Gaia Hackathon Project 2025

Tori Fowler, Hayden Tedrake, Damian Gold

README: Astronomical Data Clustering and Hertzsprung-Russell Diagram

Overview

This project analyzes astronomical data from Gaia, focusing on clustering stellar objects using DBSCAN and visualizing their spatial distributions. Additionally, it generates a Hertzsprung-Russell (HR) diagram to classify stellar populations.

Requirements

Ensure you have the following Python packages installed:

• pandas
• numpy
• scikit-learn
• matplotlib
• mpl_toolkits
• astropy

Install missing dependencies using:

pip install pandas numpy scikit-learn matplotlib astropy

Data Files

• 3k_gaia_kin_data.csv: Contains Gaia astrometric data including RA, Dec, proper motions, and parallax.
• all_w_rv_kin_3k.fits: FITS file containing stellar photometric and astrometric data.

Functionality

1. Parallax-Based Distance Calculation

Converts parallax measurements into distances from the Sun in parsecs.

2. Data Normalization

Scales astronomical data for clustering using StandardScaler.

3. DBSCAN Clustering

Identifies stellar clusters based on position and motion data, appending cluster labels to the dataset.

4. Coordinate Transformations

• Cartesian Coordinates (X, Y, Z): Converts RA, Dec, and distance into 3D Cartesian coordinates.
• Galactic Coordinates (l, b): Converts RA and Dec into galactocentric longitude and latitude.

5. Hertzsprung-Russell Diagram

• Computes absolute magnitudes (G_abs) using parallax.
• Computes color indices (Bp-Rp).
• Categorizes stars into:
  • Main Sequence
  • Red Giants
  • White Dwarfs
• Visualizes the HR diagram with temperature on the x-axis and absolute magnitude on the y-axis.

Usage

Running the Clustering Analysis

Execute the script to:

• Load 3k_gaia_kin_data.csv
• Compute distances and normalize data
• Apply DBSCAN clustering
• Transform to Cartesian and Galactic coordinates
• Visualize clustered stars in 2D and 3D

python cluster_algorithm_and_plot.py

Generating the HR Diagram

Execute the script to:

• Load all_w_rv_kin_3k.fits
• Compute absolute magnitudes
• Classify stellar populations
• Plot the HR diagram

python hertzsprung_russel.py

Visualization Outputs

• 2D Galactic Coordinate Plot: Stars are colored by their DBSCAN cluster.
• 3D Cartesian Plot: Spatial distribution of clusters.
• HR Diagram: Stellar classification based on absolute magnitude and color index.

Notes

• Ensure the required FITS and CSV files are available in the working directory.
• Modify clustering parameters (eps, min_samples) to optimize DBSCAN performance.
• In the HR diagram, hotter stars appear on the left (temperature axis is inverted).

License

Copyright (c) 2025 Hayden Tedrake Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.