Metadata-Version: 2.4
Name: jnkepler
Version: 0.2.5
Summary: Differentiable JAX N-body code for modeling nearly-Keplerian orbits
Author-email: Kento Masuda <kmasuda@ess.sci.osaka-u.ac.jp>
License: MIT
Project-URL: Homepage, https://github.com/kemasuda/jnkepler
Project-URL: Repository, https://github.com/kemasuda/jnkepler
Project-URL: Documentation, https://github.com/kemasuda/jnkepler
Requires-Python: >=3.10
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: jax<7.0
Requires-Dist: numpyro
Requires-Dist: pandas
Requires-Dist: numpy
Requires-Dist: matplotlib
Requires-Dist: importlib_resources
Requires-Dist: corner
Requires-Dist: arviz
Dynamic: license-file

# 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](https://jax.readthedocs.io/en/latest/index.html), 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](https://num.pyro.ai). 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. [`jaxoplanet`](https://jax.exoplanet.codes/en/latest/) needs to be installed for using this package.
- **jnkepler.nbodyrv**: A differentiable RV model taking into account mutual interactions between planets.

See [readthedocs](https://jnkepler.readthedocs.io/en/stable/) 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**:

```bash
export XLA_FLAGS="--xla_cpu_use_thunk_runtime=false"
```

Or inside Python:

```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.
**Please note that this workaround is intended for JAX < 0.7**; this is why `jnkepler` currently requires `jax<0.7`.

### *Note on the transit-finding algorithm (since v0.2.5)*

In `JaxTTV.get_transit_times_obs`, the default transit-time solver is now `"fast"`. This method is optimized for efficient evaluation near the observed transit times, which is typically sufficient for inference because models far from the data have very low likelihood. For models that are substantially offset from the observed transit times, however, the fast method can be less accurate than the Newton-based method. Use `transit_time_method="newton"` when robust accuracy is needed in that regime.

## 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** (normal likelihood): `examples/kep51_ttv_normal.ipynb` 
  - posterior sampling with NUTS
  - reproducing the result in [Libby-Roberts et al. (2020)](https://ui.adsabs.harvard.edu/abs/2020AJ....159...57L/abstract)
- **TTV modeling** (Student's t likelihood):
  - `examples/v1298tau_ttv_student.ipynb`: reproducing the analysis in [Livingston et al. (2026)](https://www.nature.com/articles/s41586-025-09840-z)
  - `examples/trappist-1_ttv_student.ipynb`: reproducing the analysis in [Agol et al. (2021)](https://iopscience.iop.org/article/10.3847/PSJ/abd022)
  
- **Photodynamical modeling**: `examples/kep51_photodynamics_gp.ipynb`
  - SVI optimization & posterior sampling with NUTS
  - noise modeling using Gaussian Process with [`tinygp`](https://tinygp.readthedocs.io/en/stable/)

## Applications

- TOI-1136: TTV modeling of 6-planets in a resonance chain [[paper]](https://ui.adsabs.harvard.edu/abs/2022arXiv221009283D/abstract)
- TOI-2015: Joint TTV & RV modeling of a two-planet system [[paper]](https://arxiv.org/abs/2310.11775)
- Kepler-51: Four-planet modeling including JWST data [[paper]](https://arxiv.org/abs/2410.01625) [[repository]](https://github.com/kemasuda/kep51_jwst)
- K2-19: TTVs confirm 3:2 resonance [[paper]](https://arxiv.org/abs/2509.18031)
- TOI-4495: Photodynamical modeling of a pair of near-resonant sub-Neptunes [[paper]](https://arxiv.org/abs/2601.02665) [[repository]](https://github.com/kemasuda/toi4495-ttv)
- V1298 Tau: Four low-density planets transiting a young star [[paper]](https://www.nature.com/articles/s41586-025-09840-z) [[repository]](https://github.com/kemasuda/v1298tau-ttv); see also `examples/v1298tau_ttv_student.ipynb` in this repo
- TOI-2076: [[paper]](https://ui.adsabs.harvard.edu/abs/2026arXiv260302550W/abstract) 

## References

- Masuda et al. (2024), [A Fourth Planet in the Kepler-51 System Revealed by Transit Timing Variations](https://ui.adsabs.harvard.edu/abs/2024AJ....168..294M/abstract), AJ 168, 294
- Masuda (2025), [jnkepler: Differentiable N-body model for multi-planet systems](https://ui.adsabs.harvard.edu/abs/2025ascl.soft05006M/abstract),  Astrophysics Source Code Library, ascl:2505.006.

