Motivation

Why Differentiable Ray Tracing?

IACTrace allows differentiable raytracing and source rendering by being based on JAX. This has a variety of advantages:

Gradient-based optimization

Rather than exhaustive grid searches or stochastic sampling, we can use efficient gradient descent to find optimal telescope configurations. This is particularly powerful for high-dimensional problems like aligning hundreds of individual mirror facets.

Inverse problems

Given an observed image (e.g., from a calibration LED or star), we can infer the telescope state that produced it. This enables automated alignment monitoring and correction.

Sensitivity analysis

Gradients directly quantify how sensitive the optical performance is to each parameter, informing manufacturing tolerances and maintenance priorities.

Why JAX?

IACTrace is built on JAX, a numerical computing library that provides:

Automatic differentiation

JAX can differentiate through arbitrary Python/NumPy code, including loops, conditionals, and custom data structures. This eliminates the need to manually derive and implement gradient formulas.

Hardware acceleration

The same code runs on CPU, GPU, or TPU with minimal modification. This enables efficient parallel simulations of skyfields, reducing the time requirement for simulating many sources at once.

Why Another Package for IACT Raytracing?

The reasons will be given more in-depth below, but on a personal note, I was unsatisfied with the current offer: Commercial raytracers were too expensive, existing alternatives such as ROBAST / sim_telarray do not offer GPU support or differentiability.

This package is meant to fill the gap, for people who:

  • Do not want to use commercial software

  • Do not need electronics / airshower simulations

  • Don’t need very high accuracy / non-sequential raytracing

But care about:

  • Speed via GPU-acceleration

  • Differentiability

  • Simplicity and good python integration.