building html

docs/sphinx/build/html/_sources/datasets.rst

@@ -5,6 +5,26 @@
 
 Here is the list of the datasets we implemented in SIMUnet to fit SMEFT coefficients. For each dataset, we indicate the experiment, the centre of mass energy :math:`\sqrt{s}`, the final-state channel, the observable(s) used in the fit, the integrated luminosity :math:`\mathcal{L}` in inverse femtobarns, and the number of data points :math:`n_{\rm dat}`, together with the corresponding publication reference. The datasets are organised by physical sectors, namely top, Drell-Yan, W helicity, EW precision observables, Higgs and Diboson.
 
+The user can easily include any of the following datasets to perform a fit with :math:`\text{SIMUnet}`. This 
+is done by adding the corresponding ``Dataset name`` with its corresponding configuration keys to the ``dataset_input`` in the runcard,
+as shown in the example below:
+
+.. code-block:: yaml
+
+    dataset_inputs:
+    - {dataset: NMC, frac: 0.75}
+    - {dataset: ATLASTTBARTOT7TEV, cfac: [QCD], simu_fac: "EFT_NLO"}
+    - {dataset: CMS_SINGLETOPW_8TEV_TOTAL, simu_fac: "EFT_NLO", use_fixed_predictions: True}
+
+The dataset ``NMC`` is a NNPDF dataset, and it is included as it would be on an NNPDF4.0 runcard.
+:math:`\text{SIMUnet}` recognises it as a standard dataset with no need for SMEFT modifications.
+
+In contrast, the dataset ``ATLASTTBARTOT7TEV`` includes the additional key ``simu_fac``,
+which instructs :math:`\text{SIMUnet}` to calculate theory predictions for this dataset using SMEFT NLO K-factor, as it is clear from its ``EFT_NLO`` value.
+
+The third dataset, ``CMS_SINGLETOPW_8TEV_TOTAL``, includes two keys: ``simu_fac``, which has already been discussed, and ``use_fixed_predictions``. The ``use_fixed_predictions`` key, when set to True,
+effectively removes the PDF dependence of the dataset. This approach is appropriate for observables that are either independent of the PDFs (like EWPO or W-helicities in top decays, for example) or are only weakly influenced by them,
+and therefore a full computation of FK-tables would be too computationally taxing.
 
 ================================
 Top sector

docs/sphinx/build/html/_sources/tutorials/contaminated_fit.rst

@@ -341,4 +341,4 @@ The output of the fit is stored in the ``runcard_folder``. It is identical to a
 Once the fit is complete, the next steps involve uploading the results. This is particularly useful
 if, for example, you ran the fit on a cluster and want to make it avaiable to collaborators or download it
 from a different machine. You can upload the fit by using ``vp-upload runcard_folder`` and then fetch it
-with ``vp-get fit fit_name``.
+with ``vp-get fit fit_name``. Note that, to upload the fit, appropriate credentials are required.

docs/sphinx/build/html/_sources/tutorials/simu_closure_test.rst

@@ -3,7 +3,18 @@
 SIMUnet closure test
 =====================
 
-A SIMUnet closure test corresponds to running a BSM contaminated PDF fit and a simultaneous PDF-EFT fit at the same time. 
+Closure tests serve as a mechanism to assess the reliability of fits of PDFs and physical parameters in general, and the robustness of the methodologies involved.
+
+The :math:`\text{SIMUnet}` code extends the capacity of the NNPDF closure test to run contaminated fits and closure tests probing the fit quality of both PDFs and
+physical parameters.
+
+:math:`\text{SIMUnet}` can produce BSM contaminated fits by injecting New Physics into the generated pseudodata 
+while the fit is done assuming the Standard Model. With this functionality, any user
+can test the robustness of any New Physics scenario against being fitted away in a PDF fit.
+In this context, :math:`\text{SIMUnet}` can perform closure tests for both PDFs and physical parameters, giving surety of the reliability of the :math:`\text{SIMUnet}`
+methodology.
+
+A :math:`\text{SIMUnet}` closure test corresponds to running a BSM contaminated PDF fit and a simultaneous PDF-EFT fit at the same time. 
 For details about each process see each tutorial. Here is an example runcard:
 
