Run the code to produce the Fisher matrix.
Select the type of experiments that you want to study choosing within a wide range of possible cosmological observables. Then design the experimental specifications.
Perform statistical analyses of the Fisher matrices and plot results.
Both libraries constituting the CosmicFish code are based on state of the art, optimized, core algorithms including precise derivatives calculators, spectral protection of Fisher matrices against degenerate, unconstrained, parameters, to name a few.
The CosmicFish source code is available on GitHub and we welcome contributions from everyone. Follow the link to access the latest features (mostly still to be validated) and improvement. Join the developers and include your favourite models or observables.
Thanks to an exaustive documentation and clean code the CosmicFish library is easy to customize. New applications can be easily developed following the already existing ones. A flexible package system allows for as much customization as possible. Produce your own analysis and plotting pipelines following the default python apps.
The two libraries tha constitute the CosmicFish code are clearly written and documented. The code comes with two unit testing suites that are responsible for checking all the building blocks of the two libraries. The two libraries are supplied with a thorough automatic documentation that explain the interfaces and the purposes of all the functions in both codes.
Facing the advent of the next generation cosmological surveys we present a method to forecast knowledge gain on cosmological models. We propose this as a well defined and general tool to quantify the performance of different experiments in relation to different theoretical models. In particular, the assessment of experimental performance will benefit enormously from the fact that this method is invariant under re-parametrization of the model. We apply this to future surveys and compare expected knowledge advancements to the most relevant experiments performed over the history of modern cosmology. When considering the standard cosmological model, we show that it will rapidly reach knowledge saturation in the near future and forthcoming improvements will not match the past ones. On the contrary, we find that new observations have the potential for unprecedented knowledge jumps when extensions of the standard scenario are considered.
Because the code has just been released!
If you use the CosmicFish package to produce some scientific results, please refer the papers:
Information Gain in Cosmology: From the Discovery of Expansion to Future Surveys
CosmicFish Implementation Notes V1.0
We are grateful to Ana Achúcarro, Carlo Baccigalupi, Erminia Calabrese, Stefano Camera, Luigi Danese, Giulio Fabbian, Noemi Frusciante, Bin Hu, Valeria Pettorino, Levon Pogosian, Giuseppe Puglisi and Alessandra Silvestri for useful and helpful discussions on the subject. We are indebted to Luca Heltai for help with numerical algorithms.