Speaker
Description
We apply Simulation-Based Inference ('SBI') to the cosmic dipole problem for the first time, measuring the distribution of quasar counts over the sky in the infrared CatWISE2020 ('CatWISE') sample. Our SBI-based approach alleviates systematic effects arising from the WISE instrument itself and enables direct inference of the sample's dipole. We find a dipole that is twice as large as the CMB expectation but more seriously misaligned with the CMB direction (≈3σ), adding further evidence to the persistent 'dipole tension'. This result showcases the power of SBI at machine-inferring a likelihood function in the presence of difficult systematics. Our approach will be critical for robust cosmological inference with new datasets. For example, in the radio regime, the Rapid ASKAP Continuum Survey (RACS) contains a wealth of unexplored information but has possible systematics that must be understood before measuring the dipole. SBI will be a natural technique to disentangle the signal from the systematic, shedding new light on the dipole tension.