EBOP Model Automatic input Value Estimation Neural network

A machine learning model for estimating input values for characterization of detached eclipsing binaries stars by JKTEBOP.

Included in this repository are codes for generating training & test datasets, for building, training & testing the machine learning model and some other stuff still to be decided.

Warning

This is a work in progress. Handle with care.

Installation

This code base was developed within the context of an Anaconda 3 conda environment named ebop_maven. This environment supports Python 3.9+, TensorFlow, Keras, lightkurve, astropy and any further libraries upon which the code is dependent. To set up the ebop_maven conda environment, having first cloned this GitHub repo, open a Terminal, navigate to this local directory and run the following command;

$ conda env create -f environment.yaml

You will need to activate the environment whenever you wish to run any of these modules. Use the following command;

$ conda activate ebop_maven

JKTEBOP

These codes have a dependency on the JKTEBOP tool for generating and fitting lightcurves. The installation media and build instructions can be found here. The JKTEBOP_DIR environment variable is used by ebop_maven to be locate the executable at runtime and is set to ~/jktebop/ in the ebop_maven conda env. This may require updating to match the location where JKTEBOP has been set up.

Alternative, venv installation

If you prefer not to use a conda environment, the following venv setup works although I haven't tested it as thoroughly. Again, from this directory run the following to create and activate the .ebop_maven env;

$ python -m venv .ebop_maven

$ source .ebop_maven/bin/activate

Then run the following to set up the required packages within the environment:

$ pip install -r requirements.txt

The ebop_maven package

Finally there is support for installing ebop_maven as a pip package, however this is still very much "work in progress" and subject to change. Simply run:

$ pip install git+https://github.com/SteveOv/ebop_maven

This will install the Estimator class, a pre-built default model and the required support libraries. The code used in the following steps for training and testing models is not installed.

Usage

Generation of training and testing datasets

The first step is to generate the datasets which will be used to train and test the machine learning model. These are built by running the following:

$ python3 make_training_datasets.py

This module will write three datasets under the ./datasets directory:

• formal-training-dataset : a synthetic dataset built by randomly sampling distributions of JKTEBOP model parameters across its entire parameter space
  • currently this generates 250,000 instances split on the ratios 80:20 between training and validation sets
• synthetic-mist-tess-dataset : a synthetic dataset of light-curves from physically plausible systems based on MIST stellar models and the TESS photometric bandpass
  • this depends on MIST isochrone files being downloaded and extracted into the./ebop_maven/libs/data/stellar_models/mist/MIST_v1.2_vvcrit0.4_basic_isos/ directory
  • 20,000 randomly oriented instances will be generated based on an initial random selection of metallicity, age and initial masses supplemented with lookups of stellar parameters in the isochrones
• formal-test-dataset : a set of real, well characterized systems from DEBCAT
  • selection criteria being the availability of TESS lightcurves, suitability for fitting with JKTEBOP and a published characterization from which parameters can be taken
  • the file ./config/formal-test-dataset.json contains the search criteria, labels and supplementary information for each target

This will take roughly one hour on a moderately powerful system, with the resulting datasets taking up ~15 GB of disk space.

Training and testing the machine learning model

The default machine learning model can be built and tested by running the following:

$ python3 make_trained_cnn_model.py

This will create the default CNN/DNN model, trained and validated on the formal-training-dataset to predict the $r_A+r_B$, $k$, $J$, $e\cos{\omega}$, $e\sin{\omega}$ and $b_P$ labels. Once trained it is evaluated on the synthetic-mist-tess-dataset before a final evaluation on the real systems of the formal-test-dataset.

By default CUDA cores are disabled so that training and testing is repeatable. In this configuration the process above takes about an hour and a half on my laptop with an 8 core 11th gen Intel i7 CPU. If you have them, CUDA cores can be enabled by setting the ENFORCE_REPEATABILITY const to False to give a significant reduction in training time.

Note: there are recorded incidents where TensorFlow v2.16.1 does not "see" installed GPUs (me, for one) and under these circumstances the above change may have no effect.

The compiled and trained model will be saved to the ./drop/training/cnn-new-ext0-4096-0.75-250k/default-model.keras file. Plots of the learning curves and the model structure are written to the plots sub-directory.

A detailed evaluation of any models can be invoked with the following command:

$ python3 model_testing.py [model_files ...]

This will initially evaluate model predictions against the synthetic-mist-tess-dataset and the formal-test-dataset. Subsequently it will run the full end-to-end testing of model predictions and JKTEBOP fitting against the formal-test-dataset. Testing output files and a log file will be written to a testing sub-directory alongside any tested models.

You can test the pre-built model, at ./ebop_maven/data/estimator/default-model.keras, by running model_testing without any arguments. In this case, the results will be written to the ./drop/training/published/testing/ directory.

Warning

The model structure and hyperparameters are still subject to change as ongoing testing and model searches continue to reveal improvements.

Model structure and hyperparameter search

A search over a range of model structures and hyperparameter values, using the hyperopt libarary's tpe.suggest algorithm, can be run with the following command:

$ python3 model_search.py

Warning

This will take a long time! As in hours, if not days.