arXiv subjects

Zhuoqi Zhang

Publications and source records attributed to Zhuoqi Zhang.

4 recordsLinked to original sources

arXiv2023

Transitioning from Stage-III to Stage-IV: Cosmology from galaxy$\times$CMB lensing and shear$\times$CMB lensing

We examine the cosmological constraining power from two cross-correlation probes between galaxy and CMB surveys: the cross-correlation of lens galaxy density with CMB lensing convergence $\langleδκ\rangle$, and source galaxy weak lensing shear with CMB lensing convergence $\langleγκ\rangle$. These two cross-correlation probes provide an independent cross-check of other large-scale structure constraints and are insensitive to galaxy-only or CMB-only systematic effects. In addition, when combined with other large-scale structure probes, the cross-correlations can break degeneracies in cosmological and nuisance parameters, improving both the precision and robustness of the analysis. In this work, we study how the constraining power of $\langleδκ\rangle+\langleγκ\rangle$ changes from Stage-III (ongoing) to Stage-IV (future) surveys. Given the flexibility in selecting the lens galaxy sample, we also explore systematically the impact on cosmological constraints when we vary the redshift range and magnitude limit of the lens galaxies using mock galaxy catalogs. We find that in our setup, the contribution to cosmological constraints from $\langleδκ\rangle$ and $\langleγκ\rangle$ are comparable in the Stage-III datasets; but in Stage-IV surveys, the noise in $\langleδκ\rangle$ becomes subdominant to cosmic variance, preventing $\langleδκ\rangle$ to further improve the constraints. This implies that to maximize the cosmological constraints from future $\langleδκ\rangle+\langleγκ\rangle$ analyses, we should focus more on the requirements on $\langleγκ\rangle$ instead of $\langleδκ\rangle$. Furthermore, the selection of the lens sample should be optimized in terms of our ability to characterize its redshift or galaxy bias instead of its number density.

astro-ph.CO↗

arXiv2023

Detecting deviations from Gaussianity in high-redshift CMB lensing maps

While the probability density function (PDF) of the cosmic microwave background (CMB) convergence field approximately follows a Gaussian distribution, primordial non-Gaussianities and small contributions from structures at low redshifts make the overall distribution slightly non-Gaussian. Some of the late-time component can be modelled using the distribution of galaxies and subtracted off from the original CMB lensing map to produce a map of matter distribution at high redshifts. Using this high-redshift mass map, we are able to directly study the early phases of structure formation. In this work, we forecast the detectability of signatures of non-Gaussianity due to nonlinear structure formation at $z>1.2$. Assuming the optimal case of no systematics, we find that it is challenging to detect such signatures in current surveys, but future experiments such as the deep field of CMB-S4 will be able to make detections of $\sim7σ$.

astro-ph.CO↗

arXiv2023

Deep-field Metacalibration

We introduce deep-field metacalibration, a new technique that reduces the pixel noise in metacalibration estimators of weak lensing shear signals by using a deeper imaging survey for calibration. In standard metacalibration, when estimating the object's shear response, extra noise is added to correct the effect of shearing the noise in the image, increasing the uncertainty on shear estimates by ~ 20%. Our new deep-field metacalibration technique leverages a separate, deeper imaging survey to calculate calibrations with less degradation in image noise. We demonstrate that weak lensing shear measurement with deep-field metacalibration is unbiased up to second-order shear effects. We provide algorithms to apply this technique to imaging surveys and describe how to generalize it to shear estimators that rely explicitly on object detection (e.g., metacalibration). For the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST), the improvement in weak lensing precision will depend on the somewhat unknown details of the LSST Deep Drilling Field (DDF) observations in terms of area and depth, the relative point-spread function properties of the DDF and main LSST surveys, and the relative contribution of pixel noise vs. intrinsic shape noise to the total shape noise in the survey. We conservatively estimate that the degradation in precision is reduced from 20% for metacalibration to ~ 5% or less for deep-field metacalibration, which we attribute primarily to the increased source density and reduced pixel noise contributions to the overall shape noise. Finally, we show that the technique is robust to sample variance in the LSST DDFs due to their large area, with the equivalent calibration error being ~ 0.1%. The deep-field metacalibration technique provides higher signal-to-noise weak lensing measurements while still meeting the stringent systematic error requirements of future surveys.

astro-ph.IM↗

arXiv2021

DeepGhostBusters: Using Mask R-CNN to Detect and Mask Ghosting and Scattered-Light Artifacts from Optical Survey Images

Wide-field astronomical surveys are often affected by the presence of undesirable reflections (often known as "ghosting artifacts" or "ghosts") and scattered-light artifacts. The identification and mitigation of these artifacts is important for rigorous astronomical analyses of faint and low-surface-brightness systems. However, the identification of ghosts and scattered-light artifacts is challenging due to a) the complex morphology of these features and b) the large data volume of current and near-future surveys. In this work, we use images from the Dark Energy Survey (DES) to train, validate, and test a deep neural network (Mask R-CNN) to detect and localize ghosts and scattered-light artifacts. We find that the ability of the Mask R-CNN model to identify affected regions is superior to that of conventional algorithms and traditional convolutional neural networks methods. We propose that a multi-step pipeline combining Mask R-CNN segmentation with a classical CNN classifier provides a powerful technique for the automated detection of ghosting and scattered-light artifacts in current and near-future surveys.

astro-ph.IM↗