updated instructions and figures

_episodes/05-MET101.md

@@ -19,11 +19,8 @@ keypoints:
 > ## After following the instructions in the jets exercise setup, make sure you have the CMS environment and create the symbolic link to MET analyzer:
 >
 > ~~~
-> cd $CMSSW_BASE/src
+> cd $CMSSW_BASE/src/Analysis
 > cmsenv
-> cd Analysis
-> ln -s JMEDAS/MET_Analysis MET
-> scram b 
 > ~~~
 > {: .language-bash}
 {: .callout}
@@ -99,7 +96,7 @@ $$\textrm{PUPPI}~\vec{p}_{T}^{~miss} = - \sum_{i \in all~PF~Cands} w_i~\vec{p}_{
 
 The figure below presents the MET distribution for both PF MET and PUPPI MET in events with leptonically decaying W bosons, demonstrating the improved performance achieved with PUPPI MET.
 
-<img src="../fig/episode5/CMS-JME-17-001_Figure_003.pdf" alt="" style="width:70%">
+<img src="../fig/episode5/PF_vs_PUPPI.pdf" alt="" style="width:70%">
 
 > ## Remember
 > PUPPI MET is the default MET algorithm in Run~3.

_episodes/06-METcalibration.md

@@ -74,9 +74,8 @@ $$\vec{p}_{T}^{~miss,~Type-1} = - \sum_{i}^{nJets} \vec{p}_{T, jet}^{~corr} - \s
 
 
 <figure>
-  <img src="../fig/episode6/CMS-JME-17-001_Figure_009.pdf" alt="" style="width:40%">
-  <img src="../fig/episode6/CMS-JME-17-001_Figure_013.pdf" alt="" style="width:40%">
-  <center><figcaption>We will revisit this in MET performance, but this figure shows a comarison between the MET scale for raw PF MET vs Type-1 PF MET.</figcaption></center>
+  <img src="../fig/episode6/Response-2.pdf" alt="" style="width:40%">
+  <center><figcaption>We will revisit this in MET performance, but this figure shows a comparison between the MET scale for raw and Type-1 corrected MET.</figcaption></center>
 </figure>
 
 ### Type-1 Smear MET (For MC only)
@@ -147,7 +146,7 @@ We will use the same file as in the previous exercise [Exercise 1.1](https://cms
 Execute the following commands inside the CMSSW environment created during setup:
 
 ~~~
-cd $CMSSW_BASE/src/Analysis/MET
+cd $CMSSW_BASE/src/Analysis/JMEDAS
 cmsRun test/run_CMSDAS_MET_Exercise2_cfg.py
 ~~~
 {: .language-bash}
@@ -158,7 +157,7 @@ This script will:
 - Print the values of various sources of systematic uncertainties  
 Additionally, the script demonstrates how to access MET with different levels of corrections applied. By default, Type-1 MET is selected.
 
-The analyzer being run using is command is `CMSDAS_MET_Analysis/plugins/CMSDAS_MET_AnalysisExercise2.cc`. The printout looks like the following:
+The analyzer being run using is command is `JMEDAS/plugins/CMSDAS_MET_AnalysisExercise2.cc`. The printout looks like the following:
 ```
 Begin processing the 1st record. Run 1, Event 138728702, LumiSection 513811 on stream 0 at 05-Jan-2025 14:40:03.942 CST
  MET : 
@@ -185,7 +184,7 @@ Begin processing the 1st record. Run 1, Event 138728702, LumiSection 513811 on s
 {: .solution}
 
 ## Exercise 2.2
-Now we make the following modifications to the configuration script `CMSDAS_MET_Analysis/test/run_CMSDAS_MET_Exercise2_cfg.py`:
+Now we make the following modifications to the configuration script `JMEDAS/test/run_CMSDAS_MET_Exercise2_cfg.py`:
 - Prevent printouts by setting `doprints` to `False`.
 - Reduce the frequency of the report from "every" event to "every 10000" events by `modifying process.MessageLogger.cerr.FwkReport.reportEvery`.
 - Run over all events in the file by updating `process.maxEvent`s from 10 to -1.
@@ -200,7 +199,7 @@ Once the process completes (it will take a few seconds), it will produce a ROOT
 To generate the plot, run the following commands:
 
 ~~~
-cd $CMSSW_BASE/src/Analysis/MET/scripts
+cd $CMSSW_BASE/src/Analysis/JMEDAS/scripts
 root -l -q 'cmsdasmetplotsexercise2.C("step2a")'
 ~~~
 {: .language-bash}
@@ -218,7 +217,7 @@ Next, we will focus on Type-1 PF MET and study the impact of various uncertainti
 To generate the corresponding plot, use the following command:
 
