Add time domain GP models into the noise module

[jeremy-baier]

NANOGrav has been using time-domain GPs to model chromatic noise processes (Hazboun+ 2025)
These time-domain GPs use linear interpolation to achieve a rank-reduced basis.
See the implementation in enterprise_extensions.

There exist a number of covariance functions used:

• square exponential (radial basis function)
• periodic
• 2d covariance w/ a rational quadratic along the frequency dimension
• white noise kernel (“dmx like”)

All of these can be applied to model DM, chromatic, solar wind, etc.

TO DO
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• dispersion measure
  • white noise WIP: Add time domain noise models to PINT noise_models.py #1911
  • square exponential WIP: Add time domain noise models to PINT noise_models.py #1911
  • periodic WIP: Add time domain noise models to PINT noise_models.py #1911
  • sq exp x quadratic in radio freq
  • periodic x rational quadratic in radio freq
• chromatic
  • white noise
  • square exponential
  • periodic
  • sq exp x quadratic in radio freq
  • periodic x rational quadratic in radio freq
• solar wind
  • white noise Feat time domain solar wind covariance #1966
  • square exponential Feat time domain solar wind covariance #1966
  • periodic Feat time domain solar wind covariance #1966