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Add ALLEEGRO and IDEA steering file
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115 changes: 98 additions & 17 deletions full-detector-simulations/FCCeeGeneralOverview/FCCeeGeneralOverview.md
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<!-- This version has been prepared for a 30 minutes tutorial at the Second US FCC Workshop (2024): https://indico.mit.edu/event/876/contributions/2893/ -->

Welcome to this general overview of the FCC-ee Full Simulation.
This tutorial aims at showing you how to run the state of the art full simulation of the various detector concepts currently under study for FCC-ee: CLD, ALLEGRO and IDEA. The DD4hep geometry descriptions of these detectors are hosted in the [k4geo](https://github.com/key4hep/k4geo/tree/main/FCCee) GitHub repository and made centrally available with the Key4hep stack under the `$K4GEO` environment variable.
This tutorial aims at showing you how to run the state of the art full simulation of the various detector concepts currently under study for FCC-ee: CLD, ALLEGRO and IDEA. The [DD4hep](https://dd4hep.web.cern.ch/dd4hep/usermanuals/DD4hepManual/DD4hepManual.pdf) geometry descriptions of these detectors are hosted in the [k4geo](https://github.com/key4hep/k4geo/tree/main/FCCee) GitHub repository and made centrally available with the Key4hep stack under the `$K4GEO` environment variable. This tutorial should work on any `Alma9` machine with `cvmfs` access.

<!-- Click on the k4geo link, show and explain the different existing CLD versions (the one starting by FCCee are legacy for reproducibility, the useful ones are CLD_...) and where they are documented -->

So, let's start playing with Full Sim!

## Setting-up the environment
```bash
# connect to an Alma9 machine with cvmfs access
ssh -X [email protected]
# set-up the Key4hep environment
source /cvmfs/sw-nightlies.hsf.org/key4hep/setup.sh
# create a repository with the tutorial
mkdir FCC_Full_Sim_Tutorial
cd FCC_Full_Sim_Tutorial
git clone https://github.com/HEP-FCC/fcc-tutorials
cd fcc-tutorials/full-detector-simulations/FCCeeGeneralOverview/
```

## Towards Full Sim physics analyses with CLD

The CLD detector has a complete geometry description and reconstruction chain. It is thus a very good candidate to start full sim physics analyses. To illustrate that, we will process some physics events through its Geant4 simulation and reconstruction, look at automatically generated diagnostic plots and produce ourselves a higher level quantity plot.
Expand All @@ -19,9 +32,10 @@ Let's first run the CLD Geant4 simulation, through ddsim, for some $e^{+}e^{-} \
<!-- /eos/experiment/fcc/ee/generation/stdhep/wzp6_ee_mumuH_ecm240/events_057189088.stdhep.gz -->

```bash
source /cvmfs/sw-nightlies.hsf.org/key4hep/setup.sh
# retrieve Z(mumu)H(X) MC generator events
wget http://fccsw.web.cern.ch/fccsw/tutorials/MIT2024/wzp6_ee_mumuH_ecm240_GEN.stdhep.gz
gunzip wzp6_ee_mumuH_ecm240_GEN.stdhep.gz
# run the Geant4 simulation
ddsim -I wzp6_ee_mumuH_ecm240_GEN.stdhep -N 10 -O wzp6_ee_mumuH_ecm240_CLD_SIM.root --compactFile $K4GEO/FCCee/CLD/compact/CLD_o2_v05/CLD_o2_v05.xml
# NB: we run only on 10 events (-N 10) here for the sake of time
```
Expand All @@ -33,22 +47,26 @@ This will produce an output file in edm4hep dataformat with Geant4 SimHits that
Let's now apply the CLD reconstruction (from ILCSoft through the Gaudi wrappers and data format converters) on top of the SIM step:

```
cd ../../../
git clone https://github.com/key4hep/CLDConfig.git
cd CLDConfig/CLDConfig
k4run CLDReconstruction.py --inputFiles ../../wzp6_ee_mumuH_ecm240_CLD_SIM.root --outputBasename wzp6_ee_mumuH_ecm240_CLD_RECO
k4run CLDReconstruction.py --inputFiles ../../fcc-tutorials/full-detector-simulations/FCCeeGeneralOverview/wzp6_ee_mumuH_ecm240_CLD_SIM.root --outputBasename ../../fcc-tutorials/full-detector-simulations/FCCeeGeneralOverview/wzp6_ee_mumuH_ecm240_CLD_RECO
# Change the EvtMax variable if you want to run on more events (-1 means all events)
# And do not forget to modify the geoservice.detectors variable if you do not use the central detector
cd ../../fcc-tutorials/full-detector-simulations/FCCeeGeneralOverview/
```

This creates a bunch of RECO level collections, including `edm4hep::ReconstructedParticle` from Particle Flow (PandoraPFA). You can inspect the ROOT file content with
This created an edm4hep ROOT file with a bunch of new RECO level collections, including `edm4hep::ReconstructedParticle` from Particle Flow (PandoraPFA). You can inspect the ROOT file content with

```
podio-dump wzp6_ee_mumuH_ecm240_CLD_RECO.root
podio-dump wzp6_ee_mumuH_ecm240_CLD_RECO_edm4hep.root
```
<!-- Explain a bit the rootfile content -->

A detailed documentation on the collection content still has to be written.