 ~~~
-cd $CMSSW_BASE/src/Analysis/MET/scripts
+cd $CMSSW_BASE/src/Analysis/JMEDAS/scripts
 root -l -q 'cmsdasmetplotsexercise2.C("step2b")'
 ~~~
 {: .language-bash}

_episodes/07-METperformance.md

@@ -59,7 +59,7 @@ In this exercise, we will measure the scale of the "uncorrected" (raw) PF MET as
 
 To start, run the following commands:
 ~~~
-cd $CMSSW_BASE/src/Analysis/MET/scripts
+cd $CMSSW_BASE/src/Analysis/JMEDAS/scripts
 root -l -q 'cmsdasmetplotsexercise3.C("step3_scale_pfraw")'
 ~~~
 {: .language-bash}
@@ -75,7 +75,7 @@ root -l -q 'cmsdasmetplotsexercise3.C("step3_scale_pfraw")'
 
 Next, measure the MET scale using the Type-1 calibrated MET. Run the following commands:
 ~~~
-cd $CMSSW_BASE/src/Analysis/MET/scripts
+cd $CMSSW_BASE/src/Analysis/JMEDAS/scripts
 root -l -q 'cmsdasmetplotsexercise3.C("step3_scale_pftype1")'
 ~~~
 {: .language-bash}
@@ -94,7 +94,7 @@ root -l -q 'cmsdasmetplotsexercise3.C("step3_scale_pftype1")'
 
 Now, let’s analyze the resolution of MET as a function of pT(Z) and the number of pileup vertices. To do this, run:
 ~~~
-cd $CMSSW_BASE/src/Analysis/MET/scripts
+cd $CMSSW_BASE/src/Analysis/JMEDAS/scripts
 root -l -q 'cmsdasmetplotsexercise3.C("step3_resolution_pftype1")'
 ~~~
 {: .language-bash}
@@ -131,7 +131,7 @@ Equipped with the ability to evaluate MET performance through scale and resoluti
 
 To generate the corresponding plots, use the following command:
 ~~~
-cd $CMSSW_BASE/src/Analysis/MET/scripts
+cd $CMSSW_BASE/src/Analysis/JMEDAS/scripts
 root -l -q 'cmsdasmetplotsexercise4.C'
 ~~~
 {: .language-bash}

_episodes/08-AnomalousMET.md

@@ -33,6 +33,11 @@ These anomalous MET events can arise from:
 
 In such events, the MET value may be much higher than expected and does not reflect true missing energy from invisible particles (like neutrinos or dark matter candidates).
 
+<figure>
+  <img src="../fig/episode8/met_scanning_fig.pdf" alt="" style="width:70%">
+  <center><figcaption> An example of identifying the source of anomalous MET.</figcaption></center>
+</figure>
+
 ## Noisy event filters
 
 To identify false MET, several algorithms have been developed that analyze factors such as timing, pulse shape, and signal topology.
@@ -41,7 +46,7 @@ These cleaning algorithms, or filters, run in separate processing paths, and the
 Analyzers can use this decision bit to filter out noisy events. These filters are specifically designed to reject events with unusually large MET values caused by spurious signals.
 
 <figure>
-  <img src="../fig/episode8/CMS-JME-17-001_Figure_004.pdf" alt="" style="width:80%">
+  <img src="../fig/episode8/CMS-JME-17-001_Figure_004.pdf" alt="" style="width:70%">
   <center><figcaption> MET $p_T$ and leading jet $\phi$ distributions, with and without the application of event filters.</figcaption></center>
 </figure>
 
@@ -52,12 +57,12 @@ Noisy event filters (previously called MET Filters) are stored as trigger result
 
 In this exercise, we will show how to access the MET Filters in miniAOD. Please run the following commands:
 ~~~
-cd $CMSSW_BASE/src/Analysis
-cmsRun MET/test/run_CMSDAS_MET_Exercise4_cfg.py
+cd $CMSSW_BASE/src/Analysis/JMEDAS
+cmsRun test/run_CMSDAS_MET_Exercise4_cfg.py
 ~~~
 {: .language-bash}
 
-This example accesses the decision bits for the following MET Filters: `Beam Halo`, `HBHE`, `HBHE (Iso)`, `Ecal Trigger Primitives`, `EE SuperCluster`, `Bad Charged Hadron`, and `Bad PF Muon`. A "true" decision means the event was not rejected by the filter. The analyzer used in this example is `Analysis/MET/plugins/CMSDAS_MET_AnalysisExercise5.cc`. The printed result will look like this:
+This example accesses the decision bits for the following MET Filters: `Beam Halo`, `HBHE`, `HBHE (Iso)`, `Ecal Trigger Primitives`, `EE SuperCluster`, `Bad Charged Hadron`, and `Bad PF Muon`. A "true" decision means the event was not rejected by the filter. The analyzer used in this example is `JMEDAS/plugins/CMSDAS_MET_AnalysisExercise5.cc`. The printed result will look like this:
 
 ```
 Begin processing the 1st record. Run 317626, Event 178458435, LumiSection 134 on stream 0 at 28-Jun-2020 10:39:20.656 CDT
@@ -74,7 +79,7 @@ MET Filters decision:
 ```
 
 > ## Question 4
-> To see the output for a bad event, modify the input file in `Analysis/MET/test/run_CMSDAS_MET_Exercise4_cfg.py`.
+> To see the output for a bad event, modify the input file in `JMEDAS/test/run_CMSDAS_MET_Exercise4_cfg.py`.
 > Comment out the line for the first input file `cmsdas_met_METFilters1.root` and uncomment the line for the second input file `cmsdas_met_METFilters2.root`.
 > Then run the code again. What changes do you notice?
 {: .challenge}