Exoplanet Detection Using the Transit Method

Overview

This project focuses on detecting🔍🔭 exoplanets🪐🌍 using the transit method, a technique that identifies planets outside our solar system through the temporary dimming of a star as an exoplanet passes in front of it. By using the change in flux (light intensity) data of several thousand stars, Spiking Neural Networks (SNNs) are applied to efficiently process and analyze the observational data.

Data Description

The dataset comprises observations of several thousand stars, each annotated with a binary label:

• 2 indicates a star confirmed to have at least one exoplanet in orbit, potentially hosting multi-planet systems.
• 1 denotes stars without confirmed exoplanets.

These labels are derived from continuous monitoring of stars' light flux over extended periods, looking for regular dimming patterns indicative of orbiting bodies. Such stars are initially considered 'candidate' systems, with further investigations required to 'confirm' the presence of exoplanets.

If you want to download the data please click here

Objectives

• Efficient Data Analysis: Implement Spiking Neural Networks (SNNs) to analyze star flux data with minimal energy consumption, making the process suitable for space-based observations.
• Exoplanet Detection: Identify potential exoplanet-hosting stars from flux measurements, focusing on those with dimming patterns consistent with planetary transits.
• Validation of Candidates: Classify stars into candidates and confirm systems based on their light intensity patterns, aiding in the prioritization of stars for further study.

Results

The implementation of Spiking Neural Networks (SNNs) for the analysis of stellar flux data has yielded promising results:

• Test Loss: 0.10, indicating a low error rate in the predictions made by the model.
• Test Accuracy: 97.85%, showcasing the model's high reliability in classifying stars correctly as hosting or not hosting exoplanets.
• Sensitivity: 40.00%, reflecting the model's capability to identify true positives, though it suggests a need for improvement in detecting stars with exoplanets more effectively.
• Specificity: 98.42%, demonstrating the model's strength in correctly identifying stars that do not host exoplanets, thereby minimizing false positives.
• AUC-ROC: 81.1834%, indicating a good ability of the model to distinguish between classes, though there is potential for further refinement to improve sensitivity without significantly reducing specificity.

These results underscore the efficacy of SNNs in processing astronomical data for exoplanet detection, particularly under the constraints of space-based observations. The high accuracy and specificity are encouraging, though the sensitivity indicates a challenge in detecting all true exoplanet-hosting stars, highlighting an area for future research and model optimization. The AUC-ROC score supports the model's overall discriminative capability, suggesting a balanced performance across various decision thresholds.

Further Information

For more insights into the transit method and its role in exoplanet exploration, visit NASA's Exoplanet Exploration Page.

How to install dependencies

Declare any dependencies in src/requirements.txt for pip installation and src/environment.yml for conda installation.

To install them, run:

pip install -r src/requirements.txt

How to run your Kedro pipeline

You can run your Kedro project with:

kedro run

How to test your Kedro project

Have a look at the file src/tests/test_run.py for instructions on how to write your tests. You can run your tests as follows:

kedro test

To configure the coverage threshold, go to the .coveragerc file.

Project dependencies

To generate or update the dependency requirements for your project:

python -m piptools compile --upgrade --resolver backtracking -o src/requirements.lock src/requirements.txt -v

pip install -r src/requirements.lock

This will pip-compile the contents of src/requirements.txt into a new file src/requirements.lock. You can see the output of the resolution by opening src/requirements.lock.

After this, if you'd like to update your project requirements, please update src/requirements.txt and re-run kedro build-reqs.

How to work with Kedro and notebooks

Note: Using kedro jupyter or kedro ipython to run your notebook provides these variables in scope: catalog, context, pipelines and session.

Jupyter, JupyterLab, and IPython are already included in the project requirements by default, so once you have run pip install -r src/requirements.txt you will not need to take any extra steps before you use them.

Jupyter

To use Jupyter notebooks in your Kedro project, you need to install Jupyter:

pip install jupyter

After installing Jupyter, you can start a local notebook server:

kedro jupyter notebook

JupyterLab

To use JupyterLab, you need to install it:

pip install jupyterlab

You can also start JupyterLab:

kedro jupyter lab

IPython

And if you want to run an IPython session:

kedro ipython

How to convert notebook cells to nodes in a Kedro project

You can move notebook code over into a Kedro project structure using a mixture of cell tagging and Kedro CLI commands.

By adding the node tag to a cell and running the command below, the cell's source code will be copied over to a Python file within src/<package_name>/nodes/:

kedro jupyter convert <filepath_to_my_notebook>

Note: The name of the Python file matches the name of the original notebook.

Alternatively, you may want to transform all your notebooks in one go. Run the following command to convert all notebook files found in the project root directory and under any of its sub-folders:

kedro jupyter convert --all

How to ignore notebook output cells in git

To automatically strip out all output cell contents before committing to git, you can run kedro activate-nbstripout. This will add a hook in .git/config which will run nbstripout before anything is committed to git.

Note: Your output cells will be retained locally.