Avraham Binnenfeld

Postdoctoral Researcher @ EPFL

Astronomy & Planetary Science

About

I am a postdoctoral researcher at EPFL (École Polytechnique Fédérale de Lausanne) Institute of Physics, working with Prof. Richard Anderson at the Geneva Observatory. In December 2026, I will join Princeton University as a Rothschild Fellow, working with Prof. Josh Winn.

I develop methods to extract planetary and stellar signals from large astronomical surveys. My work combines Gaia, TESS, and spectroscopy to discover planets, characterize stellar systems, and study their orbital architectures.

Prior to that, I completed my PhD in astronomy and planetary sciences under the supervision of Prof. Shay Zucker, as part of Prof. Tsevi Mazeh's research group.
I am an active member of the Gaia Data Processing and Analysis Consortium (DPAC) and the TESS Follow-up Observing Program (TFOP).

publications

Research Interests

My research focuses on time-domain astrophysics, especially exoplanets, stellar variability, and periodicity detection in large astronomical datasets. I work with photometric, spectroscopic, and astrometric data, developing and applying methods for problems such as exoplanet detection, binary-star characterization, and star–planet interactions.

Much of my work is connected to the Gaia misson, where I am a member of the Data Processing and Analysis Consortium (DPAC). I have developed statistical and machine-learning tools for Gaia time-series data and work on exoplanet detection and characterization using Gaia photometry and astrometry.

I am also a member of the TESS Follow-up Observing Program (TFOP) and work on the Gaia–TESS collaboration. At EPFL, I study time-domain spectroscopy in the context of the planned Wide-field Spectroscopic Telescope (WST).

The SPARTA Code Library

Publicly available SPectroscopic vARiabiliTy Analysis python tools

The SPARTA library for SPectroscopic vARiabiliTy Analysis is a collection of tools designed to analyze periodically-variable spectroscopic observations.

The library is written in Python. It is easy to install and includes multiple example notebooks and tutorials.

It includes our user-friendly implementation for the PDC periodograms:
multiple model-independent tool for periodicity detection in photometric, spectroscopic, or astrometric time series.

Developed with Dr. Sahar Shahaf.

News

Press coverage for various projects by past and current research groups