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Discrepancies between cosmological parameter estimates from cosmic shear surveys and from recent Planck cosmic microwave background measurements challenge the ability of the highly successful $\Lambda$CDM model to describe the nature of the Universe. To rule out systematic biases in cosmic shear survey analyses, accurate redshift calibration within tomographic bins is key. In this paper, we improve photo-$z$ calibration via Bayesian hierarchical modeling of full galaxy photo-$z$ conditional densities, by employing $\textit{StratLearn}$, a recently developed statistical methodology, which accounts for systematic differences in the distribution of the spectroscopic training/source set and the photometric target set. Using realistic simulations that were designed to resemble the KiDS+VIKING-450 dataset, we show that $\textit{StratLearn}$-estimated conditional densities improve the galaxy tomographic bin assignment, and that our $\textit{StratLearn}$-Bayesian framework leads to nearly unbiased estimates of the target population means. This leads to a factor of $\sim 2$ improvement upon the previously best photo-$z$ calibration method. Our approach delivers a maximum bias per tomographic bin of $\Delta \langle z \rangle = 0.0095 \pm 0.0089$, with an average absolute bias of $0.0052 \pm 0.0067$ across the five tomographic bins., Comment: 24 pages, 20 figures, 3 appendices

We present results from angular cross-correlations between select samples of CHIME/FRB repeaters and galaxies in three photometric galaxy surveys, which have shown correlations with the first CHIME/FRB catalog containing repeating and nonrepeating sources: WISE$\times$SCOS, DESI-BGS, and DESI-LRG. We find a statistically significant correlation ($p$-value $<0.001$, after accounting for look-elsewhere factors) between a sample of repeaters with extragalactic dispersion measure DM $>395$ pc cm$^{-3}$ and WISE$\times$SCOS galaxies with redshift $z>0.275$. We demonstrate that the correlation arises surprisingly because of a statistical association between FRB 20200320A (extragalactic DM $\approx550$ pc cm$^{-3}$) and a galaxy group in the same dark matter halo at redshift $z\approx0.32$. We estimate that the host halo, along with an intervening halo at redshift $z\approx0.12$, accounts for at least $\sim$$30\%$ of the extragalactic DM. Our results strongly motivate incorporating galaxy group and cluster catalogs into direct host association pipelines for FRBs with $\lesssim$$1'$ localization precision, effectively utilizing the two-point information to constrain FRB properties such as their redshift and DM distributions. In addition, we find marginal evidence for a negative correlation at 99.4% CL between a sample of repeating FRBs with baseband data (median extragalactic DM $=354$ pc cm$^{-3}$) and DESI-LRG galaxies with redshift $0.3\le z<0.45$, suggesting that the repeaters might be more prone than apparent nonrepeaters to propagation effects in FRB-galaxy correlations due to intervening free electrons over angular scales $\sim$$0\mbox{$.\!\!^\circ$}5$., Comment: 14 pages, 4 figures, 3 tables, published in ApJ

We propose a simple, statistically principled, and theoretically justified method to improve supervised learning when the training set is not representative, a situation known as covariate shift. We build upon a well-established methodology in causal inference, and show that the effects of covariate shift can be reduced or eliminated by conditioning on propensity scores. In practice, this is achieved by fitting learners within strata constructed by partitioning the data based on the estimated propensity scores, leading to approximately balanced covariates and much-improved target prediction. We demonstrate the effectiveness of our general-purpose method on two contemporary research questions in cosmology, outperforming state-of-the-art importance weighting methods. We obtain the best reported AUC (0.958) on the updated "Supernovae photometric classification challenge", and we improve upon existing conditional density estimation of galaxy redshift from Sloan Data Sky Survey (SDSS) data.