High-Performance Computing
Many problems in modern exoplanet science – forward-modeling planetary populations, fitting high-resolution spectra, running ensembles of N-body integrations, simulating granulation across the solar disk – require significant computational resources. Our group develops high-performance computing tools and techniques, with an emphasis on the Julia programming language, GPU computing, and parallel algorithms, and we make our codes freely available.
Key Projects
Population Synthesis Computing
Forward-modeling planetary populations requires jointly sampling over millions of synthetic planetary systems and comparing them to survey data. Our group developed the ExoplanetsSysSim ecosystem (ExoplanetsSysSim.jl, SysSimExClusters, SysSimPyMMEN) in Julia, exploiting surrogate models, multi-threading, and distributed computing to make demographic inference with state-of-the-art population models tractable on the Penn State Roar Collab cluster.
Analysis of Extremely Precise Radial Velocity Observations
Extracting precise radial velocities from high-resolution spectra involves fitting thousands of spectral lines across hundreds of observations per star. This is computationally intensive even for a single target, and demanding at survey scale. Our RvSpectML ecosystem provides Julia tools for line-by-line modeling, telluric correction, and stellar variability characterization.
Julia Ecosystem Contributions
Beyond our science-domain packages, our group contributes general-purpose Julia tools that benefit the broader community. For example, ExpectationMaximizationPCA.jl provides robust PCA for data with missing values, useful for large spectral datasets. PlutoTeachingTools.jl extends the Pluto notebook environment with interactive pedagogical widgets, supporting reproducible, computation-heavy course materials in astrophysics and data science.
Selected Publications
- The Future of Artificial Intelligence and the Mathematical and Physical Sciences (AI+MPS)
Ferguson, Andrew et al. (2025), arXiv e-prints, arXiv:2509.02661. abstract doi - Data-Driven Modeling of Telluric Features and Stellar Variability with StellarSpectraObservationFitting.jl
Gilbertson, Christian et al. (2024), arXiv e-prints, arXiv:2408.17289. abstract doi - GRASS. II. Simulations of Potential Granulation Noise Mitigation Methods
Palumbo, Michael L. et al. (2024), AJ, 168, 46. abstract doi - GRASS: Distinguishing Planet-induced Doppler Signatures from Granulation with a Synthetic Spectra Generator
Palumbo, III, Michael L. et al. (2022), AJ, 163, 11. abstract doi - Toward Extremely Precise Radial Velocities. II. A Tool for Using Multivariate Gaussian Processes to Model Stellar Activity
Gilbertson, Christian et al. (2020), ApJ, 905, 155. abstract doi - Quantifying the Bayesian Evidence for a Planet in Radial Velocity Data
Nelson, Benjamin E. et al. (2020), AJ, 159, 73. abstract doi - The efficiency of geometric samplers for exoplanet transit timing variation models
Tuchow, Noah W. et al. (2019), MNRAS, 484, 3772-3784. abstract doi - Geometric adaptive Monte Carlo in random environment
Papamarkou, Theodore, Lindo, Alexey, Ford, Eric B. (2016), arXiv e-prints, arXiv:1608.07986. abstract doi - Empirically Derived Dynamical Models for the 55 Cancri and GJ 876 Planetary Systems
Nelson, Benjamin E. et al. (2014), 310, 93-95. abstract doi - RUN DMC: An Efficient, Parallel Code for Analyzing Radial Velocity Observations Using N-body Integrations and Differential Evolution Markov Chain Monte Carlo
Nelson, Benjamin, Ford, Eric B., Payne, Matthew J. (2014), ApJS, 210, 11. abstract doi - Swarm-NG: A CUDA library for Parallel n-body Integrations with focus on simulations of planetary systems
Dindar, Saleh et al. (2013), \na, 23, 6-18. abstract doi - A Bayesian Surrogate Model for Rapid Time Series Analysis and Application to Exoplanet Observations
Ford, Eric B., Moorhead, Althea V., Veras, Dimitri (2011), arXiv e-prints, arXiv:1107.4047. abstract doi - Parallel algorithm for solving Kepler's equation on Graphics Processing Units: Application to analysis of Doppler exoplanet searches
Ford, Eric B. (2009), New Astronomy, 14, 406-412. abstract doi - Adaptive Scheduling Algorithms for Planet Searches
Ford, Eric B. (2008), AJ, 135, 1008-1020. abstract doi - Improving the Efficiency of Markov Chain Monte Carlo for Analyzing the Orbits of Extrasolar Planets
Ford, Eric B. (2006), ApJ, 642, 505-522. abstract doi - Quantifying the Uncertainty in the Orbits of Extrasolar Planets
Ford, Eric B. (2005), AJ, 129, 1706-1717. abstract doi - Evolution of the Cluster Mass Function: GPC$³$ Dark Matter Simulations
Bode, Paul et al. (2001), ApJ, 551, 15-22. abstract doi