gp6

Python code for Gaussian Processes (for Physics) specifically designed for the reconstruction of late time cosmological data (e.g., arXiv:2105.12970, arXiv:2106.08688 and arXiv:2111.08289).

Please cite the above papers when using gp6, and let me know about any questions or comments. Always happy to discuss. Thanks. - Reggie

Installation: pip install gp6

Minimal example: in terminal, python ex1_minimal_cc.py

Hubble Expansion Rate Reconstruction with Cosmic Chronometers (CC) without Covariance. Output shown below.

Hubble function reconstruction with CC data (No Covariance)

Hubble function derivative reconstruction with CC data (No Covariance)

Another minimal example: see notebook quick_example.ipynb

$H(z)$ Reconstruction with CC Covariance + Bonus Quintessence potential and Dark Energy Equation of State GP inferences. Details about the reconstruction in arXiv:2105.12970.

Upcoming

• Cosmology-specialized reconstructions
• DE models for physics inferrencing
• ABC automated kernel selection (as in arXiv:2106.08688)