NB: this step also produces a file named `wzp6_ee_mumuH_ecm240_CLD_RECO_aida.root` where you can find a lot of debugging distributions such as the pulls of the track fit.

### Plotting the Higgs recoil mass

Let's now use the reconstructed sample to produce some physics quantities. As an example, we will plot the Higgs recoil mass using the Python bindings of edm4hep. The following very simple script already does a decent job:
Expand Down Expand Up @@ -84,33 +102,48 @@ canvas_recoil.Print("recoil_mass.png")
Let's run it on a sample with slightly more stat:

```
cd ../../
git clone https://github.com/HEP-FCC/fcc-tutorials
cd fcc-tutorials/full-detector-simulations/FCCeeGeneralOverview/
wget https://fccsw.web.cern.ch/fccsw/tutorials/MIT2024/wzp6_ee_mumuH_ecm240_CLD_RECO_moreStat.root
python plot_recoil_mass.py
display recoil_mass.png
```

This illustrates how easy it is already to do physics with Full Sim. Of course, if we had to do a realistic analysis, we would run on more events, properly select muons from the Z, include backgrounds, ..., and we would therefore use FCCAnalyses or plain C++ but it is not the topic of this tutorial. If you want to go further, the following [Doxygen page](https://edm4hep.web.cern.ch/classedm4hep_1_1_reconstructed_particle-members.html) will help you in understanding what members can be called on a given edm4hep object.

## Optimizing a detector with Full Sim: ALLEGRO ECAL
## Towards detector optimization with Full Sim: ALLEGRO ECAL

In this section we will learn how to run with a modified version of a detector (needed for optimization), taking the ALLEGRO ECAL as an example.

### Modifying the sub-detector content

So far we have been using the 'central' version of the detector, as directly provided by Key4hep. The first thing we have to do to run a modified version is to clone the repository where detector geometries are hosted and update the environment variable `$K4GEO`:
So far we have been using the 'central' version of the detector, as directly provided by Key4hep. The first thing we have to do to run a modified version is to clone the repository where the DD4hep detector geometries are hosted and update the environment variable `$K4GEO` so that it points to your local folder:

```
# go in a 'clean' place i.e. outside of the git repository in which you were before
# go in a 'clean' place i.e. outside of the git repository in which you were before
cd ../../../
# load the Key4hep environment if not yet done so
source /cvmfs/sw-nightlies.hsf.org/key4hep/setup.sh
git clone https://github.com/key4hep/k4geo
export K4GEO=$PWD/k4geo/
```
NB: a typical DD4hep detector implementation has a C++ detector builder where the shapes, volumes and placedVolumes are defined plus an xml "compact" file that configures the detector (e.g. setting the dimensions) and from which the C++ builder retrieves all the free parameters. When modifying the compact file nothing has to be recompiled, when touching the C++ detector builder, `k4geo` has to be recompiled and some more environment variables have to be set in order to use your local detector builder. This can be done as follows:
```
# No need to run this for the tutorial, it is just here for completeness
cd k4geo
# modify a detector builder, e.g. detector/calorimeter/ECalBarrel_NobleLiquid_InclinedTrapezoids_o1_v02_geo.cpp
mkdir build install
cd build
cmake .. -DCMAKE_INSTALL_PREFIX=../install
make install -j 8
cd ..
k4_local_repo # this updates environment variables and PATHs such as LD_LIBRARY_PATH
cd ..
```
In the xml compact file, the detector builder to use is specified by the `type` keyword in the `detector` beacon (e.g. `<detector id=1 name="MyCoolDetectorName" type="MYCOOLDETECTOR" ... />`) and it should match the detector type defined in the C++ builder with the instruction `DECLARE_DETELEMENT(MYCOOLDETECTOR)`.

