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We build a deep learning framework that connects the local formation process of dark matter haloes to the halo bias. We train a convolutional neural network (CNN) to predict the final mass and concentration of dark matter haloes from the initial conditions. The CNN is then used as a surrogate model to derive the response of the haloes’ mass and concentration to long-wavelength perturbations in the initial conditions, and consequently the halo bias parameters following the ‘response bias’ definition. The CNN correctly predicts how the local properties of dark matter haloes respond to changes in the large-scale environment, despite no explicit knowledge of halo bias being provided during training. We show that the CNN recovers the known trends for the linear and second-order density bias parameters b1 and b2, as well as for the local primordial non-Gaussianity linear bias parameter bϕ. The expected secondary assembly bias dependence on halo concentration is also recovered by the CNN: at fixed mass, halo concentration has only a mild impact on b1, but a strong impact on bϕ. Our framework opens a new window for discovering which physical aspects of the halo’s Lagrangian patch determine assembly bias, which in turn can inform physical models of halo formation and bias.

 

Molecular virology tools are critical for basic studies of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and for developing new therapeutics. Experimental systems that do not rely on viruses capable of spread are needed for potential use in lower-containment settings. In this work, we use a yeast-based reverse genetics system to develop spike-deleted SARS-CoV-2 self-replicating RNAs. These noninfectious self-replicating RNAs, or replicons, can be trans-complemented with viral glycoproteins to generate replicon delivery particles for single-cycle delivery into a range of cell types. This SARS-CoV-2 replicon system represents a convenient and versatile platform for antiviral drug screening, neutralization assays, host factor validation, and viral variant characterization.