Measuring the spectral index of turbulent gas with deep learning from projected density maps

Piero Trevisan, Mario Pasquato, Alessandro Ballone, Michela Mapelli

    Research output: Contribution to journalArticlepeer-review


    Turbulence plays a key role in star formation in molecular clouds, affecting star cluster primordial properties. As modelling present-day objects hinges on our understanding of their initial conditions, better constraints on turbulence can result in windfalls in Galactic archaeology, star cluster dynamics, and star formation. Observationally, constraining the spectral index of turbulent gas usually involves computing spectra from velocity maps. Here, we suggest that information on the spectral index might be directly inferred from column density maps (possibly obtained by dust emission/absorption) through deep learning. We generate mock density maps from a large set of adaptive mesh refinement turbulent gas simulations using the hydro-simulation code RAMSES. We train a convolutional neural network (CNN) on the resulting images to predict the turbulence index, optimize hyperparameters in validation and test on a holdout set. Our adopted CNN model achieves a mean squared error of 0.024 in its predictions on our holdout set, over underlying spectral indexes ranging from 3 to 4.5. We also perform robustness tests by applying our model to altered holdout set images, and to images obtained by running simulations at different resolutions. This preliminary result on simulated density maps encourages further developments on real data, where observational biases and other issues need to be taken into account.

    Original languageEnglish (US)
    Pages (from-to)5798-5803
    Number of pages6
    JournalMonthly Notices of the Royal Astronomical Society
    Issue number4
    StatePublished - Nov 1 2020


    • Hydrodynamics
    • Methods: numerical
    • Methods: statistical
    • Turbulence

    ASJC Scopus subject areas

    • Astronomy and Astrophysics
    • Space and Planetary Science


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