 .. code-block:: yaml
@@ -25,7 +36,7 @@ For details about each process see each tutorial. Here is an example runcard:
     ############################################################
     dataset_inputs:
 
-    ### 'Vanilla' datasets ###
+    ### 'Standard' datasets ###
     - {dataset: NMCPD_dw_ite, frac: 0.75}
     - {dataset: NMC, frac: 0.75}
     - {dataset: SLACP_dwsh, frac: 0.75}

docs/sphinx/build/html/_sources/tutorials/simu_fit.rst

@@ -367,10 +367,7 @@ Additinally, in :math:`\text{SIMUnet}` you will find this file:
 
 - ``bsm_fac.csv``: file with the values of the EFT coefficients for this replica.
 
-Once the fit is complete, the next steps involve uploading and analyzing the results:
-
-1. Commence by uploading the fit data using ``vp-upload runcard_folder`` and then
-   retrieve and install the fitted PDF set with ``vp-get fit fit_name``.
+Once the fit is complete, the next steps involve uploading and analysing the results.
 
 .. _upload-fit:
 

docs/sphinx/build/html/datasets.html

@@ -345,6 +345,22 @@ <h2> Contents </h2>
   <section id="dataset-selection">
 <span id="dataset"></span><h1><strong>Dataset selection</strong><a class="headerlink" href="#dataset-selection" title="Permalink to this heading">#</a></h1>
 <p>Here is the list of the datasets we implemented in SIMUnet to fit SMEFT coefficients. For each dataset, we indicate the experiment, the centre of mass energy <span class="math notranslate nohighlight">\(\sqrt{s}\)</span>, the final-state channel, the observable(s) used in the fit, the integrated luminosity <span class="math notranslate nohighlight">\(\mathcal{L}\)</span> in inverse femtobarns, and the number of data points <span class="math notranslate nohighlight">\(n_{\rm dat}\)</span>, together with the corresponding publication reference. The datasets are organised by physical sectors, namely top, Drell-Yan, W helicity, EW precision observables, Higgs and Diboson.</p>
+<p>The user can easily include any of the following datasets to perform a fit with <span class="math notranslate nohighlight">\(\text{SIMUnet}\)</span>. This
+is done by adding the corresponding <code class="docutils literal notranslate"><span class="pre">Dataset</span> <span class="pre">name</span></code> with its corresponding configuration keys to the <code class="docutils literal notranslate"><span class="pre">dataset_input</span></code> in the runcard,
+as shown in the example below:</p>
+<div class="highlight-yaml notranslate"><div class="highlight"><pre><span></span><span class="nt">dataset_inputs</span><span class="p">:</span>
+<span class="p p-Indicator">-</span><span class="w"> </span><span class="p p-Indicator">{</span><span class="nt">dataset</span><span class="p">:</span><span class="w"> </span><span class="nv">NMC</span><span class="p p-Indicator">,</span><span class="nt"> frac</span><span class="p">:</span><span class="w"> </span><span class="nv">0.75</span><span class="p p-Indicator">}</span>
+<span class="p p-Indicator">-</span><span class="w"> </span><span class="p p-Indicator">{</span><span class="nt">dataset</span><span class="p">:</span><span class="w"> </span><span class="nv">ATLASTTBARTOT7TEV</span><span class="p p-Indicator">,</span><span class="nt"> cfac</span><span class="p">:</span><span class="w"> </span><span class="p p-Indicator">[</span><span class="nv">QCD</span><span class="p p-Indicator">],</span><span class="nt"> simu_fac</span><span class="p">:</span><span class="w"> </span><span class="s">&quot;EFT_NLO&quot;</span><span class="p p-Indicator">}</span>
+<span class="p p-Indicator">-</span><span class="w"> </span><span class="p p-Indicator">{</span><span class="nt">dataset</span><span class="p">:</span><span class="w"> </span><span class="nv">CMS_SINGLETOPW_8TEV_TOTAL</span><span class="p p-Indicator">,</span><span class="nt"> simu_fac</span><span class="p">:</span><span class="w"> </span><span class="s">&quot;EFT_NLO&quot;</span><span class="p p-Indicator">,</span><span class="nt"> use_fixed_predictions</span><span class="p">:</span><span class="w"> </span><span class="nv">True</span><span class="p p-Indicator">}</span>
+</pre></div>
+</div>
+<p>The dataset <code class="docutils literal notranslate"><span class="pre">NMC</span></code> is a NNPDF dataset, and it is included as it would be on an NNPDF4.0 runcard.
+<span class="math notranslate nohighlight">\(\text{SIMUnet}\)</span> recognises it as a standard dataset with no need for SMEFT modifications.</p>
+<p>In contrast, the dataset <code class="docutils literal notranslate"><span class="pre">ATLASTTBARTOT7TEV</span></code> includes the additional key <code class="docutils literal notranslate"><span class="pre">simu_fac</span></code>,
+which instructs <span class="math notranslate nohighlight">\(\text{SIMUnet}\)</span> to calculate theory predictions for this dataset using SMEFT NLO K-factor, as it is clear from its <code class="docutils literal notranslate"><span class="pre">EFT_NLO</span></code> value.</p>
+<p>The third dataset, <code class="docutils literal notranslate"><span class="pre">CMS_SINGLETOPW_8TEV_TOTAL</span></code>, includes two keys: <code class="docutils literal notranslate"><span class="pre">simu_fac</span></code>, which has already been discussed, and <code class="docutils literal notranslate"><span class="pre">use_fixed_predictions</span></code>. The <code class="docutils literal notranslate"><span class="pre">use_fixed_predictions</span></code> key, when set to True,
+effectively removes the PDF dependence of the dataset. This approach is appropriate for observables that are either independent of the PDFs (like EWPO or W-helicities in top decays, for example) or are only weakly influenced by them,
+and therefore a full computation of FK-tables would be too computationally taxing.</p>
 <section id="top-sector">
 <h2>Top sector<a class="headerlink" href="#top-sector" title="Permalink to this heading">#</a></h2>
 <section id="ttbar">

