Exploring the Stars: A Data-Driven Guide to Gaia DR3 in Celebration of Astronomy Day

The universe has always held mysteries, and with every new observation, we inch closer to understanding its secrets. This project, born in celebration of Astronomy Day, is my humble attempt to touch the stars through data. Etheria Luminis: The Stellar Path of Light is the name I’ve given to this exploration, where data science meets the cosmos, and where the vastness of the universe unfolds one star at a time.

While the official day passed, the stars remain—unchanged, waiting to be discovered again and again, no matter how late we arrive.

The dataset from Gaia DR3 offers us a glimpse into a universe that is vast, intricate, and profoundly beautiful. It is through this lens of data that we can begin to unravel the stories that the stars have been telling for billions of years. This tutorial is both a scientific exploration and an invitation to anyone who, like me, feels drawn to the skies—not just to analyse the numbers, but to stand in awe of what they represent.

While I take pride in the rigor of this analysis, I am also deeply aware that science is a journey. Sometimes, arriving late allows for reflection, and in that space, we find our most profound insights.

Step 1: Accessing Gaia DR3 Data – A Glimpse into the Cosmos

The Gaia DR3 catalogue is like an ancient map of the stars, meticulously charted by the universe itself. Through this dataset, we are offered a window into the positions, distances, and movements of over a billion stars. By selecting just a thousand of these celestial objects, we begin to scratch the surface of this stellar expanse.

Using the power of Python and Astroquery, I initiated my search with the following query, drawing down stars based on their apparent brightness:

from astroquery.gaia import Gaia
query = "SELECT * FROM gaiadr3.gaia_source WHERE phot_g_mean_mag < 18 LIMIT 1000"
job = Gaia.launch_job(query)
results = job.get_results()        

The dataset is immense, so this selection narrows our focus to the brightest stars, those that shine through the cosmic distances to reach us. Each one represents a story of light, distance, and time, spanning the vastness of the universe.

Step 2: Data Cleaning and Preparation: Filtering the Light

As with any large dataset, cleaning the data is essential to ensure meaningful analysis. Stars with missing values for key attributes like parallax and photometric magnitude were removed, allowing us to work with complete and reliable data:

df = results.to_pandas()
df_clean = df.dropna(subset=['parallax', 'phot_g_mean_mag'])
df_clean['parallax'] = pd.to_numeric(df_clean['parallax'], errors='coerce')        

Cleansing the data is much like refining a telescope’s lens—it sharpens our view and allows us to study the stars with greater precision. Each value we retain brings us closer to the true nature of the universe.

Step 3: Visualizing Stellar Positions: Mapping the Cosmic Landscape

The positions of stars, expressed through their Right Ascension and Declination, reveal a two-dimensional map of the night sky. I visualized this celestial map to explore the distribution of stars in our study:

plt.figure(figsize=(10, 6))
plt.scatter(df_clean['ra'], df_clean['dec'], s=1, color='red')
plt.title(r'\textbf{Stellar Positions in Gaia DR3}', fontsize=18)
plt.xlabel(r'\textit{Right Ascension (degrees)}', fontsize=14)
plt.ylabel(r'\underline{Declination (degrees)}', fontsize=14)
plt.grid(True)
plt.show()        

This map, reminiscent of ancient star charts, shows the stars as they are scattered across the sky. Though they may seem randomly placed, their positions tell a deeper story of cosmic forces and time. It reminds us that while the stars appear fixed in the sky, they are constantly moving in the dance of the universe.

Step 4: Distance Distribution: A Journey Through Space

To better understand the stars’ locations relative to us, I focused on their distances, visualized through a distribution plot. By narrowing the analysis to stars within a 100-parsec radius, I was able to highlight the concentration of stars at various distances:

plt.hist(df_clean['parallax'], bins=50, color='blue', edgecolor='black')
plt.title('Distribution of Stars by Distance (Parallax)')
plt.xlabel('Distance (parsecs)')
plt.ylabel('Number of Stars')
plt.show()        

The majority of stars are located between 30 and 70 parsecs from Earth. This visualization helps us understand the structure of our stellar neighbourhood, revealing the density of stars as we move outward through the galaxy. The closer stars—our celestial neighbours—appear most frequently in the data, while the more distant ones grow sparser.

Step 5: Further Analysis: The Hertzsprung-Russell Diagram – A Stellar Tale

One of the most powerful tools in stellar astrophysics is the Hertzsprung-Russell (H-R) diagram, which plots stars based on their absolute magnitude and parallax. This allows us to see the life stages of stars, from young, hot, and bright to older, cooler, and fainter.

