Release notes

This file only lists the most important changes to each version. We try to follow semantic versioning: - Major release means API incompatibilities - Minor release means API compatibilities, but significant feature differences - Bugfix release only fixes bugs

Release 2.1

• An option to control the verbosity in log file has been added (“system/logfile_verbose_level”, v2.1.3)

Forward related

• Add a way of transforming all bias models into a forward deterministic transition. It means more flexibility at the cost of losing performance/memory by doing more computations than required. For example, each subcatalog needs its own bias which could trigger quite a lot of recomputation and/or caching.

• PMv2 optimization when sampling.

• Implement a simple (non-MPI) haar transform.

• Add EnforceMass model element to articifially fix the mass conservation.

• Forward models may support a new behavior for adjointModel_v2. They can accumulate all adjoint vectors that are provided to them through adjointModel_v2. The new behavior must be requested by calling BORGForwardModel::accumulateAdjoint. In that case, the user is explicitly requested to clear the adjoint gradient when the computation is done by calling BORGForwardModel::clearAdjointGradient. That behavior has been ported to pyborg. If the mode is not supported, an exception will be triggered.

• Merged Altair code.

• Bind ClassCosmo to ARES. Python binding is also active and vectorized for get_Tk.

Sampler related

• Add CG89 “higher order” symplectic integrator.

API related:

• ManyPower bias model needs a likelihood info entry now to set the width of the prior on parameters. The name is ManyPower_prior_width in [info].

• Code cleanup in velocity field estimator. It also now supports Simplex-In-Cell (no adjoint gradient yet and only non-MPI).

• Models accept a broader range of parameters using BORGForwardModel::setModelParams.

Python related:

• NEW tool hades_python which supports a full deterministic transition written in python/tensorflow/jax. Data loading is still work in progress and may need hacking at the moment

• Python extension is supporting LikelihoodInfo and the bias as forward model element.

• Add a ‘setup.py’ to support compiling the BORG python module directly with pip and packaging as a wheel file.

• Samplers fully supported from Python.

Build related

• build.sh only downloads the dependency if the file is not already there

• Error reporting include a full C++ stacktrace on supported platforms (cmake flag is STACKTRACE_USE_BACKTRACE=ON, experimental at the moment It can be turned off).

• Added GIT hooks to check on basic text elements (like formatting) before running commits. clang-formatter absence may be overridden using ARES_CLANG_OVERRIDE=1

Release 2.0alpha

• Use a prior that is purely gaussian unit variance (Fourier) in HMC now. The cosmology is completely moved as a BORGForwardModel.

• BORGForwardModel adds the v2 API to executes model: forwardModel_v2, and adjointModel_v2. This relies heavily on the mechanics of ModelIO

• Deterministic models are now self-registering and the available lists can be dynamically queried.

• Add a hook to optionally dump extra bias fields.

• Add QLPT and QLPT_RSD forward model in extra/borg

• Lots of documentation reorganization

• Added the lyman alpha model in extra/borg

• Merged the EFT likelihood effort in extra/borg

Release 1.0

Initial release