docs/sphinx/build/html/tutorials/contaminated_fit.html

@@ -660,7 +660,7 @@ <h2>3. Uploading the fit<a class="headerlink" href="#uploading-the-fit" title="P
 <p>Once the fit is complete, the next steps involve uploading the results. This is particularly useful
 if, for example, you ran the fit on a cluster and want to make it avaiable to collaborators or download it
 from a different machine. You can upload the fit by using <code class="docutils literal notranslate"><span class="pre">vp-upload</span> <span class="pre">runcard_folder</span></code> and then fetch it
-with <code class="docutils literal notranslate"><span class="pre">vp-get</span> <span class="pre">fit</span> <span class="pre">fit_name</span></code>.</p>
+with <code class="docutils literal notranslate"><span class="pre">vp-get</span> <span class="pre">fit</span> <span class="pre">fit_name</span></code>. Note that, to upload the fit, appropriate credentials are required.</p>
 </section>
 </section>
 

docs/sphinx/build/html/tutorials/simu_closure_test.html

@@ -44,6 +44,7 @@
     <script src="../_static/_sphinx_javascript_frameworks_compat.js"></script>
     <script src="../_static/doctools.js"></script>
     <script src="../_static/scripts/sphinx-book-theme.js?digest=5a5c038af52cf7bc1a1ec88eea08e6366ee68824"></script>
+    <script async="async" src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js"></script>
     <script>DOCUMENTATION_OPTIONS.pagename = 'tutorials/simu_closure_test';</script>
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
@@ -317,7 +318,15 @@ <h1>SIMUnet closure test</h1>
 
