Introduction to Astronomical Data Science

The stepping stones to explore the cosmos

All images are created by the author using DALLE3 or Midjourney

Since the dawn of human civilization, we have gazed at the night sky in awe and wonder. From the voyager probes to the James Webb Space Telescope, 100s of scientific systems are out there exploring the cosmos.

All of them have one thing in common. They gather data from beyond our world. All scientific discoveries that we make come from analyzing it. Today, this data can overflow into 1000s of TBs, and it’s all out there for anyone to explore and unravel the secrets of the universe.

There are three excellent opportunities here:

• Analyze archived astronomical data to find things that have been missed before.
• Build newer techniques or algorithms to do something faster and better. As the dataset grows, we must create efficient ways to handle and analyze it.
• Contribute to research groups and academic programs analyzing current data from live experiments and ongoing missions.

In this article, we take the first steps into that journey.

Where’s all the astronomical data?

• Observatories and Telescopes: Ground-based observatories like the Very Large Telescope (VLT) (images taken), space telescopes like Hubble or the James Webb, and radio telescopes like the Atacama Large Millimeter/submillimeter Array (ALMA) are significant sources.
• Space Agencies: Organizations like NASA, ESA, and others archive massive amounts of data from various space missions.
• Online Archives and Databases: Resources such as the NASA/IPAC Extragalactic Database (NED) and the Sloan Digital Sky Survey (SDSS) provide extensive catalogs accessible online.
• Scientific Data Repositories: The Astrophysics Data System (ADS) and arXiv host research papers and preprints, often with accompanying datasets.

The sources above are your starting point. So many other public datasets are available out there beyond this, too.

What does the data look like?

• Images: From raw telescope captures to processed composite images.
• Spectra: Data showing the intensity of light over various wavelengths, revealing chemical compositions, and more.
• Time Series: Observations of astronomical objects over time, crucial for studying variable stars, exoplanet transits, etc.
• Catalogs: Massive compilations of astronomical objects with various recorded properties (e.g., positions, magnitudes, temperatures).
• Simulations: Data from computational models simulating cosmic phenomena, like galaxy formation.

To get a real feel of the data, pick up any source, try to download it onto your computer, and check the actual files. You can get access to most data via the following paths.

• Public Archives: Access large public databases like the Mikulski Archive for Space Telescopes (MAST) or SDSS with online interfaces.
• APIs: Some data repositories and observatories offer APIs (Application Programming Interfaces) for more tailored data retrieval.
• FTP Servers: Certain datasets can be downloaded directly from FTP servers maintained by space agencies or observatories.
• Special Requests: For some specific or unreleased data, researchers may need to make special requests or proposals to observatories.
• Academic Collaboration: Joining established research teams or collaborations often provides access to otherwise restricted data.

Most of the access links are just a simple Google search or an email away.

What can you do with this data?

• Object Identification and Cataloging: Using data to locate and categorize astronomical objects (e.g., stars, galaxies, exoplanets).
• Spectral Analysis: Investigating spectra to determine chemical composition, mass, and velocity properties.
• Cosmological Studies: Analyzing large-scale data to understand the universe’s evolution, structure, dark matter, and dark energy.
• Time-Series Analysis: Studying changes over time to detect phenomena like supernovae, variable stars, or exoplanet transits.
• Astrostatistics: Applying statistical methods to interpret astronomical data and address cosmological questions.
• Machine Learning: Utilizing ML algorithms for tasks like image classification, anomaly detection, and predictive analysis.

Note: This list is not exhaustive.

Practical Examples

Let’s get hands-on and explore some applications. With each tutorial, we learn:

• How to build the intuition to think about an astronomical problem.
• How to get and process the required data.
• How to implement our approach/algorithm and apply it to the data.
• How to infer and document the results.

Here are some ideas:

1. Classifying Galaxies Using Hubble Telescope Images
2. Identifying Elements in a Star’s Spectrum
3. Monitoring a Variable Star
4. Analyzing Meteor Shower Frequencies
5. Detecting Exoplanets via Transit Method

Here are more examples for inspiration if you are looking for something more specific and complex.

• Analyze the distribution of dark matter in a specific galaxy cluster using gravitational lensing data.
  — Data: Gravitational lensing measurements and associated images of the selected galaxy cluster.
• Trace the growth and evolution of supermassive black holes over cosmic time.
  — Data: X-ray and radio observations of distant galaxies and quasars across various epochs.
• Can we detect patterns or periodicities in the mysterious fast radio bursts (FRBs) by analyzing a decade of radio observations?
  — Data: Archived radio telescope observations capturing FRB events across various frequencies.

Each of these problems offers unique insights into various aspects of our universe and holds the potential to reshape our understanding of cosmic phenomena.

We shall explore them in future tutorials where we can actually try to implement the above applications. #StayTuned.

(This article will be updated with the links to those tutorials once they are ready.)

Future directions

A lot of new missions are coming up, and this will explode the data we gather, calling in for newer techniques and more people to handle it. Here are some directions to look ahead in this space.

• Next-Generation Telescopes: Projects like the Vera C. Rubin Observatory will bring unprecedented volumes of data, necessitating advanced processing techniques.
• Interdisciplinary Approaches: Combining astrophysics with fields like data science and machine learning (or bio-inspired algorithms) to handle complex data analysis and find new types of patterns.
• Automated Systems: Developing more sophisticated AI and ML systems for real-time data processing and anomaly detection.

Want to explore more and collaborate on projects? Find me on LinkedIn or Twitter!

Feel free to leave your thoughts and ideas in the responses.

Read a similar analysis I did on bioinformatics here.