Merge branch 'gh-pages' into fleble

_episodes/02-introduction.md

@@ -28,6 +28,8 @@ First we can begin with some introductions. The facilitators are:
 
 The team members are:
 - Florian Eble, PhD student at ETH Zurich, working on Dark Matter search by looking for Semi-Visible Jets, also working on luminosity in BRIL.
+- Yusuf Can Cekmecelioglu, Masters Student at Bogazici Univeristy, Istanbul -- Currently working on back-end of the HGCal detector.
+- Federica Riti, PhD student at ETH Zurich, she works on the search for lepton flavour universality violation via the R(J/psi) measurement.
 - (We'll practice some basic push and pull to fill in your info later)
 
 The overall goal of this long exercise is to offer everyone a high-level exposure of the different components of a physics analysis. In practice, carrying out an analysis takes months and often past a year; we hope to cover the key points of the process and won't have too much time to do so. To help you through the material, we will be providing some useful code for your use. We strongly suggest you focus on understanding the conceptual flow of the analysis and to ask as many questions as you'd like. We also encourage that you get creative and challenge yourself! If there are any parts of the exercise that you feel you can tackle by writing your own scripts, or optimizing ours, please do! We are all collaborators and we can learn a lot from each other.

_episodes/07-preselection.md

@@ -3,14 +3,145 @@ title: "Developing a pre-selection"
 teaching: 10
 exercises: 50
 questions:
-- "What cuts could you apply that follow the signal topology"
+- "What is the purpose of a preselection?"
+- "What loose cuts could you apply that follow the signal topology?"
 - "What are background processes that could enter this selection?"
 - "How do I normalise background processes?"
 objectives:
-- "Develop a signal selection cutflow."
+- "Define a 'good' region of the detector and apply MET filters."
+- "Develop a simple signal selection cutflow."
 - "Make stack plots of background processes."
-- "Make N-1 plots to understand the effect of selection cuts."
 keypoints:
-- "First key point. Brief Answer to questions. (FIXME)"
+- "Preselection reduces data size, but further signal optimization is done later"
+- "Preselected events should be in good regions of the detector with appropriate filters"
+- "Stacked histograms are an important tool for creating cuts"
 ---
-FIXME
+# Introduction
+
+A preselection serves two purposes, first to ensure that passing events only utilize a "good" region of the detector with
+appropriate noise filters and second to start applying simple selections motivated by the physics of the signal topology. 
+
+## Defining a "Good" Region of the Detector
+
+A "good" region of the detector depends heavily on the signal topology. The muon system and tracker extend to about 
+|η| = 2.4 while the calorimeter extends to |η| = 3.0 with the forward calorimeter extending further. Thus, if the signal
+topology relies heavily on tracking or muons, then a useful preselection would limiting the region to |&eta;| < 2.4. Some 
+topologies, like vector boson fusion (commonly called VBF) have two forward (high eta) jets, so placing a preselection 
+that requires two forward jets is a useful preselection.
+
+> ## Discuss (5 min)
+> Now, let's take a more detailed look at our signal topology and see how it fits in with the detector. The b-star is produced 
+from the interaction of a bottom quark and a gluon, will this production mode yield any characteristic forward jets?
+> In this topology, the b-star decays to a jet from a W boson and a jet from a top quark. What is characteristic of a top jet?
+What about a W jet? How does this impact the region of the detector needed? What |&eta;| and &phi; in the detector do we need? 
+Think about this while looking at the Feynman diagram and the signal topology.
+>
+> <img src="../fig/bstarFeynman.png" alt="bstarFeynman" style="width:200px"> 
+> <img src="../fig/bstarTopo.png" alt="bstarTopo" style="width:200px"> 
+> 
+> > ## Solution
+> > The production mode does not have any characteristic forward jets, but the final state has two jets. The top quark decays to a 
+> > b jet and W jet, where the b jet is typically identified by making use of it's characteristic secondary vertex. This secondary
+> > vertex is identified in the tracker. Both the W jet and top jet have unique substructure that can be used to distinguish them 
+> > from QCD jets. Therefore it is crucial to use a region of the detector with good tracking and granular calorimetery,so we should
+> > restrict |&eta;| < 2.4. There are no detector differences in phi that should impact this search, so there should be no restriction
+> > in &phi;.
+> > {: .source}
+> {: .solution}
+> {: .source}
+{: .callout}
+
+## Finding Appropriate MET Filters
+
