arXiv subjects

Boyan Yin

Publications and source records attributed to Boyan Yin.

6 recordsLinked to original sources

arXiv2026

Robust Photometry for Roman High-Latitude Imaging Survey Cosmology Using Roman and Rubin Imaging

Accurate and precise photometry is essential for Roman cosmology because it affects photometric-redshift performance, galaxy and cluster selection, tomographic binning, and redshift-distribution characterization. Achieving robust photometry is challenging because Roman's depth leads to substantial source blending, particularly in joint Roman--Rubin analyses, where the two surveys have different spatial resolutions. In this work, we develop and validate slimfarmer, a model-fitting photometry pipeline designed for Roman High-Latitude Imaging Survey (HLIS) cosmological analyses. Building on The Farmer, slimfarmer introduces treatments of the correlated noise present in Roman images and of astronomical shot noise, together with model-fitting configurations tuned for Roman imaging. We validate the pipeline using Roman coadded simulated images and perform forced photometry on matched Rubin image simulations. We find that slimfarmer recovers galaxy colors with the mode of the color residuals within 20 millimag for Roman-based colors and within 70 millimag for Rubin-based colors. Properly accounting for correlated noise is essential for uncertainty quantification, as photometric uncertainties are otherwise underestimated by up to a factor of ~3. We also find that applying multi-object fitting consistently to both Roman and Rubin imaging substantially mitigates blending-induced color systematics. In crowded regions, single-object fitting produces environment-dependent color biases of up to ~0.5 mag, leading to environment-dependent degradation of photometric-redshift performance. Together, these results establish slimfarmer as a prototype photometric pipeline for Roman HLIS cosmology applications and identify the correlated-noise corrections, astronomical shot-noise treatment, and joint Roman--Rubin multi-object fitting required for robust photometric-redshift characterization.

astro-ph.IM↗

arXiv2026

Brightest Cluster Galaxy ellipticity as proxy for halo shape: Orientation bias, assembly bias, and potential selection effects in SZ-selected clusters

The orientation of triaxial galaxy clusters with respect to the line-of-sight is expected to be one of the prime sources of scatter and potential bias in optical observables (e.g., richness and weak-lensing signal) of galaxy clusters. In this work, we use the observed shape of the central Brightest Cluster Galaxy (BCG) as proxy for the orientation along the line-of-sight for clusters selected via the Sunyaev-Zel'dovich (SZ) effect from the South Pole Telescope (SPT) and Atacama Cosmology Telescope (ACT) surveys, matched to optically selected clusters from the Dark Energy Survey Year 3 (DES). We construct two samples of clusters that are designed to be identical in SZ mass estimate and redshift but with the roundest vs. the most elliptical BCGs, which we expect to correspond to BCGs (and clusters) with major axes aligned along the line-of-sight vs. in the plane of the sky, respectively. We find that the optical richness of round-BCG clusters is $\sim 10$\% larger than that of elliptical-BCG clusters, in agreement with the expectation from projection effects and presenting the first such detection in data. The density profiles, however, are not in agreement with the expectation from projection effects: the 1-halo term (below $6~h^{-1}\rm{Mpc}$) of both the weak-lensing and galaxy density profiles are the same for the subsamples, contrary to previous studies based on X-ray selected clusters. In the 2-halo regime (above $6~h^{-1}\rm{Mpc}$), we find a significant excess of the elliptical-BCG cluster profiles compared to the round-BCG cluster profiles, which is the opposite of the expectation from numerical simulations. We hypothesize that the intrinsic shape of the BCG reflects not just the orientation angle, but also intrinsic properties of the cluster which can affect both the SZ signal and the amplitude of the 2-halo term.

astro-ph.CO↗

arXiv2026

Modeling Redshift Uncertainties in Roman Weak Lensing Cosmology

Cosmological constraints using weak gravitational lensing measurements from the Roman Space Telescope will require a powerful method for modelling uncertainties in the galaxy redshift distribution. In this work, we use an optimized version of the principal component analysis (PCA) to model uncertainties in the full shape of the redshift distributions, a method proposed by \cite{pca_method} and recently used in the Dark Energy Survey Y6 analysis. Here, we implement this new approach within the Roman High Latitude Imaging Survey (HLIS) Cosmology Project Infrastructure Team (PIT) pipeline, namely Cobaya-Cosmolike Joint Architecture (\texttt{CoCoA}). To validate the PCA in mitigating biases on cosmological parameters, $S_8$ and $\Omega_m$, we use a set of redshift distributions from \texttt{Cardinal} generated for a variety of Roman configurations. Overall, when the simulated cosmic shear data vector is not strongly miscalibrated relative to the fiducial one, both the mean-shift and the PCA-based approaches produce consistent cosmological constraints when marginalizing over nuisance parameters. For mild to strong miscalibration, including additional PCs progressively mitigates biases in $S_8$ and $\Omega_m$, and can achieve comparable performance with fewer parameters than the nine tomographic-bin mean-shift model.

