spectroxide: A code package for computing cosmic microwave background spectral distortions
Ethan Baker, Hongwan Liu, Siddharth Mishra-Sharma
arXiv:2604.24838v1 Announce Type: new
Abstract: We present spectroxide, a code package for computing cosmic microwave background spectral distortions in which all ${sim}14{,}500$ lines of Rust code, Python interface, and ${sim}400$ automated tests were written by an AI assistant (Claude Code) under human physicist supervision. The solver evolves the photon Boltzmann equation under Compton scattering, double Compton emission, and Bremsstrahlung from $z sim 5 times 10^6$ to the present, computing spectral distortions from arbitrary heat and photon injection within this redshift range. No fully open-source code of this kind is publicly available; we validate against analytic limits, published spectra, and publicly available precomputed Green’s function tables. We document the development as a case study in AI-assisted scientific computing, highlighting how domain expertise caught physics bugs (incorrect dimensional prefactors, near-cancellation errors) that evaded the full automated test suite, and provide recommendations for best practices in human–AI collaborative development of scientific software. We make spectroxide publicly available on GitHub.arXiv:2604.24838v1 Announce Type: new
Abstract: We present spectroxide, a code package for computing cosmic microwave background spectral distortions in which all ${sim}14{,}500$ lines of Rust code, Python interface, and ${sim}400$ automated tests were written by an AI assistant (Claude Code) under human physicist supervision. The solver evolves the photon Boltzmann equation under Compton scattering, double Compton emission, and Bremsstrahlung from $z sim 5 times 10^6$ to the present, computing spectral distortions from arbitrary heat and photon injection within this redshift range. No fully open-source code of this kind is publicly available; we validate against analytic limits, published spectra, and publicly available precomputed Green’s function tables. We document the development as a case study in AI-assisted scientific computing, highlighting how domain expertise caught physics bugs (incorrect dimensional prefactors, near-cancellation errors) that evaded the full automated test suite, and provide recommendations for best practices in human–AI collaborative development of scientific software. We make spectroxide publicly available on GitHub.