A deep learning approach to test the small-scale galaxy morphology and its relationship with star formation activity in hydrodynamical simulationsDOI: info:10.1093/mnras/staa3864v. 5014359–4382
Zanisi, Lorenzo, Huertas-Company, Marc, Lanusse, François, Bottrell, Connor, Pillepich, Annalisa, Nelson, Dylan, Rodriguez-Gomez, Vicente, Shankar, Francesco, Hernquist, Lars, Dekel, Avishai, Margalef-Bentabol, Berta, Vogelsberger, Mark, and Primack, Joel. 2021. "A deep learning approach to test the small-scale galaxy morphology and its relationship with star formation activity in hydrodynamical simulations." Monthly Notices of the Royal Astronomical Society, 501 4359–4382. https://doi.org/10.1093/mnras/staa3864.