astro-ph.CO↗

arXiv2025

Confronting cosmic shear astrophysical uncertainties: DES Year 3 revisited

Cosmology from weak gravitational lensing has been limited by astrophysical uncertainties in baryonic feedback and intrinsic alignments. By calibrating these effects using external data, we recover non-linear information, achieving a 2% constraint on the clustering amplitude, $S_8$, resulting in a factor of two improvement on the $\Lambda$CDM constraints relative to the fiducial Dark Energy Survey Year 3 model. The posterior, $S_8=0.832^{+0.013}_{-0.017}$, shifts by $1.5\sigma$ to higher values, in closer agreement with the cosmic microwave background result for the standard six-parameter $\Lambda$CDM cosmology. Our approach uses a star-forming 'blue' galaxy sample with intrinsic alignment model parameters calibrated by direct spectroscopic measurements, together with a baryonic feedback model informed by observations of X-ray gas fractions and kinematic Sunyaev-Zel'dovich effect profiles that span a wide range in halo mass and redshift. Our results provide a blueprint for next-generation surveys: leveraging galaxy properties to control intrinsic alignments and external gas probes to calibrate feedback, unlocking a substantial improvement in the precision of weak lensing surveys.

astro-ph.CO↗

arXiv2025

Dimensional reduction for sampled priors and application to photometric redshift distributions

A typical Bayesian inference on the values of some parameters of interest $\bf q$ from some data $D$ involves running a Markov Chain (MC) to sample from the posterior $p({\bf q},{\bf n} | D) \propto \mathcal{L}(D | {\bf q},{\bf n}) p({\bf q}) p({\bf n}),$ where $\bf n$ are some nuisance parameters with separable prior. In some cases, the nuisance parameters are high-dimensional, and their prior $p({\bf n})$ is itself defined only by a set of samples that have been drawn from some other MC. The MC for the posterior will typically require evaluation of $p({\bf n})$ at arbitrary values of ${\bf n},$ i.e.\ one needs to provide a density estimator over the full $\bf n$ space from the provided samples. But the high dimensionality of $\bf n$ hinders both the density estimation and the efficiency of the MC for the posterior. We describe a solution to this problem: a linear compression of the $\bf n$ space into a much lower-dimensional space $\bf u$ which projects away directions in $\bf n$ space that cannot appreciably alter $\mathcal{L}.$ The algorithm for doing so is a slight modification to principal components analysis, and is less restrictive on $p(\bf n)$ than other proposed solutions to this issue. We demonstrate this ``mode projection'' technique using the analysis of 2-point correlation functions of weak lensing fields and galaxy density in the \textit{Dark Energy Survey}, where $\bf n$ is a binned representation of the redshift distribution $n(z)$ of the galaxies.

astro-ph.IM↗

arXiv2023

An unbiased method of measuring the ratio of two data sets

In certain cases of astronomical data analysis, the meaningful physical quantity to extract is the ratio $R$ between two data sets. Examples include the lensing ratio, the interloper rate in spectroscopic redshift samples, the decay rate of gravitational potential and $E_G$ to test gravity. However, simply taking the ratio of the two data sets is biased, since it renders (even statistical) errors in the denominator into systematic errors in $R$. Furthermore, it is not optimal in minimizing statistical errors of $R$. Based on Bayesian analysis and the usual assumption of Gaussian error in the data, we derive an analytical expression of the posterior PDF $P(R)$. This result enables fast and unbiased $R$ measurement, with minimal statistical errors. Furthermore, it relies on no underlying model other than the proportionality relation between the two data sets. Even more generally, it applies to the cases where the proportionality relation holds for the underlying physics/statistics instead of the two data sets directly. It also applies to the case of multiple ratios ($R\rightarrow {\bf R}=(R_1,R_2,\cdots)$). We take the lensing ratio as an example to demonstrate our method. We take lenses as DESI imaging survey galaxies, and sources as DECaLS cosmic shear and \emph{Planck} CMB lensing. We restrict the analysis to the ratio between CMB lensing and cosmic shear. The resulting $P(R)$, for multiple lens-shear pairs, are all nearly Gaussian. The S/N of measured $R$ ranges from $4.9$ to $8.4$. We perform several tests to verify the robustness of the above result.

astro-ph.CO↗