Cosmological Inference Under Photometric Redshift Uncertainty: Challenges, Solutions, and everything in between

The treatment of photometric redshift uncertainty, especially in the estimation of redshift distributions of samples of galaxies, is a vital requirement to perform reliable cosmological inference using large area photometric surveys like LSST. I will discuss current challenges in the estimation and calibration of sample redshift distributions, discuss how these biases can affect cosmological inference, and present my research on mitigation strategies. I will then present a research program to facilitate cosmological inference under redshift error in the context of LSST. In the second part of my talk, I discuss sample redshift inference as an inverse problem and provide a practical tutorial on the solution of inverse problems using sample redshift inference as an example.