update

Author: ShujieL

_episodes/01-dd4hep.md

@@ -17,14 +17,14 @@ keypoints:
 ## DD4hep simulation
 This Geant4-based simualtion package propagates particles through magnetic field and materials. Particles and detector hits for each event are saved in the output rootfiles.
 
-- __Input 1: Event generation__
+### __Input 1: Event generation__
 
   The collision event at ePIC, including the beam particles, vertices, and outgoing particles, are typically generated with a dedicated event generator, e.g. PYTHIA8 for specific physics channels. The outputs are provided to the DD4hep simulation in [HEPMC3 format](https://arxiv.org/pdf/1912.08005).
 
   One can also use the DD4hep's particle gun to generate outgoing particles with given vertex and distribution, see the [previous tutorial](https://eic.github.io/tutorial-simulations-using-ddsim-and-geant4/aio/index.html) on `ddsim`.
 
 
-- __Input 2: Detector description__
+### __Input 2: Detector description__
 
   The ePIC detector description in DD4hep is maintained [on github](https://github.com/eic/epic/). On the bottom of each sub-detector compact file under `epic/compact`, the `readout` block specifies how the detector hits are saved in the output rootfile.
 
@@ -41,13 +41,14 @@ This Geant4-based simualtion package propagates particles through magnetic field
   All hits from this silicon vertex barrel detector, including their position, energy deposit, time, will be stored under the branch `VertexBarrelHits` in output.
   Each detector hit also comes an assigned 64-bit cell ID, with the last 32 bits from right to left represents the hit localtion in a 0.020 x 0.020 mm mesh grid. This __segmentation__ often represents the detector granularity (in this case, the silicon pixel sensor size) that will be used later for hit digitization.
 
-- __Output__
+### __Output__
   The `event` tree in the simulation output contains
   - `MCParticles`: records the truth info of primary and secondary particles
   -  Individual branches for signals from various sub-detector systems e.g. `VertexBarrelHits`
 
 > Exercise 1.1: access simulation campaign rootfiles 
 > The simulation campaign [website](https://eic.github.io/epic-prod/documentation/default_datasets.html) documents the available datasets and version information.
+>
 > -__Browse the directory__ 
   > For stand-alone `xrdfs` command, see the [previous tutorials](https://eic.github.io/tutorial-analysis/01-introduction/index.html). Here we will proceed with the python interface:
   ```console
@@ -89,23 +90,23 @@ print(df)
 
 
 > Exercise 1.3: extract momentum distribution of primary electrons
+>
+> Make selection
 > ```console
-  # select electrons 
   from particle import Particle
   part   = Particle.from_name("e-")
   pdg_id = part.pdgid.abspid
-  condition1  = df["MCParticles.PDG"]==pdg_id
-  # select primary particles
-  condition2  = df["MCParticles.generatorStatus"]==1
-  
-  # extract momentum
-  # all electrons
-  df_new = df[condition1]
+  condition1  = df["MCParticles.PDG"]==pdg_id # select electrons
+  condition2  = df["MCParticles.generatorStatus"]==1  # select primary particles
+ ```
+> extract momentum
+> ```console 
+  df_new = df[condition1]   # all electrons
   mom    = np.sqrt(df_new["MCParticles.momentum.x"]**2+df_new["MCParticles.momentum.y"]**2+df_new["MCParticles.momentum.z"]**2)
   bins   = np.arange(0,20)
   _      = plt.hist(mom,bins=bins,alpha=0.5)
-  # primary electrons
-  df_new = df[condition1&condition2]
+
+  df_new = df[condition1&condition2]   # primary electrons
   mom    = np.sqrt(df_new["MCParticles.momentum.x"]**2+df_new["MCParticles.momentum.y"]**2+df_new["MCParticles.momentum.z"]**2)
   _      = plt.hist(mom,bins=bins,histtype="step", color='r')
   ```

_episodes/02-eicrecon.md

@@ -24,7 +24,7 @@ Each reconstruction step involves 3 components:
 > in this section, we will use __track reconstruction__ as an example. Please refer to the Lehigh reconstruction workfest [presentations](https://indico.bnl.gov/event/20727/sessions/7433/#20240725) for reconstruction workflow of other systems. 
 
 
-> - __Digitization__
+### __Digitization__
   All simualted detector hits are digitized to reflect certain detector specs e.g. spatial and time resolution, and energy threshold.  For example, the `VertexBarrelHits` from simulation are digitized through the `SiliconTrackerDigi` factory in `EICrecon/src/detectors/BVTX/BVTX.cc`:
   ```console
       // Digitization
@@ -72,7 +72,8 @@ Each reconstruction step involves 3 components:
       - int32_t           charge             
       - int32_t           timeStamp         
   ```
-    In addition, the one-to-one relation between the sim hit and its digitized hit is stored as an `MCRecoTrackerHitAssociation` object: 
+
+  In addition, the one-to-one relation between the sim hit and its digitized hit is stored as an `MCRecoTrackerHitAssociation` object: 
   ```console
     edm4eic::MCRecoTrackerHitAssociation:
     Description: "Association between a RawTrackerHit and a SimTrackerHit"
@@ -83,7 +84,8 @@ Each reconstruction step involves 3 components:
       - edm4eic::RawTrackerHit rawHit       // reference to the digitized hit
       - edm4hep::SimTrackerHit simHit       // reference to the simulated hit
   ```
-    which is filled in `SiliconTrackerDigi.cc`:
+
+  which is filled in `SiliconTrackerDigi.cc`:
   ```console
     auto hitassoc = associations->create();
     hitassoc.setWeight(1.0);
@@ -95,34 +97,34 @@ Each reconstruction step involves 3 components:
 {: .challenge}
 
