Welcome to NEST’s documentation!

NEST (Neural network Estimator of Stellar Times) is a python package designed to make the use of pre-trained neural networks for stellar age estimation easy.

It is based on Boin et al. 2026 (to be published in A&A).

@ARTICLE{2026arXiv260309540B,
    author = {{Boin}, T. and {Casamiquela}, L. and {Haywood}, M. and {Di Matteo}, P. and {Lebreton}, Y. and {Uddin}, M. and {Reese}, D.~R.},
        title = "{Stellar age determination using deep neural networks: Isochrone ages for 1.3 million stars, based on BaSTI, MIST, PARSEC, Dartmouth and SYCLIST evolutionary grids}",
    journal = {arXiv e-prints},
    keywords = {Astrophysics of Galaxies},
        year = 2026,
        month = mar,
        eid = {arXiv:2603.09540},
        pages = {arXiv:2603.09540},
archivePrefix = {arXiv},
    eprint = {2603.09540},
primaryClass = {astro-ph.GA},
    adsurl = {https://ui.adsabs.harvard.edu/abs/2026arXiv260309540B},
    adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

You can download the BibTeX citation file here: NEST.bib

With it, you can estimate the ages of stars based on their position in the Color-Magnitude Diagram and their metallicity. It contains a suite of Neural Networks trained on different stellar evolutionary grids. If observational uncertainties are provided, it can compute age uncertainties.

If you are looking for a quick way to test the Neural Networks, a web interface is also available here (click the image):

Web interface