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Rachel Mandelbaum

Publications and source records attributed to Rachel Mandelbaum.

At least 55 records · Page 3Linked to original sources
arXiv2023

Hyper Suprime-Cam Year 3 Results: Cosmology from Galaxy Clustering and Weak Lensing with HSC and SDSS using the Minimal Bias Model

We present cosmological parameter constraints from a blind joint analysis of three two-point correlation functions measured from the Year 3 Hyper Suprime-Cam (HSC-Y3) imaging data, covering 416 deg$^2$, and the SDSS DR11 spectroscopic galaxies spanning the redshift range $[0.15, 0.70]$. We subdivide the SDSS galaxies into three volume-limited samples separated in redshift, each of which acts as a large-scale structure tracer characterized by the measurement of the projected correlation function, $w_{\rm p}(R)$. We also use the measurements of the galaxy-galaxy weak lensing signal $ΔΣ(R)$ for each of these SDSS samples which act as lenses for a secure sample of source galaxies selected from the HSC-Y3 shape catalog based on their photometric redshifts. We combine these measurements with the cosmic shear correlation functions, $ξ_{\pm}(\vartheta)$, measured for our HSC source sample. We model these observables with the minimal bias model of the galaxy clustering observables in the context of a flat $Λ$CDM cosmology. We use conservative scale cuts, $R>12$ and $8~h^{-1}$Mpc, for $ΔΣ$ and $w_{\rm p}$, respectively, where the minimal bias model is valid, in addition to conservative prior on the residual bias in the mean redshift of the HSC photometric source galaxies. Our baseline analysis yields $S_8=0.775^{+0.043}_{-0.038}$ (68% C.I.) for the $Λ$CDM model, after marginalizing over uncertainties in other parameters. Our value of $S_8$ is consistent with that from the Planck 2018 data, but the credible interval of our result is still relatively large. Our results are statistically consistent with those of a companion paper, which extends this analysis to smaller scales with an emulator-based halo model.

astro-ph.CO↗
arXiv2023

A Differentiable Perturbation-based Weak Lensing Shear Estimator

Upcoming imaging surveys will use weak gravitational lensing to study the large-scale structure of the Universe, demanding sub-percent accuracy for precise cosmic shear measurements. We present a new differentiable implementation of our perturbation-based shear estimator (FPFS), using JAX, which is publicly available as part of a new suite of analytic shear algorithms called AnaCal. This code can analytically calibrate the shear response of any nonlinear observable constructed with the FPFS shapelets and detection modes utilizing auto-differentiation (AD), generalizing the formalism to include a family of shear estimators with corrections for detection and selection biases. Using the AD capability of JAX, it calculates the full Hessian matrix of the non-linear observables, which improves the previously presented second-order noise bias correction in the shear estimation. As an illustration of the power of the new AnaCal framework, we optimize the effective galaxy number density in the space of the generalized shear estimators using an LSST-like galaxy image simulation for the ten-year LSST. For the generic shear estimator, the magnitude of the multiplicative bias $|m|$ is below $3\times 10^{-3}$ (99.7% confidence interval), and the effective galaxy number density is improved by 5%. We also discuss some planned future additions to the AnaCal software suite to extend its applicability beyond the FPFS measurements.

astro-ph.CO↗
arXiv2023

Unified framework for diffusion generative models in SO(3): applications in computer vision and astrophysics

Diffusion-based generative models represent the current state-of-the-art for image generation. However, standard diffusion models are based on Euclidean geometry and do not translate directly to manifold-valued data. In this work, we develop extensions of both score-based generative models (SGMs) and Denoising Diffusion Probabilistic Models (DDPMs) to the Lie group of 3D rotations, SO(3). SO(3) is of particular interest in many disciplines such as robotics, biochemistry and astronomy/cosmology science. Contrary to more general Riemannian manifolds, SO(3) admits a tractable solution to heat diffusion, and allows us to implement efficient training of diffusion models. We apply both SO(3) DDPMs and SGMs to synthetic densities on SO(3) and demonstrate state-of-the-art results. Additionally, we demonstrate the practicality of our model on pose estimation tasks and in predicting correlated galaxy orientations for astrophysics/cosmology.

