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We present GIGANTES, the most extensive and realistic void catalog suite everreleased -- containing over 1 billion cosmic voids covering a volume largerthan the observable Universe, more than 20 TB of data, and created by runningthe void finder VIDE on QUIJOTE's halo simulations. The expansive and detailedGIGANTES suite, spanning thousands of cosmological models, opens up the studyof voids, answering compelling questions: Do voids carry unique cosmologicalinformation? How is this information correlated with galaxy information?Leveraging the large number of voids in the GIGANTES suite, our Fisherconstraints demonstrate voids contain additional information, criticallytightening constraints on cosmological parameters. We use traditional voidsummary statistics (void size function, void density profile) and the voidauto-correlation function, which independently yields an error of$0.13\,\mathrm{eV}$ on $\sum\,m_{\nu}$ for a 1 $h^{-3}\mathrm{Gpc}^3$simulation, without CMB priors. Combining halos and voids we forecast an errorof $0.09\,\mathrm{eV}$ from the same volume. Extrapolating to next generationmulti-Gpc$^3$ surveys such as DESI, Euclid, SPHEREx, and the Roman SpaceTelescope, we expect voids should yield an independent determination ofneutrino mass. Crucially, GIGANTES is the first void catalog suite expresslybuilt for intensive machine learning exploration. We illustrate this bytraining a neural network to perform likelihood-free inference on the void sizefunction. Cosmology problems provide an impetus to develop novel deep learningtechniques, leveraging the symmetries embedded throughout the universe fromphysical laws, interpreting models, and accurately predicting errors. WithGIGANTES, machine learning gains an impressive dataset, offering uniqueproblems that will stimulate new techniques.

The GIGANTES dataset: precision cosmology from voids in the machine learning era