Originally from Argentina, I got my MS in Astronomy from La Plata National University in 2005 and my PhD in Physics from University of Buenos Aires in 2010. During my graduate studies I focused on gravitational field theories and various extensions of General Relativity. Afterwards, I continued my work on theoretical physics and cosmology at Brandeis University. I joined Harvard-Smithsonian Center for Astrophysics in 2013 to work computational stellar astrophysics. In 2018, I became part of the Institute for Applied Computational Science at Harvard University, where I used machine learning algorithms to conduct data driven astrophysical research. Currently, I am an astrophysicist at Harvard-Smithsonian Center for Astrophysics, working for NASA's Chandra X-ray Observatory.
I am interested in stellar evolution, activity, X-ray emission, and rotation, as well as the impact that stars have on their orbiting planets: star-planet interaction. I use magnetohydrodynamic simulations and deep learning to study problems like the spin-down evolution of young, active stars, high-energy radiation effect on exoplanetary environments and their habitability, and the orbital evolution of cataclysmic variables. More recently I have been focusing on uncertainty quantification in deep neural networks. In particular, developing probabilistic models and bayesian neural networks to retrieve fundamental stellar properties from observable quantities.
Harvard-Smithsonian Center for Astrophysics
Stellar activity and rotation are two sides of the same coin. Rotation fuels activity by mixing the magnetized plasma in the convective envelope of the star, which visible symptom is its X-ray emission. In turn, the resulting magnetic fields determine the stellar winds through which the star spins down. This self-regulating mechanism results in a very powerful tool, called Gyrochronology, that allows us to convert rotation periods, that we can measure to great precision, into stellar ages, that are very difficult to determine.
The magnetic activity of a star and its evolution determines their high-energy radiation, that is crucial to the space weather of its close-in orbiting exoplanets, and understanding stellar winds can help us understand the evolution of close binary systems.
Activity, Rotation and X-ray emission
Open cluster observations have shown a bimodal behavior in rotation periods of young, active stars difficult to explain with current models of angular momentum loss. I model stellar winds to understand how the rate at which a star spins down evolves throughout its life. The X-ray emission will reflect of this evolution.
Energetic Radiation and Exoplanetary Environments
Stellar winds and high-energy radiation are a potential threat to close-in exoplanets' atmospheres. I model the coroane of planet hosting stars to asses their "habitability". I have studied the space environment of exoplanets orbiting M-Dwarfs and found that they are likely to suffer from strong atmospheric stripping and evaporation.
The orbital evolution of Cataclysmic Variables (CV), for periods longer than the period gap of ~3.2 hrs, is governed by their angular momentum loss through magnetized winds. I am interested in how the magnetic field alignment and complexity impacts their evolution, potentially explaining the existence of the period gap.
for Stellar Evolution
Neural Networks are known to outperform traditional methods for highly non linear problems. Stellar evolutionary tracks are an are complex and difficult to reverse in order to retrieve fundamental properties from observables like. I have built a fully connected neural network that fully characterizes a star directly from its observed photometry. In addition, I have built a Bayesian Neural Network to determine uncertainties in the model and predictions. I use this to improve the model where it is needed.
Skills & Specialties
Stellar astrophysics, high-energy radiation, X-ray emission, exoplanetary environments, and black hole physics. Machine learning, artificial neural networks, probabilistic programming, bayesian inference, MHD modeling, large data, and parallel computing.
Python, Pytorch, Pyro, IDL, Fortran, Jupyter Notebooks, Git, Colab.
Extreme Ultraviolet (EUV) stellar emission is crucial for planetary atmospheric evolution, since it controls the upper atmosphere's chemistry. Unfortunately, EUV stellar fluxes are highly unconstrained, and a dedicated mission to observe it would be a huge and necessary step in that direction. I am part of two mission concept to do so.
I am part of the science team of ESCAPE - EUV Stellar Characterization for Atmospheric Physics and Evolution - a NASA Small Explorer Concept in Phase A. - PI: Kevin France
I am the Deputy Principal Investigator for NExtUP - Normal incidence Extreme Ultraviolet Photometer - a NASA Smallsat Explorer Mission Concept - PI: Jeremy J. Drake
170 - 180 Å spectroscopy of nearby stars
170 - 180 Å narrow band photometry of fainter stars
by Jeremy J. Drake
A Complete Spin-Down Model that reproduces OC observations:
...and also explains the Cataclysmic Variables Period Gap!
