A self-supervised machine learning approach to identify instrumental systematics in 21cm Cosmology

The primordial sound waves in the baryon-photon plasma are imprinted on the large-scale galaxy distribution as Baryon Acoustic Oscillations (BAO), serving as a cosmological standard ruler to constrain dark energy and the Hubble parameter. The upcoming 21cm intensity-mapping arrays are designed to map the 21cm emission across large volumes and measure the BAO scale. However, radio interferometers suffer from instrumental systematics that can hinder detection of the cosmological 21cm signal by leaking bright foregrounds into the signal-dominated modes. This work aims to demonstrate that a self-supervised model can predict the instrument systematic state (here, the amplitude of a spectral ripple in the beam width) in interferometric visibilities and potentially disentangle the instrument from the sky without a forward model. Based on the fact that an instrument stays the same as the sky rotates in a drift-scan observation, and that the same sky is observed under different instrument states, we train a convolutional autoencoder to compress the information in the simulated visibilities and separate this information into instrument-only and sky-only latent vectors. The result shows excellent prediction of the unknown instrument systematic for 15 held-out instrument states with $R^2 = 0.9975$ with a linear probe, against $R^2 = -3.55$ for a random-encoder baseline. The instrument-latent carries no detectable sky signal, as measured with a total-power sky proxy, for any held-out test data. The sky-latent vector and instrument-latent vector show partial separation but not complete disentanglement, identifying sky-latent purity as the primary open problem. The results show the ability of the model to recognize the systematic and show a promising path towards an ML-based foreground cleaning pipeline. Full instrument-sky disentanglement constitutes the natural next step.