Clustering analysis of photometric data and its application to BAO measurements

Wide-field imaging surveys yield enormous amount of photometric data, and this offers an opportunity to perform accurate clustering analysis. However, in imaging surveys, the redshift, photo-z is derived from a few photometric bands, and hence the challenges due to inaccurate redshift information must be overcome. We first present a photo-z estimation method based on a robust machine learning method, the normalizing flow. We demonstrate that this method performs competitively relatively to the state-of-the-art algorithms. In order to perform unbiased cosmological inference, the true-redshift distribution of a photo-z sample must be estimated accurately. We describe a joint calibration method using the clustering information of the photometric data and external spectroscopic data.  In the second part of the talk, we turn to the transverse BAO measurements using the DES data. We first showcase the DES Y6 BAO measurement, which is the highest significant BAO measurement from photometric data, and discuss cosmological implications of the measurement. Finally, we describe the BAO signature in the density-shear correlation function and present its tantalizing evidence in the DES Y3 data.