That’s a neat mash‑up. We could take the temperature anisotropies from the CMB maps, smooth them into a low‑resolution grid, then label each patch with the overdensity measured in large‑scale structure surveys—basically turning each pixel into a “probability of star‑forming activity” feature. Feed that into a convolutional network and let it learn the statistical link between early‑universe fluctuations and later galaxy birth rates. It’s like training a machine to read the universe’s birth certificate. Just make sure you include a good mix of cosmic variance and foreground cleaning, otherwise the model will learn the noise instead of the signal.

Sounds solid. Let’s set up the denoising pipeline first, then feed the cleaned maps into the convolutional model. Once we get a reliable star‑formation map, we can compare it with the large‑scale surveys to see if the predictions hold. Time to fire up the processors and let the data do the rest.