   <section id="simunet-closure-test">
 <span id="simu-closure-test"></span><h1>SIMUnet closure test<a class="headerlink" href="#simunet-closure-test" title="Permalink to this heading">#</a></h1>
-<p>A SIMUnet closure test corresponds to running a BSM contaminated PDF fit and a simultaneous PDF-EFT fit at the same time.
+<p>Closure tests serve as a mechanism to assess the reliability of fits of PDFs and physical parameters in general, and the robustness of the methodologies involved.</p>
+<p>The <span class="math notranslate nohighlight">\(\text{SIMUnet}\)</span> code extends the capacity of the NNPDF closure test to run contaminated fits and closure tests probing the fit quality of both PDFs and
+physical parameters.</p>
+<p><span class="math notranslate nohighlight">\(\text{SIMUnet}\)</span> can produce BSM contaminated fits by injecting New Physics into the generated pseudodata
+while the fit is done assuming the Standard Model. With this functionality, any user
+can test the robustness of any New Physics scenario against being fitted away in a PDF fit.
+In this context, <span class="math notranslate nohighlight">\(\text{SIMUnet}\)</span> can perform closure tests for both PDFs and physical parameters, giving surety of the reliability of the <span class="math notranslate nohighlight">\(\text{SIMUnet}\)</span>
+methodology.</p>
+<p>A <span class="math notranslate nohighlight">\(\text{SIMUnet}\)</span> closure test corresponds to running a BSM contaminated PDF fit and a simultaneous PDF-EFT fit at the same time.
 For details about each process see each tutorial. Here is an example runcard:</p>
 <div class="highlight-yaml notranslate"><div class="highlight"><pre><span></span><span class="c1"># Runcard for contaminated PDF fit with SIMUnet</span>
 <span class="c1">#</span>
@@ -336,7 +345,7 @@ <h1>SIMUnet closure test</h1>
 <span class="c1">############################################################</span>
 <span class="nt">dataset_inputs</span><span class="p">:</span>
 
-<span class="c1">### &#39;Vanilla&#39; datasets ###</span>
+<span class="c1">### &#39;Standard&#39; datasets ###</span>
 <span class="p p-Indicator">-</span><span class="w"> </span><span class="p p-Indicator">{</span><span class="nt">dataset</span><span class="p">:</span><span class="w"> </span><span class="nv">NMCPD_dw_ite</span><span class="p p-Indicator">,</span><span class="nt"> frac</span><span class="p">:</span><span class="w"> </span><span class="nv">0.75</span><span class="p p-Indicator">}</span>
 <span class="p p-Indicator">-</span><span class="w"> </span><span class="p p-Indicator">{</span><span class="nt">dataset</span><span class="p">:</span><span class="w"> </span><span class="nv">NMC</span><span class="p p-Indicator">,</span><span class="nt"> frac</span><span class="p">:</span><span class="w"> </span><span class="nv">0.75</span><span class="p p-Indicator">}</span>
 <span class="p p-Indicator">-</span><span class="w"> </span><span class="p p-Indicator">{</span><span class="nt">dataset</span><span class="p">:</span><span class="w"> </span><span class="nv">SLACP_dwsh</span><span class="p p-Indicator">,</span><span class="nt"> frac</span><span class="p">:</span><span class="w"> </span><span class="nv">0.75</span><span class="p p-Indicator">}</span>

docs/sphinx/build/html/tutorials/simu_fit.html

@@ -669,11 +669,7 @@ <h2>Output of the fit<a class="headerlink" href="#output-of-the-fit" title="Perm
 <ul class="simple">
 <li><p><code class="docutils literal notranslate"><span class="pre">bsm_fac.csv</span></code>: file with the values of the EFT coefficients for this replica.</p></li>
 </ul>
-<p>Once the fit is complete, the next steps involve uploading and analyzing the results:</p>
-<ol class="arabic simple">
-<li><p>Commence by uploading the fit data using <code class="docutils literal notranslate"><span class="pre">vp-upload</span> <span class="pre">runcard_folder</span></code> and then
-retrieve and install the fitted PDF set with <code class="docutils literal notranslate"><span class="pre">vp-get</span> <span class="pre">fit</span> <span class="pre">fit_name</span></code>.</p></li>
-</ol>
+<p>Once the fit is complete, the next steps involve uploading and analysing the results.</p>
 </section>
 <section id="upload-fit">
 <span id="id4"></span><h2>3. Uploading the fit<a class="headerlink" href="#upload-fit" title="Permalink to this heading">#</a></h2>