Now lets first modify the sub-detector content of ALLEGRO. Since we will deal with the calorimeter, let's remove the drift chamber to run faster. For this, you just need to open the main detector compact file with your favorite text editor and remove the import of the drift chamber (line 39), for instance (copy paste won't work here):
Now lets first modify the sub-detector content of ALLEGRO. Since we will deal with the calorimeter, let's remove the drift chamber to run faster. For this, you just need to open the main detector "compact file" (`xml` file with detector parameters) with your favorite text editor and remove the import of the drift chamber (line 39), for instance:

```
# copy paste won't work here
vim $K4GEO/FCCee/ALLEGRO/compact/ALLEGRO_o1_v02/ALLEGRO_o1_v02.xml
:39
dd
Expand All @@ -119,18 +152,62 @@ dd

### Runnning a first ALLEGRO ECAL simulation

Let's now run a first simulation with w
Let's now run a first particle gun simulation:

```
cd fcc-tutorials/full-detector-simulations/FCCeeGeneralOverview/
ddsim --enableGun --gun.distribution uniform --gun.energy "10*GeV" --gun.particle e- --numberOfEvents 100 --outputFile electron_gun_10GeV_ALLEGRO_SIM.root --random.enableEventSeed --random.seed 42 --compactFile $K4GEO/FCCee/ALLEGRO/compact/ALLEGRO_o1_v02/ALLEGRO_o1_v02.xml
```

And apply the ALLEGRO reconstruction, including an MVA based calibration:
```
wget https://fccsw.web.cern.ch/fccsw/tutorials/MIT2024/lgbm_calibration-CaloClusters.onnx # needed for the MVA calibration
k4run run_ALLEGRO_RECO.py --EventDataSvc.input="electron_gun_10GeV_ALLEGRO_SIM.root" --out.filename="electron_gun_10GeV_ALLEGRO_RECO.root"
```

Now let's plot the energy resolution for raw clusters and MVA calibrated clusters:

```
python plot_calo_energy_resolution.py electron_gun_10GeV_ALLEGRO_RECO.root
# display with your favorite png renderer
```

Look at both distributions to see how the MVA calibration modifies the cluster energy. NB: we are in the middle of a transition for the Geant4 interface and we are here using the new version which is not yet fully validated, this explains the drop in energy.

### Runnning the ALLEGRO ECAL reconstruction
### Changing Liquid Argon to Liquid Krypton

wget https://fccsw.web.cern.ch/fccsw/tutorials/MIT2024/lgbm_calibration-CaloClusters.onnx
In a sampling calorimeter, the ratio between the energy deposited in the dead absorbers and sensitive media (sampling fraction) is an important parameter for energy resolution: the more energy in the sensitive media the better. Liquid Krypton being denser than Liquid Argon, it is an appealing choice for the detector design. Though it is more expensive and difficult to procure in real life, testing this option in Full Sim is extremely cheap:

### Plotting energy resolution
```
vim $K4GEO/FCCee/ALLEGRO/compact/ALLEGRO_o1_v02/ECalBarrel_thetamodulemerged.xml
# change "LAr" to "LKr" in line 114 and 121
```

And to accomodate this change, we have to update the sampling fraction in the steering file:

```
vim run_ALLEGRO_RECO.py
# comment out line 105 uncomment line 106
```
The derivation of new sampling fractions upon geometry change is done by ECAL experts and is out of scope for this tutorial.

Let's now re-run the simulation with the modified detector:
```
ddsim --enableGun --gun.distribution uniform --gun.energy "10*GeV" --gun.particle e- --numberOfEvents 100 --outputFile electron_gun_10GeV_ALLEGRO_LKr_SIM.root --random.enableEventSeed --random.seed 42 --compactFile $K4GEO/FCCee/ALLEGRO/compact/ALLEGRO_o1_v02/ALLEGRO_o1_v02.xml
k4run run_ALLEGRO_RECO.py --EventDataSvc.input="electron_gun_10GeV_ALLEGRO_LKr_SIM.root" --out.filename="electron_gun_10GeV_ALLEGRO_LKr_RECO.root"
python plot_calo_energy_resolution.py electron_gun_10GeV_ALLEGRO_LKr_RECO.root
```

See how the resolution changed.


## Towards IDEA tracking with the detailed Drift Chamber

```
ddsim --enableGun --gun.distribution uniform --gun.energy "10*GeV" --gun.particle e- --numberOfEvents 100 --outputFile electron_gun_10GeV_IDEA_SIM.root --random.enableEventSeed --random.seed 42 --compactFile $K4GEO/FCCee/IDEA/compact/IDEA_o1_v02/IDEA_o1_v02.xml
```


## Running IDEA simulation with detailed Drift Chamber


## Detector visualization
Expand Down Expand Up @@ -164,5 +241,9 @@ From there, you can navigate the geometry hierarchy to see how volumes are neste

This tool is useful but not perfect and will not meet all the needs (especially if you want to overlay a physics event). To go further, other solutions are described in the dedicated [Visualization](https://hep-fcc.github.io/fcc-tutorials/master/full-detector-simulations/Visualization/Visualization.html) tutorial.

## Additional Resources
- [Key4hep tutorial](https://key4hep.github.io/key4hep-doc/setup-and-getting-started/README.html)
- [FCC Full Sim webpage](https://fcc-ee-detector-full-sim.docs.cern.ch/)
- Bi-weekly FCC Full Sim [working meeting](https://indico.cern.ch/category/16938/)


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