Using the Gaia DR3 data, I created an H-R diagram to explore the stellar population:

plt.figure(figsize=(10, 6))
plt.scatter(
    df_clean['parallax'], 
    df_clean['absolute_magnitude'], 
    c=df_clean['phot_g_mean_mag'], cmap='coolwarm', s=10
)
plt.colorbar(label='Apparent Magnitude (G-band)')
plt.gca().invert_yaxis()
plt.title('Hertzsprung-Russell Diagram', fontsize=16)
plt.xlabel('Parallax (milliarcseconds)', fontsize=12)
plt.ylabel('Absolute Magnitude (G-band)', fontsize=12)
plt.grid(True)
plt.show()        

The H-R diagram shows a clear distribution of stars along the main sequence, a line where stars spend most of their lives, burning hydrogen into helium. We also observe the red giants, stars that have exhausted their core hydrogen, shining with less intensity but growing larger and cooler. The parallax values help us estimate their distances, allowing us to see the relationship between their brightness and their stage in life. This diagram is a map of stellar evolution, each point representing a different chapter in the life of a star.

Step 6: Kinematic Analysis: Proper Motion vs Radial Velocity – The Stellar Dance

Stars move through space with two key components: proper motion (movement across the sky) and radial velocity (movement toward or away from us). By comparing these, we can infer a star’s kinematic behaviour and whether it aligns with our solar system or exhibits independent motion.

I plotted the relationship between proper motion in Right Ascension and radial velocity to identify patterns in stellar motion:

plt.figure(figsize=(10, 6))
sns.scatterplot(x=df_clean['pmra'], y=df_clean['radial_velocity'])
plt.title('Proper Motion in Right Ascension vs Radial Velocity')
plt.xlabel('Proper Motion in Right Ascension (pmra)')
plt.ylabel('Radial Velocity (km/s)')
plt.grid(True)
plt.show()        

Most stars exhibit proper motions close to zero in both right ascension and declination, clustering near 0 km/s in radial velocity. This suggests that a significant portion of the stars share similar kinematic behaviour, likely due to their proximity to or alignment with the movement of our solar system. However, several outliers deviate significantly, showing higher values of proper motion and radial velocity. These stars present an intriguing opportunity for further study, as their kinematic differences hint at unique orbits, distances, or origins.

Discussion

The analysis of Gaia DR3 data reveals distinct patterns and intriguing anomalies. Most stars within the dataset adhere to expected stellar evolution paths, as seen in the Hertzsprung-Russell diagram. The clear delineation between the main sequence and red giants emphasizes the consistency of stellar life cycles, with stars following predictable brightness and parallax patterns.

The kinematic analysis further supports this, with most stars showing minimal deviation in proper motion and radial velocity. This suggests a strong alignment with our solar system's motion, likely due to the relative proximity of these stars. However, the identification of outliers—stars with significantly higher values in proper motion and radial velocity—raises compelling questions. What causes these stars to deviate from the norm? Are they remnants of different stellar populations or results of unique gravitational interactions?

These outliers present an important avenue for future investigation. Their unusual behaviour may shed light on stellar dynamics, galactic structure, or even undiscovered forces influencing their movement. While the broader dataset offers valuable insights into stellar regularity, these deviations are the key to unlocking deeper understanding of the cosmos.

Conclusion

The Gaia DR3 dataset provides a rich framework for studying the life cycles and kinematics of stars. Through the Hertzsprung-Russell diagram, we see the clear stages of stellar evolution, each star a point on a grand timeline of cosmic change. The proper motion and radial velocity analysis further reveals the dynamic nature of stars within our galaxy, with most stars sharing kinematic similarities due to their alignment with the solar system’s motion.

However, it is the outliers—those stars that deviate from the expected patterns—that offer the greatest potential for discovery. These stars, with their unusual motions, suggest that there is still much to learn about the forces shaping our galaxy. Their study could lead to revelations about galactic dynamics, star formation histories, or even new celestial phenomena.

Etheria Luminis: The Stellar Path of Light has illuminated not just the typical behaviours of stars, but the intriguing mysteries that lie in their anomalies. This journey is but the first step in a larger exploration of the universe’s most profound secrets.

As this is my first astronomical study, I recognize that there may be errors or areas for improvement. I invite anyone with expertise or a shared passion for the cosmos to review my work. Feel free to address me if you notice anything that might not be accurate—I am eager to learn and grow from this experience.

The full analysis, along with the code and visualizations, is available in my GitHub repository. Let’s start a conversation, share insights, and continue exploring the stars together.