Classifying Modified Gravity and Dark Energy Theories with Bayesian Neural Networks: Massive Neutrinos, Baryonic Feedback, and the Theoretical Error

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We study the capacity of Bayesian Neural Networks (BNNs) to detect new physics in the dark matter power spectrum.

• As in previous studies, the Bayesian Cosmological Network (BaCoN) classifies spectra into one of 5 classes: $\Lambda$CDM, $f(R)$, $w$CDM, Dvali-Gabadaze-Porrati (DGP) gravity and a 'random' class, with this work extending it to include the effects of massive neutrinos and baryonic feedback.
• We further develop the treatment of theoretical errors in BaCoN-II, investigating several approaches and identifying the one that best allows the trained network to generalise to other power spectrum modelling prescriptions.
• In particular, we compare power spectra data produced by EuclidEmulator2, HMcode and halofit, all supplemented with the halo model reaction to model beyond-$\Lambda$CDM physics.