+Missing transverse momentum (called MET) is used to identify detector noise and MET filters are used to remove detector noise. The 
+MET group publishes recommendations on the filters that should be used for different eras of data.
+
+> ## Exercise (5 min)
+> The recommended MET filters for Run II are listed on this [twiki](https://twiki.cern.ch/twiki/bin/viewauth/CMS/MissingETOptionalFiltersRun2).
+> Use this twiki to create a list of MET filters to use in the preselection.
+> {: .source}
+{: .callout}
+
+
+## Simple Selections
+
+The preselection should also include a set of simple selections based on our physics knowledge of the signal topology. These "simple"
+selections typically consist of loose lower bounds only, which help to reduce the number of events which will get passed to the rest of
+the analysis while still preserving the signal region. 
+
+Consider a heavy resonance decaying to two Z bosons that produce jets to create a dijet final state. In this case, the energy of the 
+collision would go into producing a heavy resonance with little transverse momentum, so conservation of momentum tells us that the jets should
+be well separated in &phi;, ideally they should have a separation of &pi; in &phi;. Therefore placing a selection of &Delta;&phi; > &pi;/2 should 
+not cut out signal, but will reduce the number of events passed on to the next stage. This also a good stage to place a lower limit on 
+the jet p<sub>T</sub>.  
+
+A jet originating from a Z boson should also have two "prongs" (regions of energy in the calorimeter), these "prongs" are part of the jet
+substructure discussed in the earlier lessons. For a two pronged jet like a Z jet, it is good to place a lower limit on the &tau;<sub>21</sub> ratio. 
+Another useful substructure variable to use in the preselection is the softdrop mass. The softdrop algorithm will help to reduce the amount
+of pileup that is used when measuring the jet mass. The preselection is a good place to define a wide softdrop mass region. For this example,
+a wide region around the W boson mass would be ideal, such as 65 < m<sub>SD</sub> < 115 GeV. 
+
+It is important to emphasize that the preselections should be relatively light. It is important to check that the preselection is not eliminating
+large amounts of signal. A good way to monitor this is to utilize stacked histograms. More about these plots is described below.
+
+> ## Discuss (5 min)
+> Again, use the images above to think about the signal topology. What "simple" selections can be used in the preselection? Any
+&Delta;&phi; or p<sub>T</sub> criteria? What about substructure?
+>
+> 
+> > ## Solution
+> > In this signal topology the t and W should be well separated, so a light &Delta;&phi; cut should be placed. Think about a reasonable 
+selection and investigate the result in the N-1 exercise. Same for the jet p<sub>T</sub>. Both the top jet and the W jet should have substructure.
+> > The top jet should have three prongs and W jet should have two prongs. Think about the softdrop regions and n-subjettiness (&tau;) 
+ratios that should be used and investigate them in the N-1 exercise.
+> > {: .source}
+> {: .solution}
+> {: .source}
+{: .callout}
+
+# Applying our selection and monitoring the MC response
+
+When applying the preselection, the selections will be placed serially in the code creating a "cutflow". The filters are applied first 
+to ensure that the data was taken in "good" detector conditions. Then the kinematic/substructure cuts are applied. It is important to 
+monitor the signal and background in between these physics inspired cuts.
+
+> ## Exercise (20 min) Stacked Plots to Monitor Signal and Background
+> Find where the filters are applied in the `bs_select.py` script, check that all the filters are there, and then create a stacked histogram 
+displaying the &Delta;&phi; between the leading and subleading jet. 
+> This stacked histogram should display the signal Monte Carlo with the background Monte Carlo stacked on top.
+>
+> 
+> > ## Solution
+> > The filters are listed as flags
+> > ~~~python
+> > flags = ["Flag_goodVertices",
+> >        "Flag_globalTightHalo2016Filter", 
+> >        "Flag_eeBadScFilter", 
+> >        "Flag_HBHENoiseFilter", 
+> >        "Flag_HBHENoiseIsoFilter", 
+> >        "Flag_ecalBadCalibFilter", 
+> >        "Flag_EcalDeadCellTriggerPrimitiveFilter"]
+> > ~~~
+> > {: .source}
+> > Then they are applied using the `Cut` function
+> >
+> > ~~~python
+> > # Initial cuts
+> > a.Cut('filters',a.GetFlagString(flags))
+> > ~~~
+> > 
+> > Make the stacked histogram in &Delta;&phi;, 
+> >
+> >
+> > Ideally, we would make stacked histograms for m<sub>SD</sub>, &tau;<sub>21</sub>, and jet p<sub>T</sub>. 
+> > These plots are left as a bonus exercise. 
+> > 
+> > {: .source}
+> {: .solution}
+> {: .source}
+{: .callout}
+
+
+
+{% include links.md %}
+