cs.LG↗
arXiv2023

Hyper Suprime-Cam Year 3 Results: Cosmology from Cosmic Shear Two-point Correlation Functions

We perform a blinded cosmology analysis with cosmic shear two-point correlation functions (2PCFs) measured from more than 25 million galaxies in the Hyper Suprime-Cam three-year shear catalog in four tomographic redshift bins ranging from 0.3 to 1.5. After conservative masking and galaxy selection, the survey covers 416 deg$^2$ of the northern sky with an effective galaxy number density of 15 arcmin$^{-2}$ over the four redshift bins. The 2PCFs adopted for cosmology analysis are measured in the angular range: $7.1 < θ/{\rm arcmin} < 56.6$ for $ξ_+$ and $31.2 <θ/{\rm arcmin} < 248$ for $ξ_-$, with a total signal-to-noise ratio of 26.6. We apply a conservative, wide, flat prior on the photometric redshift errors on the last two tomographic bins, and the relative magnitudes of the cosmic shear amplitude across four redshift bins allow us to calibrate the photometric redshift errors. With this flat prior on redshift errors, we find $Ω_{\rm m}=0.256_{-0.044}^{+0.056}$ and $S_8\equiv σ_8 \sqrt{Ω_{\rm m}/0.3}=0.769_{-0.034}^{+0.031}$ (both 68\% CI) for a flat $Λ$ cold dark matter cosmology. We find, after unblinding, that our constraint on $S_8$ is consistent with the Fourier space cosmic shear and the 3$\times$2pt analyses on the same HSC dataset. We carefully study the potential systematics from astrophysical and systematic model uncertainties in our fiducial analysis using synthetic data, and report no biases (including projection bias in the posterior space) greater than $0.5σ$ in the estimation of $S_8$. Our analysis hints that the mean redshifts of the two highest tomographic bins are higher than initially estimated. In addition, a number of consistency tests are conducted to assess the robustness of our analysis. Comparing our result with Planck-2018 cosmic microwave background observations, we find a ~$2σ$ tension for the $Λ$CDM model.

astro-ph.CO↗
arXiv2023

Weak Lensing Tomographic Redshift Distribution Inference for the Hyper Suprime-Cam Subaru Strategic Program three-year shape catalogue

We present posterior sample redshift distributions for the Hyper Suprime-Cam Subaru Strategic Program Weak Lensing three-year (HSC Y3) analysis. Using the galaxies' photometry and spatial cross-correlations, we conduct a combined Bayesian Hierarchical Inference of the sample redshift distributions. The spatial cross-correlations are derived using a subsample of Luminous Red Galaxies (LRGs) with accurate redshift information available up to a photometric redshift of $z < 1.2$. We derive the photometry-based constraints using a combination of two empirical techniques calibrated on spectroscopic- and multiband photometric data that covers a spatial subset of the shear catalog. The limited spatial coverage induces a cosmic variance error budget that we include in the inference. Our cross-correlation analysis models the photometric redshift error of the LRGs to correct for systematic biases and statistical uncertainties. We demonstrate consistency between the sample redshift distributions derived using the spatial cross-correlations, the photometry, and the posterior of the combined analysis. Based on this assessment, we recommend conservative priors for sample redshift distributions of tomographic bins used in the three-year cosmological Weak Lensing analyses.

astro-ph.CO↗
arXiv2023

Hyper Suprime-Cam Year 3 Results: Measurements of Clustering of SDSS-BOSS Galaxies, Galaxy-Galaxy Lensing and Cosmic Shear