Watch my keynote presentation on Stellar Activity and Rotation at the Einstein Fellowship Symposium:
Proxima b and TRAPPIST-1
On the habitability of our temperate terrestrial, rocky neighbors:
Our work suggests that such close-in planets can be exposed to strong dynamic pressure from the stellar wind, 2000 times stronger than the solar wind pressure at Earth or higher, and to fast variations of this pressure over timescales of days, creating conditions for potentially strong atmospheric stripping, heating and possible evaporation.
Simulation of the magnetic environment around the planet TRAPPIST-1 f
In addition, we find that close enough planets do not have magnetospheres protecting them from the intense stellar winds and magnetic pressures. Instead, stellar magnetic field lines connect with the planetary field over most of the planets surface, allowing energetic particles to constantly precipitate on to the atmosphere.
Garraffo et al. 2016(arXiv:1609.09076), Physics Today, Garraffo et al. 2017 (arXiv:1706.04617), selected as a AAS NOVA highlight in 2017 and Press release by CfA, 2017.
Watch my ITC talk on this "Proxima b and TRAPPIST-1: Check the Space Weather Before Packing"
My career path, from early age until now - Interview for Mentes Inquietas by Luciano Zafiro (in Spanish)
I enjoy the fresh perspectives that arise from working with students.
At CfA I have mentored several Masters students on projects ranging from Cataclysmic Variables mass transfer to exoplanets and the history of the Sun.
While IACS I have been involved in teaching two classes on Data Science (CS109/209A and B). I was a mentor at the YouthAstroNet program and for El Universo a Tus Manos Program for Undergraduate Science Majors at CfA, as well as an advisor for the Talented and Gifted STEM program for Latinas in the Boston Public Schools.
In addition, I am a teaching fellow and an invited Lecturer at Harvard, and I have been a teaching assistant for 6 years in Argentina.
The next generation
Latest Student Projects at IACS
Shu Xu, Yiming Xu and Ziyi Zhou
MS students, Harvard
Capstone project in collaboration with EHT. Probabilistic programming to decide when to trigger an observation
Using Machine Learning to Solve Einstein’s Equations
Uses Neural Networks to find new solutions to General Relativity.
Devansh Agarwal, PhD student, W. Virginia
Jamila Pegues, PhD student, Harvard
Ray-tracing code that uses interpolation and computational geometry to significantly speed up accurate ray-tracing of a given scene
Developing a Ray Tracing Model
Diversity and Inclusion
The scientific community (and our society at large) still faces the challenge of making research environments diverse and inclusive. As a woman and latina scientist I have faced unconscious biases at work and am committed to arise awareness to support and inspire young women in science as well as people from underrepresented minorities.
Since 2015 and until August 2018 I have been the organizer of the CfA Women in Science group meetings and coffee-hours aimed at women in early career stages.
Based on an idea by Mohaddesseh Azimlu, we came together for a group photo of the female astronomers at the CfA in February 2014. This was 100.75 years after the historic group picture of Annie Jump Cannon and her colleagues at the Harvard College Observatory, who worked on stellar spectral classifications, identifications of novae, and other astronomical topics. Here’s the photo of today’s female astronomers at the CfA, who work on topics ranging from exoplanets to star formation and cosmology to data visualization.
At CfA I strive to serve as a minority role model and advocate in a community that faces big challenges regarding gender balance and diversity. In an effort to broaden the scope of my impact, I am involved in outreach activities in developing countries, where my goal is to encourage young women and underprivileged minorities to pursue STEM careers in first rank Universities.
I am a mentor for Women in Data Science WiDS Cambridge Datathon 2021, an annual workshop preceding the WiDS Cambridge Conference. The workshop aims to provide mentorship and training for those interested in participating in the WiDS Datathon Challenge, and, more generally, anyone with a strong interest in data science
PhD in Physics, Mar 2010, University of Buenos Aires
M.S. in Astronomy, Dec 2005, National University of La Plata
Harvard-Smithsonian Center for Astrophysics, 2021 to present.
Research Associate, IACS Harvard University, 2018 - 2021.
Research Associate, Harvard-Smithsonian CfA, 2018 to 2021.
Postdoctoral Fellow, Harvard-Smithsonian CfA, 2013 - 2018.
Research Associate at Brandeis University, 2010 - 2013.
Doctoral Fellow of the National Scientific and Technological Research Council Argentina, 2006 - 2010.
Teaching experience of 6 years, mentoring for 3 years.
35 publications in scientific, peer reviewed journals (12 as a first author)
967 citations (as of Aug 2020)
I sing and play the bass guitar with Esteparios, a group of friends who get together to play latin music.
Find me here
60 Garden Street