 
-> -__Track Reconstruction__
-    By default, we use the Combinatorial Kalman Filter from the ACTS library to handle track finding and fitting. This happens in the `CKFTracking` factory.  
+### __Track Reconstruction__
+By default, we use the Combinatorial Kalman Filter from the ACTS library to handle track finding and fitting. This happens in the `CKFTracking` factory.  
 
 > Exercise 2.2: please find the inputs and outputs of `CKFTracking`, and draw a flow chart from `CentralTrackerMeasurements` to `CentralTrackVertices`.
 {: .challenge}
 
--__Reconstruction output__
-    - `events` tree:
-        - `MCParticles` and detector sim hits are copied from simulation output to recon output
-        - outputs from each step of recon algorithms must be either `edm4hep` or `edm4eic` object if you want to save them in recon output
-        - the default list of saved objects in recon output is defined in `EICrecon/src/services/io/podio/JEventProcessorPODIO.cc`. It can be configured in command line. 
-    - `podio_metadata` tree:
-        - `events___idTable` provides a lookup table between output collection name and IDs.
+### __Reconstruction output__
+> - `events` tree:
+    > - `MCParticles` and detector sim hits are copied from simulation output to recon output
+    > - outputs from each step of recon algorithms must be either `edm4hep` or `edm4eic` object if you want to save them in recon output
+    > - the default list of saved objects in recon output is defined in `EICrecon/src/services/io/podio/JEventProcessorPODIO.cc`. It can be configured in command line. 
+> - `podio_metadata` tree:
+    > - `events___idTable` provides a lookup table between output collection name and IDs.
 
 
 > Exercise 2.3: 
 > The __vector member__ or __relation__ of a given data collection is saved in a separate branch starts with "_".  
-    - use ```tree.keys(filter_name="_CentralCKFTrajectories*",recursive=False)``` to list those members in `CentralCKFTrajectories`
-    - for a given event, the vector member `_CentralCKFTrajectories_measurementChi2` provides a list of chi2 for each meaurement hit respectively. If multiple trajectories are found for one event, you can use `CentralCKFTrajectories.measurementChi2_begin` to locate the start index of a given trajectory (subentry). 
+> - Please use ```tree.keys(filter_name="_CentralCKFTrajectories*",recursive=False)``` to list those members in `CentralCKFTrajectories`
+> - for a given event, the vector member `_CentralCKFTrajectories_measurementChi2` provides a list of chi2 for each meaurement hit respectively. If multiple trajectories are found for one event, you can use `CentralCKFTrajectories.measurementChi2_begin` to locate the start index of a given trajectory (subentry). 
 {: .challenge}
 
 > Exercise 2.4: 
 > `CentralTrackerMeasurements` saved all available space points for tracking as 2D meaurement attached to representing surfaces. 
-    - use the relation `_CentralTrackerMeasurements_hits` to trace back to the original detector hit collection (hint: use the collection ID lookup table), and obtain the 3D coordinate of the hit 
+> - Please use the relation `_CentralTrackerMeasurements_hits` to trace back to the original detector hit collection (hint: use the collection ID lookup table), and obtain the 3D coordinate of the hit 
 {: .challenge}
 {% include links.md %}
 
 ## Future reading
-- Generate your own simulation and reconstruction rootfiles [tutorial](https://eic.github.io/tutorial-simulations-using-ddsim-and-geant4/)
-- Contribute to reconstruction algorithsm [tutorial](https://eic.github.io/tutorial-reconstruction-algorithms/)
-- Develop analysis benchmarks [tutorial](https://eic.github.io/tutorial-developing-benchmarks/)
+> - Generate your own simulation and reconstruction rootfiles [tutorial](https://eic.github.io/tutorial-simulations-using-ddsim-and-geant4/)
+> - Contribute to reconstruction algorithsm [tutorial](https://eic.github.io/tutorial-reconstruction-algorithms/)
+> - Develop analysis benchmarks [tutorial](https://eic.github.io/tutorial-developing-benchmarks/)