arXiv subjects

Qianjun Hang

Publications and source records attributed to Qianjun Hang.

11 recordsLinked to original sources

arXiv2026

Modeling of the diffuse background produced by the Vera C. Rubin Observatory M2 baffle scattered light

The Vera C. Rubin Observatory, with its unprecedented field of view and fast focal ratio, will survey the entire sky every 3.5 nights. This unique capacity requires dealing with off axis light that can produce stray light artefacts on the images. The secondary mirror (M2) baffle restricts the light that reaches the LSSTCam detector and it contributes to shaping the inner edge of the telescope optical pupil. This work studies the contribution to the background from the light scattered by the M2 baffle itself. The evanescence of this feature, together with the challenge of isolating it from the sky background, led to the necessity of performing in dome tests using a Collimated Beam Projector (CBP), normally used for calibration purposes. To complete the analysis, in addition to the in dome tests, an on sky observational campaign was conducted. This campaign employed both stellar targets and the Moon as illumination sources in order to determine the actual energy associated with the feature. The test data have been retro fitted thanks to the combination of ray tracing simulation, CBP and on sky data to infer the intensity and spatial distribution of the background scattered light within the different LSSTCam filters. We quantified the on sky impact of scattered light from the M2 baffle, both for light coming from bright and red stars and from the Moon. We also developed an approximate relation to transform the in dome measurements into predictions of on sky behavior. This transformation was achieved by comparing the illumination footprint produced by an off axis star with that generated by the CBP and by mapping the stellar Spectral Energy Distribution (SED) onto the CBP's set of discrete wavelengths. Finally, we extrapolated the scattered light behavior of the Moon to stellar sources, in order to build a compplete description of the M2 baffle contribution over the full range of magnitudes.

astro-ph.IM↗

arXiv2026

Propagating data-driven galaxy redshift distribution uncertainties in 3$\times$2-pt analyses

Uncertainties in the radial distribution of galaxies, $\boldsymbol{n}(\boldsymbol{z})$, are one of the major contributions to the error budget of early Stage-IV galaxy survey analyses of weak gravitational lensing, galaxy clustering and galaxy-galaxy lensing (3$\times$2-pt). Based on ensembles of simulated $\boldsymbol{n}(\boldsymbol{z})$ including stochastic and systematic variations, we study the impact of four different $\boldsymbol{n}(\boldsymbol{z})$ uncertainty models: shifts, shifts & stretches, Gaussian processes (GP) and principal component analysis (PCA). Due to the high dimensionality of the latter models, we make use of state-of-the-art gradient-based inference methods as well as approximate analytical marginalisation schemes. Our results show that Stage-IV 3$\times$2-pt analyses must go beyond simple shift & stretch models. In particular, we advocate for the adoption of PCA models even in early Stage-IV surveys. Our results show that considering a five-parameters PCA model only degrades the constraint on the $S_{\rm 8}$ parameter by $5$ per cent with respect to the case when only a shift and a stretch parameter are included, while incurring half the bias in its constituents parameters, $\Omega_{\rm m}$ and $\sigma_{\rm 8}$. We demonstrate that all models considered can be safely marginalised analytically, with speed-ups of up to a factor of 25 depending on the dimensionality of the model. This will allow Stage-IV analyses to safely include higher-dimensional $\boldsymbol{n}(\boldsymbol{z})$ uncertainty models in their analysis at negligible additional computational cost.

astro-ph.CO↗

arXiv2026

Calibrating redshift distributions at $z>2$ with Lyman-$\alpha$ forest cross-correlations

We explore the feasibility of using Lyman-$\alpha$ (Ly$\alpha$) forests to calibrate the ensemble redshift distribution of the high-redshift tail ($2<z<3$) of photometric galaxies. We use \texttt{CoLoRe} simulations to create mock DESI 5-year Ly$\alpha$ forests and Rubin Observatory LSST 10-year photometric galaxies up to $z=3$, and measure the galaxy redshift distribution via their angular cross-correlations. Due to large redshift-space distortions in the Ly$\alpha$ forest, the conventional $n(z)$ estimator for clustering redshifts does not apply, and we develope a theoretical framework to model the angular cross-correlation directly. Using the simulations, we explore effects of instrumental noise, continuum fitting, and contamination in the Ly$\alpha$ forest, cross-correlation angular scales ($\theta$), and redshift bin size ($\Delta z$) on the signal-to-noise (SNR) of the measurements. We find that continuum fitting methods strongly impact the SNR of the measurements. With our baseline continuum fitting method, \texttt{LyCAN}, at angular scales $\theta\sim10$ arcmin and $\Delta z=0.1$, we measure the cross-correlation signal at $24\sigma$. If the shape of the redshift distribution and galaxy bias evolution are known well for $z<2$, the cross-correlation can constrain the mean redshift of the galaxy sample to $\sigma_z/(1+\bar{z}) = 0.006$ at a mean redshift of $\bar{z}=2$. This demonstrates that Ly$\alpha$ cross-correlation is a reliable and promising method to calibrate the high-redshift tails of photometric Stage IV galaxy surveys.