We use the Sloan Digital Sky Survey (SDSS) BOSS galaxies and their overlap with approximately 416 sq. degree of deep $grizy$-band imaging from the Subaru Hyper Suprime-Cam Survey (HSC). We measure three two-point correlations that form the basis of the cosmological inference presented in our companion papers, Miyatake et al. and Sugiyama et al. We use three approximately volume limited subsamples of spectroscopic galaxies by their $i$-band magnitude from the SDSS-BOSS: LOWZ (0.1<z<0.35), CMASS1 (0.43<z<0.55) and CMASS2 (0.55<z<0.7), respectively. We present high signal-to-noise ratio measurements of the projected correlation functions of these galaxies, which is expected to be proportional to the matter correlation function times the bias of galaxies on large scales. In order to break the degeneracy between the amplitude of the matter correlation and the bias of these galaxies, we use the distortions of the shapes of galaxies in HSC due to weak gravitational lensing, to measure the galaxy-galaxy lensing signal, which probes the galaxy-matter cross-correlation of the SDSS-BOSS galaxies. We also measure the cosmic shear correlation functions from HSC galaxies which is related to the projected matter correlation function. We demonstrate the robustness of our measurements with a variety of systematic tests. Our use of a single sample of HSC source galaxies is crucial to calibrate any residual systematic biases in the inferred redshifts of our galaxies. We also describe the construction of a suite of mocks: i) spectroscopic galaxy catalogs which obey the clustering and abundance of each of the three SDSS-BOSS subsamples, and ii) galaxy shape catalogs which obey the footprint of the HSC survey and have been appropriately sheared by the large-scale structure expected in a $Λ$-CDM model. We use these mock catalogs to compute the covariance of each of our observables.

astro-ph.CO↗
arXiv2023

A General Framework for Removing Point Spread Function Additive Systematics in Cosmological Weak Lensing Analysis

Cosmological weak lensing measurements rely on a precise measurement of the shear two-point correlation function (2PCF) along with a deep understanding of systematics that affect it. In this work, we demonstrate a general framework for detecting and modeling the impact of PSF systematics on the cosmic shear 2PCF, and mitigating its impact on cosmological analysis. Our framework can describe leakage and modeling error from all spin-2 quantities contributed by the PSF second and higher moments, rather than just the second moments, using the cross-correlations between galaxy shapes and PSF moments. We interpret null tests using the HSC Year 3 (Y3) catalogs with this formalism, and find that leakage from the spin-2 combination of PSF fourth moments is the leading contributor to additive shear systematics, with total contamination that is an order of magnitude higher than that contributed by PSF second moments alone. We conducted a mock cosmic shear analysis for HSC Y3, and find that, if uncorrected, PSF systematics can bias the cosmological parameters $Ω_m$ and $S_8$ by $\sim$0.3$σ$. The traditional second moment-based model can only correct for a 0.1$σ$ bias, leaving the contamination largely uncorrected. We conclude it is necessary to model both PSF second and fourth moment contamination for HSC Y3 cosmic shear analysis. We also reanalyze the HSC Y1 cosmic shear analysis with our updated systematics model, and identify a 0.07$σ$ bias on $Ω_m$ when using the more restricted second moment model from the original analysis. We demonstrate how to self-consistently use the method in both real space and Fourier space, assess shear systematics in tomographic bins, and test for PSF model overfitting.

astro-ph.CO↗
arXiv2023

Photometric Redshift Uncertainties in Weak Gravitational Lensing Shear Analysis: Models and Marginalization

Recovering credible cosmological parameter constraints in a weak lensing shear analysis requires an accurate model that can be used to marginalize over nuisance parameters describing potential sources of systematic uncertainty, such as the uncertainties on the sample redshift distribution $n(z)$. Due to the challenge of running Markov Chain Monte-Carlo (MCMC) in the high dimensional parameter spaces in which the $n(z)$ uncertainties may be parameterized, it is common practice to simplify the $n(z)$ parameterization or combine MCMC chains that each have a fixed $n(z)$ resampled from the $n(z)$ uncertainties. In this work, we propose a statistically-principled Bayesian resampling approach for marginalizing over the $n(z)$ uncertainty using multiple MCMC chains. We self-consistently compare the new method to existing ones from the literature in the context of a forecasted cosmic shear analysis for the HSC three-year shape catalog, and find that these methods recover similar cosmological parameter constraints, implying that using the most computationally efficient of the approaches is appropriate. However, we find that for datasets with the constraining power of the full HSC survey dataset (and, by implication, those upcoming surveys with even tighter constraints), the choice of method for marginalizing over $n(z)$ uncertainty among the several methods from the literature may significantly impact the statistical uncertainties on cosmological parameters, and a careful model selection is needed to ensure credible parameter intervals.

