
functions in {\tt functions}.
\item[{\tt phase\_space}:]
The routines for generating phase-space points and their weights
are collected in this module. Phase-space routines ending with
{\tt FKS} are prepared for the \ac{FKS} subtraction procedure
with a single unresolved photon. In the weight of such routines a
factor $\xi_1$ is omitted to allow the implementation of the
distributions in the \ac{FKS} method. This corresponds to a global
variable {\tt xiout1}. This factor has to be included in the
integrand of the module {\tt{integrands}}. Also the variable {\tt
ksoft1} is provided that corresponds to the photon momentum
without the (vanishing) energy factor $\xi_1$. Routines ending
with {\tt FKSS} are routines with two unresolved photons.
Correspondingly a factor $\xi_1\,\xi_2$ is missing in the weight
and {\tt xiout1} and {\tt xiout2}, as well as {\tt ksoft1} and
{\tt ksoft2} are provided. To ensure numerical stability it is
often required to tune the phase-space routine to a particular
kinematic situation.
\item[{\tt \{pg\}\_mat\_el}]:
Matrix elements are grouped into \term{process groups} such as
muon decay ({\tt mudec}) or $\mu$-$e$ and $\mu$-$p$ scattering
({\tt mue}). Each process group contains a {\tt mat\_el} module
that provides all matrix elements for its group. Simple matrix
elements are coded directly in this module. More complicated
results are imported from sub-module not shown in
Figure~\ref{fig:structure}. A matrix element starting with {\tt P}
contains a polarised initial state. A matrix element ending in
{\tt av} is averaged over a neutrino pair in the final state.
\item[{\tt \{pg\}}:]
In this module the soft limits of all applicable matrix elements
of a process group are provided to allow for the soft subtractions
required in the \ac{FKS} scheme. These limits are simply the
eikonal factor evaluated with {\tt ksoft} from {\tt phase\_space}
times the reduced matrix element, provided through {\tt mat\_el}.
This module also functions as the interface of the process group,
exposing all necessary functions that are imported by
\item[{\tt mat\_el},] which collects all matrix elements as well
as their particle labelling or \aterm{particle
identification}{PID}.
\item[{\tt user}:]
For a user of the code who wants to run for an already implemented
process, this is the only relevant module. At the beginning of
the module, the user has to specify the number of quantities to be
computed, {\tt nr\_q}, the number of bins in the histogram, {\tt
nr\_bins}, as well as their lower and upper boundaries, {\tt
min\_val} and {\tt max\_val}. The last three quantities are arrays
of length {\tt nr\_q}. The quantities themselves, i.e. the
measurement function is to be defined in {\tt quant} by the user
in terms of the momenta of the particles. Cuts can be applied by
setting the logical variable {\tt pass\_cut} to
false\footnote{Technically, {\tt pass\_cut} is a list of length
{\tt nr\_q}, allowing to decide whether to cut for each histogram
separately.}. Some auxiliary functions
like (pseudo)rapidity, transverse momentum etc. are predefined in
{\tt functions}. Each quantity has to be given a name through the
array {\tt names}.
Further, {\tt user} contains a subroutine called {\tt userinit}.
This allows the user to read additional input at runtime, for
example which of multiple cuts should be calculated. It also
allows the user to print some information on the configuration
implemented. Needless to say that it is good idea to do this for
documentation purposes.
\item[{\tt vegas}:]
As the name suggests this module contains the adaptive Monte Carlo
routine {\tt vegas}~\cite{Lepage:1980jk}. The binning routine
{\tt bin\_it} is also in this module, hence the need for the
binning metadata, i.e. the number of bins and histograms ({\tt
nr\_bins} and {\tt nr\_q}, respectively) as well as their bounds
({\tt min\_val} and {\tt max\_val}) and names, from {\tt user}.
\item[{\tt integrands}:]
In this module the functions that are to be integrated by {\tt
vegas} are coded. There are three types of integrands:
non-subtracted, single subtracted, and double subtracted
integrands, corresponding to, for example, the three parts
of the ${\rm FKS}^2$ scheme~\cite{Engel:2019nfw,Ulrich:2020phd}.
The matrix elements to be evaluated and the phase-space routines
used are set using function pointers through a subroutine {\tt
init\_piece}. The factors $\xi_i$ that were omitted in the
phase-space weight have to be included here for the single and
double subtracted integrands.
\item[{\tt mcmule}:]
This is the main program, but actually does little else than read
the inputs and call {\tt vegas} with a function provided by {\tt
integrands}.
\item[{\tt test}:]
For developing purposes, a separate main program exists that is
used to validate the code after each change. Reference values for
matrix elements and results of short integration are stored here
and compared against.
\end{description}
The library of matrix elements (indicated by {\tt mat\_el\_lib})
deserves a few comments. As matrix elements quickly become very large,
we store them separately from the main code. This makes it also easy
to extend the program by minimising the code that needs to be changed.
We group matrix elements into process group, \term{generic processes},
and \term{generic pieces} as shown in Appendix~\ref{sec:pieces}. In
some cases a generic piece is split into various partitions (cf.
Section~\ref{sec:ps} for details on why that is important).
When running {\tt mcmule}, the code generates a statefile from which
the full state of the integrator can be reconstructed should the
integration be interrupted (cf. Section~\ref{sec:vegasff} for
details). This makes the statefile ideal to also store results in a
compact format. To analyse these results, we provide a python tool
{\tt pymule}, additionally to the main code for \mcmule{}. {\tt
pymule} uses {\tt numpy}~\cite{Walt:2011np} for data storage and {\tt
matplotlib} for plotting~\cite{Hunter:2007mp}. {\tt pymule} works with
any python interpreter but {\tt IPython}~\cite{Perez:2007ip} is
recommended. We will encounter {\tt pymule} in
Section~\ref{sec:analyse} when we discuss how to use it to analyse
results. A full list of functions provided can be found in
Appendix~\ref{sec:pymule}.
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