veloce
Welcome to veloce, the velocity power spectrum covariance emulator! We use an emulator based on neural networks to accelerate the prediction of covariance matrices for different cosmologies in the context of supernovae studies. If you are interested and want to know more, check out the paper, raise an issue or contact Davide Piras.
Installation
To use the emulator and/or sample your supernovae posterior, follow these steps:
-
(optional)
conda create -n veloce python=3.11 jupyter ipython=8.20(create a customcondaenvironment withpython 3.11) -
(optional)
conda activate veloce(activate it) -
Install the package:
pip install velocemu python -c 'import velocemu'or alternatively, clone the repository and install it:
git clone https://github.com/dpiras/veloce.git cd veloce pip install . python -c 'import velocemu'
Usage
Cloning the repository will also give you access to all Jupyter notebooks, which include information on how to generate a single element of the covariance, use the emulator, and sample the posterior.
Trained models
You can find the available models in this folder, which will be updated when new models become available. Currently, we provide the model that leads to the final results of the paper, namely the nonlinear case with fixed $\sigma_{\rm u}$, but more models are in production. If you are interested in other models, please reach out or contact Davide Piras. Also note that it should be straightforward for you to train your own models using CosmoPower, and then add them under velocemu/trained_models.
Contributing and contacts
Feel free to fork this repository to work on it; otherwise, please raise an issue or contact Davide Piras.
Citation
If you use veloce, please cite the corresponding paper:
@ARTICLE{Piras25,
author = {{Piras}, Davide and {Sorrenti}, Francesco and {Durrer}, Ruth and {Kunz}, Martin},
title = "{Anchors no more: Using peculiar velocities to constrain $H_0$ and the primordial Universe without calibrators}",
journal = {arXiv e-prints},
year = 2025,
month = apr,
eid = {arXiv:2504.10453},
pages = {arXiv:2504.10453},
doi = {10.48550/arXiv.2504.10453},
archivePrefix = {arXiv},
eprint = {2504.10453},
primaryClass = {astro-ph.CO},
adsurl = {https://ui.adsabs.harvard.edu/abs/2025arXiv250410453P},
}
License
veloce is released under the GPL-3 license (see LICENSE) subject to the non-commercial use condition.
veloce
Copyright (C) 2025 Davide Piras & contributors
This program is released under the GPL-3 license (see LICENSE.txt), subject to a non-commercial use condition.
Metadata
Release files for velocemu 0.1.1
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Source distribution (sdist)
| File | Size | Uploaded | Provenance | Actions |
|---|---|---|---|---|
| velocemu-0.1.1.tar.gz | 5.0 MB | Without provenance | Details for velocemu-0.1.1.tar.gz |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Actions Reset |
|---|---|---|---|---|
| velocemu-0.1.1-py3-none-any.whl | Python 3 | none | any | Details for velocemu-0.1.1-py3-none-any.whl |
Total release size: 10.0 MB
Release files / velocemu-0.1.1.tar.gz
| Download URL | velocemu-0.1.1.tar.gz |
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| Size | 5.0 MB |
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Release files / velocemu-0.1.1-py3-none-any.whl
| Download URL | velocemu-0.1.1-py3-none-any.whl |
|---|---|
| Size | 5.0 MB |
| Tags | Python 3 |
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