Reproducibility Code for "NSF-DOE Vera C. Rubin Observatory's First View of the Neptune Trojan L5 (Trailing) Cloud: Two New Long-Term Stable Trojans"

This archive contains Jupyter notebooks for the analysis presented in "NSF-DOE Vera C. Rubin Observatory's First View of the Neptune Trojan L5 (Trailing) Cloud: Two New Long-Term Stable Trojans" accepted for publication in ApJL; one for our color and phase curve calculations and one for our dynamical assessments of the confirmed and candidate L5 Neptune Trojans. 

Individual files, and their sources are described below:

NepTroj.ipynb - average colors and surfacecolor analysis, phase curves, and photometry (light-curve) plots.

example_neptune_trojan_dynamics_check.ipynb - n-body integration of best-fit orbit and colones to assess long term stablity and resonacnce identification. 

gmm_model.pkl.gz - P. H. Bernardinelli et al 2025 AJ 169 305 (Article DOI: 10.3847/1538-3881/adc459; Zenodo data link DOI: 10.5281/zenodo.14963648) GMM color model from DES (Dark Energy Survey) Outer Solar System detections 

markwardt.txt - L. Markwardt et al 2023 PSJ 4 135 (DOI: 10.3847/PSJ/ace528) Neptune Trojan color data

mpcorb_extended.json.gz - copy of the zipped MPCORB JSON file from the MPC (Minor Planet Center; https://minorplanetcenter.net/data) downloaded on  August 14, 2026

rubin.sqlite - Rubin MPC reported observations and photometry and astrometry from A. Koumjian & B612 Foundation | Asteroid Institute 2025. (https://doi.org/10.5281/zenodo.17047589) - downloaded on March 5, 2026. 

neptrojdata_phase.csv - Rubin MPC reported observations and photometry and astrometry with phase angle and relevant disstances calculated by JPL Horizons (https://ssd.jpl.nasa.gov/horizons/app.html#/) 

y6_des_tnos_color.fits - DES Outer Solar System Search Colors -  P. H. Bernardinelli et al 2023 ApJS 269 18 (Article DOI: 10.3847/1538-3881/adc459; Zenodo data link DOI: 10.5281/zenodo.8231238) 

gmm_anyk.py - utilities for the phase curve and color comparison 
 
These notebooks have been developed and tested with python=3.12. To run both notebooks, you will need to install via mamba, conda, or pip the following packages (versions tested with the notebooks are listed where applicable):

jupyter (run with jupyter lab v4.6.3/jupyter notebook  v 7.6.2)
astropy (v 3.0.5)
numpy (v 2.4.6)
matplotlib (v 3.11.1))
numba (v 0.66.0) 
scipy (v 1.18.0)
tqdm (v  4.70.0)
json (v 2.0.9)
pandas (v 3.0.5)
rebound (v 4.6.0)
astroquery (v 0.4.11)
matplotlib (v 3.11.1)
scikit-learn (v 1.9.0)
scikit-image (v 0.26.0)

By pip only you will be able to install:
compress_pickle (v 2.1.0)

You will need to clone the repository for SBDynT (as of writing you need to download the zip and unzip to get all the machine learning data files):

Download https://github.com/small-body-dynamics/SBDynT/archive/refs/heads/main.zip

Then from inside the SBDynT-main directory:
pip install -e .