WASP-39b Light Curve and Planet Spectra
Recently, I worked on a cool project where I plotted the light curve of the star WASP-39, during which its exoplanet — the hot Jupiter WASP-39b — was also observed. According to NASA’s exoplanet archive, WASP-39b has about 0.28 times the mass of Jupiter and a radius 1.27 times that of Jupiter (~91,000 km).
It’s a hot gas giant with a scorching temperature of around 900 °C. The planet orbits very close to its host star (about 7 million km away) and completes one orbit in just 4 days.
WASP-39b was the first exoplanet discovered to contain carbon dioxide in its atmosphere, and sulfur dioxide was also detected. It lies in the Virgo constellation and is about 700 light-years away from Earth. As part of the NameExoWorlds campaign for the IAU’s 100th anniversary, the planet was officially named Bocaprins, after Boca Prins beach in Aruba’s Arikok National Park. The star it orbits is WASP-39.
What Is a Light Curve?
In simple terms, a light curve is a plot of incoming light (photon counts or flux) versus time. It shows how the brightness of a star changes over a period (called exposure time). Telescopes observe these light curves by continuously watching a star.
One fascinating use of light curves is to detect exoplanets. When a planet passes in front of its star (a transit), it causes a small dip in the light curve — and that’s how astronomers discover new worlds!
Learn more about this method here.
What Are Planet Spectra?
When telescopes use CCD detectors, they can measure not just when photons arrive, but also their energy and count. Using this information, we can create a spectrum of the planet — which is basically a plot of photon flux (or counts) vs wavelength.
This tells us what elements or molecules are present in the planet’s atmosphere. Want to go deeper? Check out this article on planetary spectroscopy.
Datasets and Project Implementation
The dataset for this project came from the James Webb Space Telescope (JWST) using its NIRSpec instrument, specifically in Bright Object Time-Series (BOTS) mode. For more details, visit the official JWST documentation.
The data was downloaded from the MAST Portal and came in FITS format, which is the standard for astronomy data.
The project was done entirely in a Jupyter Notebook, and here are some of the interesting visualizations I generated:
Light Curve and Poisson Regression Fit
Transmission Spectroscopy
When a planet transits, some starlight passes through its atmosphere. Different wavelengths of light are absorbed by atmospheric molecules, causing variations in the transit depth. Plotting these variations against wavelength produces a transmission spectrum, which reveals details about the atmosphere’s composition. For a clear explanation, watch this video.
Transmission Spectrum of WASP-39b
The spectrum above shows how WASP-39b’s atmosphere affects light at different wavelengths.
Detecting Atmospheric Composition
To determine the atmospheric composition, we use atmospheric retrieval. This involves comparing observed data to model spectra generated with known compositions. The model that best fits the data indicates the likely atmospheric makeup. For more insights, refer to this video.
Composition of WASP-39b’s Atmosphere
As a hot Jupiter, WASP-39b is expected to contain gases like methane and carbon dioxide. Analysis confirms the presence of water, sulfur dioxide, methane, and carbon dioxide. A distinct bump in the transmission spectrum between 4–5 µm confirms carbon dioxide.
Pressure-Temperature Profile
The retrieval process assumes a Guillot pressure-temperature profile to model the atmosphere’s temperature structure.
Molecular Abundance
The abundance of molecules like water, methane, carbon dioxide, and sulfur dioxide varies with atmospheric pressure, as shown below.
The transmission spectrum highlights specific absorption features for each molecule.
REEFRENCES:
Project Repository
You can check out the complete code and analysis on GitHub:
👉 JWST Light Curve and Planet Spectra