This webapp enabled scientists to quickly classify certain types of galaxy spectra. Through an intuitive user interface, scientists build a training set for automatic galaxy classification and can curate the results of expiremental automatic classification algorithms.
Is it possible to use machine learning to reliably identify 'fossil' black holes in galaxies?
A 'fossil' black hole exists in a galaxy with large amounts of Helium II (He II). We can easily write a script to filter out graphs without He II, BUT galaxies with Wolf-Rayet (WR) stars also have He II. WR stars leave a 'bump' in the graph at a specified interval, but the bump is not well defined. There is no known way to calculate whether a graph has this WR bump or not. That's where machine learning comes in. We want to see if the WR bump can be found using a neural net. Using machine learning to find the WR bump in graphs will allow us to subtract WR bump graphs from the He II graphs. Thus we will have a list of galaxies with He II and no WR stars, leaving us with galaxies that have 'fossil' black holes.
For more details, please see ML_Info/Project_Information.pdf
- After login, user sees two modules: 'module1' and 'module2' (we can change the names later)
- Training page allows a user to classify whether a shown spectra has a WR bump or not. This human labeled data is then added to the known WR bump data set that is used to train the classification models.
- Varification page allows a user currate the results of the expiremental classification models. They are shown the spectra and the machines prediction and are asked to confirm or deny the results.
The app is built using Flask and Plotly.
With Python3.6 installed on your machine the following sequence of commands will get the development app running.
git clone https://github.com/codeforgoodconf/black_hole_frontend
cd black_hole_frontend
python -m venv venv
source venv/bin/activate
pip install -r requirements.txt
python -m scripts.seeds
python run.py