Inferring dark matter substructure with astrometric lensing beyond the power spectrum
This provides astronomers with a more powerful tool for characterizing dark matter in our Galaxy, though it represents an incremental advance in applying existing machine learning techniques to this domain.
The researchers tackled the problem of detecting dark matter substructure by developing a novel machine learning method that analyzes gravitational lensing signatures in astrometric data, achieving significantly enhanced sensitivity to cold dark matter populations and better noise scaling compared to existing correlation-based approaches.
Astrometry -- the precise measurement of positions and motions of celestial objects -- has emerged as a promising avenue for characterizing the dark matter population in our Galaxy. By leveraging recent advances in simulation-based inference and neural network architectures, we introduce a novel method to search for global dark matter-induced gravitational lensing signatures in astrometric datasets. Our method based on neural likelihood-ratio estimation shows significantly enhanced sensitivity to a cold dark matter population and more favorable scaling with measurement noise compared to existing approaches based on two-point correlation statistics. We demonstrate the real-world viability of our method by showing it to be robust to non-trivial modeled as well as unmodeled noise features expected in astrometric measurements. This establishes machine learning as a powerful tool for characterizing dark matter using astrometric data.