astro-ph.CO↗

arXiv2026

Diagnosing the Effects of Spectroscopic Training Set Imperfection on Photometric Redshift Performance

Most LSST extragalactic science will rely on photometric redshifts (photo-$z$) to extract distance information for the galaxies. However, an incomplete or non-representative training set can introduce bias into photo-$z$ estimation. It is necessary to understand how various forms of training set imperfection, such as incompleteness and non-trivial spectroscopic target selection, affect photo-$z$ estimation algorithms, and to identify metrics best-suited to quantify the impact. This work aims to systematically study metrics for diagnosing how various photo-$z$ methods react to certain types of training set incompleteness and non-representativeness. We use methods available through the open-source Python library Redshift Assessment Infrastructure Layers (RAIL) to systematically test the algorithms CMNN, GPz, FlexZBoost, and PZFlow on mock training data degraded in accordance with several existing spectroscopic sky surveys, as well as under conditions of inverse redshift incompleteness, which approximately mimics observed patterns of incompleteness at high redshift. We employ the algorithm TrainZ as a control. Finally, we quantify photo-$z$ algorithm performance using a variety of statistical metrics implemented externally to RAIL. We determine that the Kullback-Liebler Divergence, Wasserstein Distance, and Probability Integral Transform are particularly informative metrics with which to assess the impact of training set imperfection on algorithmic performance. We also find that inverse redshift incompleteness effects alone lack the complexity to realistically represent anticipated training data.

astro-ph.IM↗

arXiv2025

Uniform Rolling: An LSST Observing Cadence Offering Sufficient Survey Uniformity for Comprehensive Cosmological Analysis

The Legacy Survey of Space and Time (LSST) that will be carried out by the NSF-DOE Vera C. Rubin Observatory promises to be the defining survey of the next decade, supplying unprecedented access to the night sky to static science- and time-domain science-focused researchers alike. Maximizing the output of the broad remit of Rubin Observatory science requires a non-trivial survey strategy. For time-domain science, the most promising strategy designed so far is a rolling survey strategy, whereby a subset of the full LSST survey area is observed at higher rate compared with the nominal rate dictated by weather conditions and the observatory's technical constraints. This strategy is now the baseline approach for the LSST as a whole. Focusing on static science (galaxy clustering and weak lensing), we study how these time-domain-optimized rolling strategies affect the depth uniformity at intermediate years of the survey. We characterize the amount of survey area at high risk of being lost in static-science analyses of a baseline rolling LSST dataset due to an insufficient combination of survey contiguity and uniformity. At intermediate data releases, nearly half of the survey could be lost for static science, decreasing the Dark Energy figure of merit by approximately 40\%. We describe additional metrics focused on key analysis tasks, such as photometric redshifts and galaxy clustering. We propose a new strategy that returns the survey to uniformity at key release years, enabling use of the full survey area and restoring our metrics to the values they would have in a non-rolling cadence without loss of time domain data relative to a rolling survey with the same number of rolling cycles. This work has informed the third round of optimization of the survey strategy, and the new uniform rolling strategies have been incorporated into the baseline strategy.

astro-ph.CO↗

arXiv2025

Redshift Assessment Infrastructure Layers (RAIL): Rubin-era photometric redshift stress-testing and at-scale production

