Zoë L. de Beurs

Intro

Hello! I’m a senior undergraduate studying physics, math, and astronomy at the University of Texas at Austin. My honors thesis is being supervised by Prof. Andrew Vanderburg and is focused on using machine learning to remove stellar activity noise to reveal previously hidden planets orbiting bright, nearby stars. In the future, I aspire to lead a large research group as a professor that combines exoplanet research with cutting-edge artificial intelligence (AI) techniques. I’m interested in fusing developments in computational and experimental astrophysics to accelerate exoplanet discoveries – in particular those planets that are similar in size and composition to our earth. You can read more about my research here! Beyond research, I also love doing public outreach and advocating for improved equity, representation, and inclusion in astronomy.

When I'm not doing science, I also enjoy drinking copious amounts of coffee, reading books by my favorite author Chimamanda Ngozi Adiche, hiking, and baking Dutch desserts. My pronouns are she/hers.

Research

Current work: Using machine learning to remove stellar activity signals from RVs

Future large space missions designed to search for biosignatures in the atmospheres of Earth-like exoplanets will operate more efficiently and have a higher chance of success if stars with possible Earth analogs are known before launch. One way to find these Earth-like candidates is with the radial velocity (RV) technique, which measures the Doppler shift of the star's spectral lines as the planet tugs on the star in its orbit. The RV method has been used to discover and characterize planets for decades. As our instruments have become more stable and precis, we have gotten better and better at measuring these tiny shifts, but we have not continued finding smaller and smaller planets with RVs.

This is primarily because RV method is currently limited by spurious signals introduced by stellar activity (i.e. faculae, starspots). These inhomogenities on the star’s surface introduce shape changes to the spectral lines that have can mimic and hide the RV signals of small planets.

Previous efforts to solve this problem have focused on carefully filtering out activity signals in time using Gaussian process regression (e.g. Haywood et al. 2014), but this approach requires high-cadence observations and can be difficult to schedule on telescopes. Instead, we’ve focused on another possible solution: machine learning and neural networks. We separate activity signals from true center-of-mass RV shifts using only changes to the average shape of spectral lines, and no information about when the observations were collected.

In practice, we can visualize this process with the plot below where the curves in the top panel ae the changes in average line shape for the Sun (Observed with HARPS-N Solar Telescope) and the bottom panel output the corresponding stellar activity prediction produced by the neural network. Both panels are color-coded by the apparent shift due to stellar activity.

Overall, we demonstrated our technique on simulated data, reducing the RV scatter from 82.0 cm/s to 3.1 cm/s , and on approximately 700 observations taken nearly daily over three years with the HARPS-N Solar Telescope, reducing the RV scatter from 1.47 m s/1 to 0.78 m s/1 (a 47% or factor of ~ 1.9 improvement). In the future, these or similar techniques could remove activity signals from observations of stars outside our solar system and eventually help detect habitable-zone Earth-mass exoplanets around Sun-like stars. In this way, improvements in RV precision could significantly accelerate the characterization of habitable zone Earth-sized exoplanets.

Publications

Refereed/under review

1. de Beurs, Zoe L., Islam, N., Gopalan, G., & Vrtilek, S.D. A Comparative Study of Machine Learning Methods for X-ray Binary Classification. Submitted to ApJ.
2. de Beurs, Zoe L., Vanderburg, A., Shallue, C.J., et al. Identifying Exoplanets with Deep Learning. IV. Removing Stellar Activity Signals from Radial Velocity Measurements Using Neural Networks. Submitted to AJ. https://arxiv.org/abs/2011.00003

Also see publications indexed by ADS.

Talks

For a full list of talks and poster presentations, please see my CV .

Recent Live-Recorded Talks

1. de Beurs, Zoe L. et al. “Removing Stellar Activity from Radial Velocities Using AI,” UT Astronomy Undergraduate Summer Symposium, Virtual Meeting, August 14, 2020. Watch online (starting at 16:20) at this link
2. de Beurs, Zoe L. et al. “Removing Stellar Activity Signals from RVs Using Neural Networks,” 236th American Astronomical Society Meeting, Virtual Meeting, June 1, 2020. See live-recorded talk below:
3. de Beurs, Zoe L. et al. “AstroAI: How Machine Learning Can Help Us Understand the Universe,” Special Undergraduate Show, Astronomy on Tap ATX, Virtual Talk (900+ views), May 19, 2020. Watch online below (starting at 11:49):
4. de Beurs, Zoe L. et al. “Classifying X-ray Binaries Using Machine Learning,” SAO Astronomy Summer Intern Symposium 2019, Harvard-Smithsonian Center for Astrophysics, Cambridge, MA, August 8, 2019. See live-recorded talk below:

Recent Poster Presentations

1. de Beurs, Zoe L. et al. “Removing Stellar Activity from RVs Using Artificial Intelligence,” Exoplanets III hosted by Heidelberg University and MPIA, Virtual Conference, July 27, 2020, See online poster here .
2. de Beurs, Zoe L. et al. “Removing Stellar Activity from RVs Using Artificial Intelligence,” 2020 Sagan Exoplanet Summer Virtual Workshop, NASA Exoplanet Science Institute, July 20-24, 2020. See online poster and 2-minute video explanation below:

CV

Download CV here

Last updated December 23, 2020

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