  We present candl, an automatically differentiable python likelihood for
analysing cosmic microwave background (CMB) power spectrum measurements. candl
is powered by JAX, which makes it fast and easy to calculate derivatives of the
likelihood. This facilitates, for example, robust Fisher matrices without
finite-difference methods. We show the benefits of candl through a series of
example calculations, covering forecasting, robustness tests, and
gradient-based Markov chain Monte Carlo sampling. These also include optimising
the band power bin width to minimise parameter errors of a realistic mock data
set. Moreover, we calculate the correlation of parameter constraints from
correlated and partially overlapping subsets of the SPT-3G 2018 $TT/TE/EE$ data
release. In a traditional analysis framework, these tasks are slow and require
careful fine-tuning to obtain stable results. As such, a fully differentiable
pipeline allows for a higher level of scrutiny; we argue that this is the
paradigm shift required to leverage incoming data from ground-based
experiments, which will significantly improve the cosmological parameter
constraints from the Planck mission. candl comes with the latest primary and
lensing power spectrum data from the South Pole Telescope and Atacama Cosmology
Telescope collaborations and will be used as part of the upcoming SPT-3G
$TT/TE/EE$ and $\phi\phi$ data releases. Along with the core code, we release a
series of auxiliary tools, which simplify common analysis tasks and interface
the likelihood with other cosmological software. candl is pip-installable and
publicly available at https://github.com/Lbalkenhol/candl .