Virtually all extragalactic use cases of the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) require the use of galaxy redshift information, yet the vast majority of its sample of tens of billions of galaxies will lack high-fidelity spectroscopic measurements thereof, instead relying on photometric redshifts (photo-$z$) subject to systematic imprecision and inaccuracy best encapsulated by photo-$z$ probability density functions (PDFs). We present the version 1 release of Redshift Assessment Infrastructure Layers (RAIL), an open source Python library for at-scale probabilistic photo-$z$ estimation, initiated by the LSST Dark Energy Science Collaboration (DESC) with contributions from the LSST Interdisciplinary Network for Collaboration and Computing (LINCC) Frameworks team. RAIL's three subpackages provide modular tools for end-to-end stress-testing, including a forward modeling suite to generate realistically complex photometry, a unified API for estimating per-galaxy and ensemble redshift PDFs by an extensible set of algorithms, and built-in metrics of both photo-$z$ PDFs and point estimates. RAIL serves as a flexible toolkit enabling the derivation and optimization of photo-$z$ data products at scale for a variety of science goals and is not specific to LSST data. We thus describe to the extragalactic science community, including and beyond Rubin the design and functionality of the RAIL software library so that any researcher may have access to its wide array of photo-$z$ characterization and assessment tools.

astro-ph.IM↗

arXiv2024

The Atacama Cosmology Telescope DR6 and DESI: Structure growth measurements from the cross-correlation of DESI Legacy Imaging galaxies and CMB lensing from ACT DR6 and Planck PR4

We measure the growth of cosmic density fluctuations on large scales and across the redshift range $0.3<z<0.8$ through the cross-correlation of the ACT DR6 CMB lensing map and galaxies from the DESI Legacy Survey, using three galaxy samples spanning the redshifts of $0.3 \lesssim z \lesssim 0.45$, $0.45 \lesssim z \lesssim0.6$, $0.6 \lesssim z \lesssim 0.8$. We adopt a scale cut where non-linear effects are negligible, so that the cosmological constraints are derived from the linear regime. We determine the amplitude of matter fluctuations over all three redshift bins using ACT data alone to be $S_8\equiv\sigma_8(\Omega_m/0.3)^{0.5}=0.772\pm0.040$ in a joint analysis combining the three redshift bins and ACT lensing alone. Using a combination of ACT and \textit{Planck} data we obtain $S_8=0.765\pm0.032$. The lowest redshift bin used is the least constraining and exhibits a $\sim2\sigma$ tension with the other redshift bins; thus we also report constraints excluding the first redshift bin, giving $S_8=0.785\pm0.033$ for the combination of ACT and \textit{Planck}. This result is in excellent agreement at the $0.3\sigma$ level with measurements from galaxy lensing, but is $1.8\sigma$ lower than predictions based on \textit{Planck} primary CMB data. Understanding whether this hint of discrepancy in the growth of structure at low redshifts arises from a fluctuation, from systematics in data, or from new physics, is a high priority for forthcoming CMB lensing and galaxy cross-correlation analyses.

astro-ph.CO↗

arXiv2024

Impact of survey spatial variability on galaxy redshift distributions and the cosmological $3\times2$-point statistics for the Rubin Legacy Survey of Space and Time (LSST)

We investigate the impact of spatial survey non-uniformity on the galaxy redshift distributions for forthcoming data releases of the Rubin Observatory Legacy Survey of Space and Time (LSST). Specifically, we construct a mock photometry dataset degraded by the Rubin OpSim observing conditions, and estimate photometric redshifts of the sample using a template-fitting photo-$z$ estimator, BPZ, and a machine learning method, FlexZBoost. We select the Gold sample, defined as $i<25.3$ for 10 year LSST data, with an adjusted magnitude cut for each year and divide it into five tomographic redshift bins for the weak lensing lens and source samples. We quantify the change in the number of objects, mean redshift, and width of each tomographic bin as a function of the coadd $i$-band depth for 1-year (Y1), 3-year (Y3), and 5-year (Y5) data. In particular, Y3 and Y5 have large non-uniformity due to the rolling cadence of LSST, hence provide a worst-case scenario of the impact from non-uniformity. We find that these quantities typically increase with depth, and the variation can be $10-40\%$ at extreme depth values. Using Y3 as an example, we propagate the variable depth effect to the weak lensing $3\times2$pt analysis, and assess the impact on cosmological parameters via a Fisher forecast. We find that galaxy clustering is most susceptible to variable depth, and non-uniformity needs to be mitigated below $3\%$ to recover unbiased cosmological constraints. There is little impact on galaxy-shear and shear-shear power spectra, given the expected LSST Y3 noise.

astro-ph.CO↗

arXiv2022

Galaxy and Mass Assembly (GAMA): Probing galaxy-group correlations in redshift space with the halo streaming model

