# Estimating Orbital Parameters of Direct Imaging Exoplanet Using Neural Network

arXiv: https://arxiv.org/abs/2510.17459 · HTML: https://mlsanity.com/paper/2510.17459/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 71 references that are in the map.
- First posted on arXiv 20 Oct 2025.

## Builds on (most cited first, 16 of 16)

- [Flow Matching for Generative Modeling](https://mlsanity.com/paper/2210.02747/) (2022)
- [Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow](https://mlsanity.com/paper/2209.03003/) (2022)
- [Density estimation using Real NVP](https://mlsanity.com/paper/1605.08803/) (2016)
- [NICE: Non-linear Independent Components Estimation](https://mlsanity.com/paper/1410.8516/) (2014)
- [Neural Spline Flows](https://mlsanity.com/paper/1906.04032/) (2019)
- [Flexible statistical inference for mechanistic models of neural dynamics](https://mlsanity.com/paper/1711.01861/) (2017)
- [Flow Matching for Scalable Simulation-Based Inference](https://mlsanity.com/paper/2305.17161/) (2023)
- ["Hey, that's not an ODE": Faster ODE Adjoints via Seminorms](https://mlsanity.com/paper/2009.09457/) (2020)
- [Flow Matching: Markov Kernels, Stochastic Processes and Transport Plans](https://mlsanity.com/paper/2501.16839/) (2025)
- [Automated identification of transiting exoplanet candidates in NASA Transiting Exoplanets Survey Satellite (TESS) data with machine learning methods](https://mlsanity.com/paper/2102.10326/) (2021)
- [The GPU Phase Folding and Deep Learning Method for Detecting Exoplanet Transits](https://mlsanity.com/paper/2312.02063/) (2023)
- [Identification and Classification of Exoplanets Using Machine Learning Techniques](https://mlsanity.com/paper/2305.09596/) (2023)
- [Machine learning-based identification of Gaia astrometric exoplanet orbits](https://mlsanity.com/paper/2404.09350/) (2024)
- [NotPlaNET: Removing False Positives from Planet Hunters TESS with Machine Learning](https://mlsanity.com/paper/2405.18278/) (2024)
- [DeepTTV: Deep Learning Prediction of Hidden Exoplanet From Transit Timing Variations](https://mlsanity.com/paper/2409.04557/) (2024)
- [Astrometric Binary Classification Via Artificial Neural Networks](https://mlsanity.com/paper/2409.09563/) (2024)

## Abstract (arXiv)

In this work, we propose a flow-matching Markov chain Monte Carlo (FM-MCMC) algorithm for estimating the orbital parameters of exoplanetary systems, especially for those only one exoplanet is involved. Compared to traditional methods that rely on random sampling within the Bayesian framework, our approach first leverages flow matching posterior estimation (FMPE) to efficiently constrain the prior range of physical parameters, and then employs MCMC to accurately infer the posterior distribution. For example, in the orbital parameter inference of beta Pictoris b, our model achieved a substantial speed-up while maintaining comparable accuracy-running 77.8 times faster than Parallel Tempered MCMC (PTMCMC) and 365.4 times faster than nested sampling. Moreover, our FM-MCMC method also attained the highest average log-likelihood among all approaches, demonstrating its superior sampling efficiency and accuracy. This highlights the scalability and efficiency of our approach, making it well-suited for processing the massive datasets expected from future exoplanet surveys. Beyond astrophysics, our methodology establishes a versatile paradigm for synergizing deep generative models with traditional sampling, which can be adopted to tackle complex inference problems in other fields, such as cosmology, biomedical imaging, and particle physics.