ssm-review.md

+# Mass Gap  
+
+## Development teams
+
+Sushant Sharma Chaudhary, Deep Chatterjee
+
+
+## Reviewers and sign-off
+
+
+|   Reviewer  |  Status |      version/tag      | Sign-off hash |
+| ----------- | ------- | --------------------- | ------------- |
+| M. Coughlin | ongoing | ligo.em-bright v1.1.0 |               |
+| A. Toivonen | ongoing | ligo.em-bright v1.1.0 |               |
+
+## Step-wise installation of packages:
+1. Clone the repository in a desired directory.
+```
+git clone git@git.ligo.org:emfollow/em-properties/em-bright.git
+```
+2. Create a anaconda environment using: `conda create -n {name} python=3.{9/10}`
+
+3. Activate the environment : `conda activate {name}`
+
+4. Install poetry using : `pip install poetry`
+
+5. Go to the em-bright cloned directory and install the package using : `poetry install`
+
+## Training classifier
+1. Change configurations for training in `etc/conf.ini` (if required)
+2. Creating condor jobs for training:
+``` 
+em_bright_dag_writer
+    -d {filename}.dag \
+    -w {output_dir} \
+    -i /home/sushant.sharma-chaudhary/O4_ssm_injections \ 
+    -c {CLONED_PROJECT_DIRECTORY}/etc/conf.ini \
+    -e $(dirname `which em_bright_dag_writer`) \
+    -m ssm
+```
+3. Change directory to `{output_dir}`
+4. Submit condor jobs :
+` condor_submit_dag {filename}.dag`
+
+## Output files
+1. The `gstlal_ssm.pickle & mbta_ssm.pickle` output is stored at `{output_dir}` directory
+2. The `{pipeline}_param_sweep_has_{SSM/NS/MassGap}_ssm.png` is also stored at `{output_dir}` directory
+
+## Testing classifier on gstlal/mbta reconstructed data
+1. If you have previously installed em-bright then: `rm ~/.ligo.em_bright`
+2. In the conda environment open python
+``` 
+from ligo.em_bright import em_bright
+em_bright.source_classification_ssm({m1},{m2},{chi1},{chi2},{m_chirp},{snr},{pipeline})
+```
+This outputs (has_SSM, has_NS, has_MassGap)
+
+## Relevant Notebooks
+
+1. Training and MDC performance [Notebook](https://git.ligo.org/emfollow/em-properties/mass_gap/-/blob/main/mass_gap_train.ipynb)
+2. High mass data analysis [Notebook](https://git.ligo.org/emfollow/em-properties/mass_gap/-/blob/main/high_mass_artifact_study.ipynb)
+3. Parameter grid search [Notebook](https://git.ligo.org/emfollow/em-properties/mass_gap/-/blob/main/mass_gap_grid_search.ipynb)
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