Lalitha Sairam

Exoplanets | Stellar Activity | Circumbinary planets | Star-planet interaction | Multi-wavelength observer

Institute of Astronomy, University of Cambridge

About mE

Have you ever wondered if there are other worlds out there? As an Astrophysicist at the University of Cambridge, I am dedicated to finding the answer. My passion lies in exoplanetary science – the search for planets orbiting distant stars.

My journey began with exploring how stars influence the fate of their orbiting planets. This fascination led me to study stellar flares using X-ray astronomy. Throughout my career, I have become an expert at finding the faint “signal” of an exoplanet amidst the “noise” of stellar activity.

Stellar activity is a major hurdle in exoplanetary science, making it difficult to detect and characterise the atmospheres of distant worlds. To address this challenge, I am developing STACCATO, a robust model that forecasts stellar activity and allows for more efficient exoplanet observations. This paves the way for future studies aimed at characterising these intriguing exoplanets.

After successfully discovering planets around single stars, I am now tackling the complexities of binary star systems. I have developed methods for high-precision radial velocity measurements, with the goal of detecting planets like Tatooine from Star Wars – those that orbit two suns.

I specialise in exoplanetary science, but my research also delves into X-ray astronomy, stellar coronae, and magnetic activity. I am a continuous learner, eager to build on my academic foundation and push the boundaries of exoplanet discovery.

In my free time, I unwind with photography, running, and classical dance.

ReseArch Interests

Stellar activity

Stellar activity provides with some interesting puzzles. My work focuses on understanding and mitigating the effects of stellar magnetic activity on exoplanet detection and characterisation. I use multiwavelength data to study flares, spots, prominences, and variability cycles. I have developed Gaussian Process models, activity forecasts, and machine learning tools to separate stellar signals from planetary ones — across both radial velocity and transmission spectroscopy datasets. My current work targets stellar contamination in JWST spectra to improve atmospheric retrievals for planets around low mass stars.

EXoplanets around stars

M dwarfs are small, cool stars—but they’re central to the search for Earth-like planets. They dominate the stellar population in our neighbourhood and offer the best chance of detecting habitable-zone worlds with current instruments. My work focuses on discovering and characterising planets around these stars, where stellar activity often masks or mimics planetary signals. I’ve developed methods to pull the planet out of the noise—whether through radial velocities, transits, or atmospheric studies. The goal is clear: find small planets around small stars, and understand what makes them special.

CIRCUMBINARY PLanets

Nearly half the stars in our galaxy are in binaries, yet circumbinary planets—those that orbit both stars—are rare discoveries. Not because they don’t exist, but because they’re hard to find. The usual tools struggle with the complex, blended spectra of double-lined binaries. My work focuses on solving this. I have developed a machine learning technique to extract clean radial velocities from these systems without prior assumptions. This opens up a new path to detect planets in the most common stellar environments—and ask how planet formation works when there are two suns instead of one.

ongoing projects

STACCATO

STACCATO is my ongoing project to forecast stellar magnetic activity and optimise when we observe stars for planet detection. Stellar noise is one of the biggest barriers to finding small, Earth-like planets—especially around Sun-like stars. Instead of treating it as random interference, STACCATO predicts when a star is in its quiet phase, making it the best time to observe. The goal is to build a robust, activity-aware scheduling system that improves detection sensitivity and becomes a standard part of how we plan exoplanet observations.

DOLBY

DOLBY is a machine learning-based model I developed to extract radial velocities from double-lined binary stars—systems that have long been excluded from planet searches due to their complex, blended spectra. Traditional methods can’t handle the level of spectral overlap or the additional noise these systems introduce. DOLBY takes a data-driven approach, requiring no prior knowledge of the binary, and delivers precise RVs that make planet detection possible in these environments. With DOLBY, we open up an entirely new class of stars to radial velocity planet searches—bringing circumbinary systems into the fold.