In this episode, Michelle and Payel talk about the asymmetry in the number of craters across the moon, using microlensing to find primordial black holes, tracing the growth of disk galaxies since z~1, and using machine learning to detect group galaxies merging with massive clusters. Listen below, on Apple podcasts, Spotify, or wherever you get your podcasts.

Check out the papers we discussed below:

Lunar ejecta as the missing piece to resolving lunar cratering
asymmetry – Hailiang Li et al.

AMPM II. — A Lunar-Mass Primordial Black Hole Microlensing
Candidate in the Milky Way Halo – Renee Key et al.

Witnessing the rapid growth of disk galaxies over cosmic time
using JWST and HST – Samane Raji et al.

Identifying group galaxies merging with massive clusters using machine
learning – Rhys Jordan et al.