astro-ph.CO↗
arXiv2023

Impact of Point Spread Function Higher Moments Error on Weak Gravitational Lensing II: A Comprehensive Study

Weak gravitational lensing, or weak lensing, is one of the most powerful probes for dark matter and dark energy science, although it faces increasing challenges in controlling systematic uncertainties as \edit{the statistical errors become smaller}. The Point Spread Function (PSF) needs to be precisely modeled to avoid systematic error on the weak lensing measurements. The weak lensing biases induced by errors in the PSF model second moments, i.e., its size and shape, are well-studied. However, Zhang et al. (2021) showed that errors in the higher moments of the PSF may also be a significant source of systematics for upcoming weak lensing surveys. Therefore, the goal of this work is to comprehensively investigate the modeling quality of PSF moments from the $3^{\text{rd}}$ to $6^{\text{th}}$ order, and estimate their impact on cosmological parameter inference. We propagate the \textsc{PSFEx} higher moments modeling error in the HSC survey dataset to the weak lensing \edit{shear-shear correlation functions} and their cosmological analyses. We find that the overall multiplicative shear bias associated with errors in PSF higher moments can cause a $\sim 0.1 σ$ shift on the cosmological parameters for LSST Y10. PSF higher moment errors also cause additive biases in the weak lensing shear, which, if not accounted for in the cosmological parameter analysis, can induce cosmological parameter biases comparable to their $1σ$ uncertainties for LSST Y10. We compare the \textsc{PSFEx} model with PSF in Full FOV (\textsc{Piff}), and find similar performance in modeling the PSF higher moments. We conclude that PSF higher moment errors of the future PSF models should be reduced from those in current methods to avoid a need to explicitly model these effects in the weak lensing analysis.

astro-ph.CO↗
arXiv2023

Hyper Suprime-Cam Year 3 Results: Cosmology from Galaxy Clustering and Weak Lensing with HSC and SDSS using the Emulator Based Halo Model

We present cosmology results from a blinded joint analysis of cosmic shear, $ξ_{\pm}(\vartheta)$, galaxy-galaxy weak lensing, $Δ\!Σ(R)$, and projected galaxy clustering, $w_{\rm p}(R)$, measured from the Hyper Suprime-Cam three-year (HSC-Y3) shape catalog and the Sloan Digital Sky Survey (SDSS) DR11 spectroscopic galaxy catalog - a 3$\times$2pt cosmology analysis. We define luminosity-cut samples of SDSS galaxies to serve as the tracers of $w_{\rm p}$ and as the lens samples for $Δ\!Σ$ in three spectroscopic redshift bins spanning the range $0.15<z<0.7$. For the $ξ_{\pm}$ and $Δ\!Σ$ measurements, we use a single source sample over 416 deg$^2$, selected from HSC-Y3 based on having photometric redshifts (photo-$z$) greater than 0.75. For cosmological parameter inference, we use Dark Emulator combined with a halo occupation distribution prescription to model $w_{\rm p}$ and $Δ\!Σ$ down to quasi-nonlinear scales. In our baseline analysis we employ an uninformative flat prior of the residual photo-$z$ error to model a residual bias in the mean redshift of HSC source galaxies. We obtain a robust constraint on the cosmological parameters for the flat $Λ$CDM model: $S_8=σ_8(Ω_{\rm m}/0.3)^{0.5}=0.763^{+0.040}_{-0.036}$ (68% C.I.), or the best-constrained parameter given by $S'_8=σ_8(Ω_{\rm m}/0.3)^{0.22}=0.721\pm 0.028$, determined with about 4% fractional precision. Our HSC-Y3 data exhibits about 2.5$σ$ tension with the Planck inferred $S_8$ value for the $Λ$CDM model, and hints at a non-zero residual photo-$z$ bias implying that the true mean redshift of the HSC galaxies at $z\gtrsim 0.75$ is higher than that implied by the original photo-$z$ estimates.