We have studied the galaxy-group cross-correlations in redshift space for the Galaxy And Mass Assembly (GAMA) Survey. We use a set of mock GAMA galaxy and group catalogues to develop and test a novel 'halo streaming' model for redshift-space distortions. This treats 2-halo correlations via the streaming model, plus an empirical 1-halo term derived from the mocks, allowing accurate modelling into the nonlinear regime. In order to probe the robustness of the growth rate inferred from redshift-space distortions, we divide galaxies by colour, and divide groups according to their total stellar mass, calibrated to total mass via gravitational lensing. We fit our model to correlation data, to obtain estimates of the perturbation growth rate, $fσ_8$, validating parameter errors via the dispersion between different mock realizations. In both mocks and real data, we demonstrate that the results are closely consistent between different subsets of the group and galaxy populations, considering the use of correlation data down to some minimum projected radius, $r_{\rm min}$. For the mock data, we can use the halo streaming model to below $r_{\rm min} = 5h^{-1}$ Mpc, finding that all subsets yield growth rates within about 3% of each other, and consistent with the true value. For the actual GAMA data, the results are limited by cosmic variance: $fσ_8=0.29\pm 0.10$ at an effective redshift of 0.20; but there is every reason to expect that this method will yield precise constraints from larger datasets of the same type, such as the DESI bright galaxy survey.

astro-ph.CO↗

arXiv2021

Stacked CMB lensing and ISW signals around superstructures in the DESI Legacy Survey

The imprints of large-scale structures on the Cosmic Microwave Background can be studied via the CMB lensing and Integrated Sachs-Wolfe (ISW) signals. In particular, the stacked ISW signal around supervoids has been claimed in several works to be anomalously high. In this study, we find cluster and void superstructures using four tomographic redshift bins with $0<z<0.8$ from the DESI Legacy Survey, and measure the stacked CMB lensing and ISW signals around them. To compare our measurements with $Λ$CDM model predictions, we construct a mock catalogue with matched galaxy number density and bias, and apply the same photo-$z$ uncertainty as the data. The consistency between the mock and data is verified via the stacked galaxy density profiles around the superstructures and their quantity. The corresponding lensing convergence and ISW maps are then constructed and compared. The stacked lensing signal agrees with data well except at the highest redshift bin in density peaks, where the mock prediction is significantly higher, by approximately a factor 1.3. The stacked ISW signal is generally consistent with the mock prediction. We do not obtain a significant signal from voids, $A_{\rm ISW}=-0.10\pm0.69$, and the signal from clusters, $A_{\rm ISW}=1.52\pm0.72$, is at best weakly detected. However, these results are strongly inconsistent with previous claims of ISW signals at many times the level of the $Λ$CDM prediction. We discuss the comparison of our results with past work in this area, and investigate possible explanations for this discrepancy.

astro-ph.CO↗

arXiv2020

Galaxy clustering in the DESI Legacy Survey and its imprint on the CMB

We use data from the DESI Legacy Survey imaging to probe the galaxy density field in tomographic slices covering the redshift range $0<z<0.8$. After careful consideration of completeness corrections and galactic cuts, we obtain a sample of $4.9\times 10^7$ galaxies covering 17 739 deg$^2$. We derive photometric redshifts with precision $σ_z/(1+z)=0.012 - 0.015$, and compare with alternative estimates. Cross-correlation of the tomographic galaxy maps with Planck maps of CMB temperature and lensing convergence probe the growth of structure since $z=0.8$. The signals are compared with a fiducial Planck $Λ$CDM model, and require an overall scaling in amplitude of $A_κ=0.901\pm 0.026$ for the lensing cross-correlation and $A_{\rm ISW} = 0.984 \pm 0.349$ for the temperature cross-correlation, interpreted as the Integrated Sachs-Wolfe effect. The ISW amplitude is consistent with the fiducial $Λ$CDM prediction, but lies significantly below the prediction of the AvERA model of Rácz et al. (2017), which has been proposed as an alternative explanation for cosmic acceleration. Within $Λ$CDM, our low amplitude for the lensing cross-correlation requires a reduction either in fluctuation normalization or in matter density compared to the Planck results, so that $Ω_m^{0.78}σ_8=0.297\pm 0.009$. In combination with the total amplitude of CMB lensing, this favours a shift mainly in density: $Ω_m=0.274\pm0.024$. We discuss the consistency of this figure with alternative evidence. A conservative compromise between lensing and primary CMB constraints would require $Ω_m=0.296\pm0.006$, where the 95% confidence regions of both probes overlap.

astro-ph.CO↗