Deep learning in wide-field surveys: Fast analysis of strong lenses in ground-based cosmic experiments

Abstract

Searches and analyses of strong gravitational lenses are challenging due to the rarity and image complexity of these astronomical objects. Next-generation surveys (both ground- and space-based) will provide more opportunities to derive science from these objects, but only if they can be analyzed on realistic time-scales. Currently, these analyses are expensive. In this work, we present a regression analysis with uncertainty estimates using deep learning models to measure four parameters of strong gravitational lenses in simulated Dark Energy Survey data. Using only $gri$-band images, we predict Einstein Radius, lens velocity dispersion, lens redshift to within $10-15%$ of truth values and source redshift to $30%$ of truth values, along with predictive uncertainties. This work helps to take a step along the path of faster analyses of strong lenses with deep learning frameworks.

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BibTeX

@article{bom2019deep,
    author = "Bom, Clecio and Poh, Jason and Nord, Brian and Blanco-Valentin, Manuel and Dias, Luciana",
    title = "Deep learning in wide-field surveys: Fast analysis of strong lenses in ground-based cosmic experiments",
    journal = "arXiv preprint arXiv:1911.06341",
    year = "2019"
}

Recommended citation: Clecio Bom, Jason Poh, Brian Nord, Manuel Blanco-Valentin, Luciana Dias (2019). "Deep learning in wide-field surveys: Fast analysis of strong lenses in ground-based cosmic experiments." arXiv preprint arXiv:1911.06341.
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