Running hydrodynamical simulations to produce mock data of large-scale structure and baryonic probes, such as the thermal Sunyaev-Zeldovich (tSZ) effect, at cosmological scales is computationally challenging. We propose to leverage the expressive power of deep generative models to find an effective description of the large-scale gas distribution and temperature. We train two deep generative models, a variational auto-encoder and a generative adversarial network, on pairs of matter density and pressure slices from the BAHAMAS hydrodynamical simulation. The trained models are able to successfully map matter density to the corresponding gas pressure. We then apply the trained models on 100 lines-of-sight from SLICS, a suite of N-body simulations optimised for weak lensing covariance estimation, to generate maps of the tSZ effect. The generated tSZ maps are found to be statistically consistent with those from BAHAMAS. We conclude by considering a specific observable, the angular cross-power spectrum between the weak lensing convergence and the tSZ effect and its variance, where we find excellent agreement between the predictions from BAHAMAS and SLICS, thus enabling the use of SLICS for tSZ covariance estimation.

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In 2021, I published an Exploratory Report entitled “A network psychometric approach to neurocognition in early Alzheimer’s disease” in Cortex. In this paper, I created and analysed network models of neuropsychological task scores in cognitively normal (CN), amnestic mild cognitive impairment (aMCI), and early Alzheimer’s disease (eAD) groups. I also generated four hypotheses:•Original Hypothesis 1: Episodic memory variables will be most central in confirmatory network models of aMCI.•Original Hypothesis 2: Category Fluency becomes increasingly central in network models of groups with more severe Alzheimer’s disease (AD).•Original Hypothesis 3: Memory-semantic-language and attention-speed-working memory clusters will emerge in confirmatory network models for aMCI, eAD, and AD groups. They will be more pronounced for groups with more severe AD.•Original Hypothesis 4: Semantic networks underlying Category Fluency performance support the acquisition of word list memoranda in aMCI and eAD. After publishing my original paper, I learned of differential variability, further statistical tests, and community detection algorithms relevant to these hypotheses. In this commentary, I report the results of supplementary analyses based on the same data and network models in my original paper. Accordingly, these supplementary analyses do not provide confirmatory evidence for my original hypotheses. Instead, they reflect an attempt ratify or refine my original hypotheses based on additional analytical techniques. Indeed, the results of these supplementary analyses prompt some minor but important revisions to my original hypotheses; I present revised hypotheses throughout this commentary. R code and output for all supplementary analyses is accessible online: https://osf.io/2a7uz/.