Dark Energy Survey Year 6 Results: Clustering-redshifts and importance sampling of Self-Organised-Maps $n(z)$ realizations for $3\times2$pt samples
W. d'Assignies, G. M. Bernstein, B. Yin, G. Giannini, A. Alarcon, M. Manera, C. To, M. Yamamoto, N. Weaverdyck, R. Cawthon, M. Gatti, A. Amon, D. Anbajagane, S. Avila, M. R. Becker, K. Bechtol, C. Chang, M. Crocce, J. De Vicente, S. Dodelson, J. Fang, A. Fert\'e, D. Gruen

TL;DR
This paper enhances photometric redshift estimates for DES Year 6 by combining self-organizing maps with clustering-based redshift constraints, improving cosmological parameter precision.
Contribution
It introduces a method that integrates clustering redshifts with SOM-derived $n(z)$ realizations using importance sampling, improving redshift accuracy for cosmological analyses.
Findings
WZ constraints improve redshift estimates at $z>1.1$
Combining SOMPZ with WZ enhances cosmological parameter constraints
Achieves approximately 10 ext% improvement in $S_8$ for DES Y6
Abstract
This work is part of a series establishing the redshift framework for the pt analysis of the Dark Energy Survey Year 6 (DES Y6). For DES Y6, photometric redshift distributions are estimated using self-organizing maps (SOMs), calibrated with spectroscopic and many-band photometric data. To overcome limitations from color-redshift degeneracies and incomplete spectroscopic coverage, we enhance this approach by incorporating clustering-based redshift constraints (clustering-z, or WZ) from angular cross-correlations with BOSS and eBOSS galaxies, and eBOSS quasar samples. We define a WZ likelihood and apply importance sampling to a large ensemble of SOM-derived realizations, selecting those consistent with the clustering measurements to produce a posterior sample for each lens and source bin. The analysis uses angular scales of 1.5-5 Mpc to optimize signal-to-noise while…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
