Differentiable JAX N-body code for modeling nearly-Keplerian orbits
Project description
jnkepler
A differentiable N-body model for multi-planet systems.
jnkepler is a Python package for modeling photometric and radial velocity data of multi-planet systems via N-body integration. Built with JAX, it leverages automatic differentiation for efficient computation of model gradients. This enables seamless integration with gradient-based optimizers and Hamiltonian Monte Carlo methods, including the No-U-Turn Sampler (NUTS) in NumPyro. The package is particularly suited for efficiently sampling from multi-planet posteriors involving a large number of parameters and strong degeneracy.
Subpackages
- jnkepler.jaxttv: A differentialble N-body model for analyzing transit timing variations (TTVs) and radial velocities (RVs) of multi-planet systems.
- jnkepler.nbodytransit: A differentialble photodynamical model.
jaxoplanetneeds to be installed for using this package. - jnkepler.nbodyrv: A differentiable RV model taking into account mutual interactions between planets.
See readthedocs for more details.
Installation
pip install jnkepler
CPU performance note
If you use jnkepler on CPU with JAX ≥0.4.32, the default thunk runtime in the CPU backend can make computations much slower, especially when computing gradients.
To avoid this, disable the thunk runtime by setting the following environment variable before importing jax:
export XLA_FLAGS="--xla_cpu_use_thunk_runtime=false"
Or inside Python:
import os
os.environ["XLA_FLAGS"] = "--xla_cpu_use_thunk_runtime=false"
import jax
If this is not done, jnkepler will issue a warning on import.
Examples
Explore example notebooks in the examples/ directory to see jnkepler in action:
-
minimal example:
examples/minimal_example.ipynb- computing transit times and RVs
- plotting TTVs
- adding a non-transiting planet
-
TTV modeling:
examples/kep51_ttv_normal.ipynb- posterior sampling with NUTS
- reproducing the result in Libby-Roberts et al. (2020)
-
Photodynamical modeling:
examples/kep51_photodynamics_gp.ipynb- SVI optimization & posterior sampling with NUTS
- noise modeling using Gaussian Process with
tinygp
Applications
- TOI-1136: TTV modeling of 6-planets in a resonance chain [paper]
- TOI-2015: joint TTV & RV modeling of a two-planet system [paper]
- Kepler-51: four-planet modeling including JWST data [paper] [repository]
- K2-19: TTVs confirm 3:2 resonance [paper]
References
- Masuda et al. (2024), A Fourth Planet in the Kepler-51 System Revealed by Transit Timing Variations, AJ 168, 294
- Masuda (2025), jnkepler: Differentiable N-body model for multi-planet systems, Astrophysics Source Code Library, ascl:2505.006.
Project details
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file jnkepler-0.2.4.tar.gz.
File metadata
- Download URL: jnkepler-0.2.4.tar.gz
- Upload date:
- Size: 19.8 MB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.2.0 CPython/3.12.11
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
56f2116c0eb092db7ccd685bc2f93f7272548d7a1ab3cba67e7f18c018100c48
|
|
| MD5 |
0f2541ada970eef1ac16c9bb509ede9f
|
|
| BLAKE2b-256 |
713e8aeef8727f02586cc854203882b566e59a7b07a77cd5cf8e13eb1262837b
|
File details
Details for the file jnkepler-0.2.4-py3-none-any.whl.
File metadata
- Download URL: jnkepler-0.2.4-py3-none-any.whl
- Upload date:
- Size: 237.7 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.2.0 CPython/3.12.11
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
65340b16fe342f9d1a3f5adc23db7773563bd9733e934fa6aca608b5b7a91938
|
|
| MD5 |
77c17291e2406fdaf4565dce69f2727a
|
|
| BLAKE2b-256 |
874280b5f422395b3d0461f0a6aaaaf3ce6d815690b991e548fedf670d901028
|