Update README.md

capitalise codenames in readme

README.md

@@ -8,7 +8,7 @@
 
 # S2SCAT: Scattering covariance transform on the sphere
 
-`S2SCAT` is a Python package for computing scattering covariances on the sphere ([Mousset et al. 2024](https://arxiv.org/abs/xxxx.xxxxx)) using JAX.  It exploits autodiff to provide differentiable transforms, which are also deployable on hardware accelerators (e.g. GPUs and TPUs), leveraging the differentiable and accelerated spherical harmonic and wavelet transforms implemented in [s2fft](https://github.com/astro-informatics/s2fft) and [s2wav](https://github.com/astro-informatics/s2wav), respectively. 
+`S2SCAT` is a Python package for computing scattering covariances on the sphere ([Mousset et al. 2024](https://arxiv.org/abs/xxxx.xxxxx)) using JAX.  It exploits autodiff to provide differentiable transforms, which are also deployable on hardware accelerators (e.g. GPUs and TPUs), leveraging the differentiable and accelerated spherical harmonic and wavelet transforms implemented in [S2FFT](https://github.com/astro-informatics/s2fft) and [S2WAV](https://github.com/astro-informatics/s2wav), respectively. 
 
 > [!IMPORTANT]
 > It is worth highlighting that the input to `S2SCAT` are spherical harmonic coefficients, which can be generated with whichever software package you prefer, e.g. [`S2FFT`](https://github.com/astro-informatics/s2fft) or [`healpy`](https://healpy.readthedocs.io/en/latest/). Just ensure your harmonic coefficients are indexed using our convention; helper functions for this reindexing can be found in [`S2FFT`](https://github.com/astro-informatics/s2fft).