jayrock: JWST Observation Planning and Simulation for Minor Bodies

The James Webb Space Telescope (JWST) offers unprecedented sensitivity for characterising minor bodies across the Solar System, from Near-Earth Asteroids to Trans-Neptunian Objects (e.g. [1]). However, the complexity of JWST observation planning - incorporating moving-target visibility constraints, target-specific thermal modeling, and instrument-specific signal-to-noise ratio (SNR) requirements - often necessitates the use of multiple disparate tools and web-based interfaces. I present jayrock, an open-source python toolkit designed to unify these steps into a single, automated workflow for the minor body community.

jayrock provides a comprehensive, programmatic interface for the end-to-end simulation of minor body observations. The package automates the retrieval of physical properties via the rocks package [2] and computes JWST-centric visibility windows and ephemerides through integration with JPL Horizons and the JWST General Target Visibility Tool (jwst_gtvt). Target spectra are modeled as a superposition of reflected sunlight (using Phoenix stellar models) and thermal emission (utilising a Near-Earth Asteroid Thermal Model, NEATM [3]).

A core strength of jayrock is its local execution of the STScI pandeia engine, the powerhouse behind the official JWST Exposure Time Calculator [4]. This allows users to simulate detector configurations (including MIRI, NIRSpec, and NIRCam modes) without the need for a web browser. Crucially, the software enables automated detector optimisation: users can define specific SNR targets, and jayrock performs an iterative binary search to determine the required number of groups and integrations (ngroup/nint), based on the predicted observational parameters of one or many targets.

By providing the ability to simulate observations across multiple epochs or instrument configurations for many targets in a single script, jayrock significantly reduces the manual overhead of proposal preparation. The package is publicly available via PyPI and GitHub [5], offering a transparent and reproducible environment for planning the next generation of infrared Solar System observations.

References
1.    Pinilla-Alonso N., Brunetto R., De Prá M., et al., Nature Astronomy 2025, 9, 230-244.
2.    Berthier J., Carry B., Mahlke M., Normand J., A&A 2023, 671, A151.
3.    Implementation from https://github.com/mkelley/mskpy
4.    Pontoppidan K., Pickering T.E., Laidler V.G., et al., Observatory Operations: Strategies, Processes, and Systems VI,  SPIE, 2016, Edinburgh
5.    https://github.com/maxmahlke/jayrock