astro-ph.CO↗
arXiv2023

Hyper Suprime-Cam Year 3 Results: Cosmology from Cosmic Shear Power Spectra

We measure weak lensing cosmic shear power spectra from the three-year galaxy shear catalog of the Hyper Suprime-Cam (HSC) Subaru Strategic Program imaging survey. The shear catalog covers $416 \ \mathrm{deg}^2$ of the northern sky, with a mean $i$-band seeing of 0.59 arcsec and an effective galaxy number density of 15 $\mathrm{arcmin}^{-2}$ within our adopted redshift range. With an $i$-band magnitude limit of 24.5 mag, and four tomographic redshift bins spanning $0.3 \leq z_{\mathrm{ph}} \leq 1.5$ based on photometric redshifts, we obtain a high-significance measurement of the cosmic shear power spectra, with a signal-to-noise ratio of approximately 26.4 in the multipole range $300<\ell<1800$. The accuracy of our power spectrum measurement is tested against realistic mock shear catalogs, and we use these catalogs to get a reliable measurement of the covariance of the power spectrum measurements. We use a robust blinding procedure to avoid confirmation bias, and model various uncertainties and sources of bias in our analysis, including point spread function systematics, redshift distribution uncertainties, the intrinsic alignment of galaxies and the modeling of the matter power spectrum. For a flat $Λ$CDM model, we find $S_8 \equiv σ_8 (Ω_m/0.3)^{0.5} =0.776^{+0.032}_{-0.033}$, which is in excellent agreement with the constraints from the other HSC Year 3 cosmology analyses, as well as those from a number of other cosmic shear experiments. This result implies a $\sim$$2σ$-level tension with the Planck 2018 cosmology. We study the effect that various systematic errors and modeling choices could have on this value, and find that they can shift the best-fit value of $S_8$ by no more than $\sim$$0.5σ$, indicating that our result is robust to such systematics.

astro-ph.CO↗
arXiv2023

Analytical Weak-lensing Shear Responses of Galaxy Properties and Galaxy Detection

Shear estimation bias from galaxy detection and blending identification is now recognized as an issue for ongoing and future weak lensing surveys. Currently, the empirical approach to correcting for this bias involves numerically shearing every observed galaxy and rerunning the detection and selection process. In this work, we provide an analytical correction for this bias that is accurate to subpercent level and far simpler to use. With the interpretation that smoothed image pixel values and galaxy properties are projections of the image signal onto a set of basis functions, we analytically derive the linear shear responses of both the pixel values and the galaxy properties (i.e., magnitude, size and shape) using the shear responses of the basis functions. With these derived shear responses, we correct for biases from shear-dependent galaxy detection and galaxy sample selection. With the analytical covariance matrix of measurement errors caused by image noise on pixel values and galaxy properties, we correct for the noise biases in galaxy shape measurement and the detection/selection process to the second-order in noise. The code used for this paper can carry out the detection, selection, and shear measurement for ~1000 galaxies per CPU second. The algorithm is tested with realistic image simulations, and we find, after the analytical correction (without relying on external image calibration) for the detection/selection bias of about $-4\%$, the multiplicative shear bias is $-0.12 \pm 0.10\%$ for isolated galaxies; and $-0.3 \pm 0.1\%$ for blended galaxies with Hyper Suprime-Cam observational condition.

astro-ph.CO↗
arXiv2023

PSFs of coadded images

We provide a detailed exploration of the connection between choice of coaddition schemes and the point-spread function (PSF) of the resulting coadded images. In particular, we investigate what properties of the coaddition algorithm lead to the final coadded image having a well-defined PSF. The key elements of this discussion are as follows: 1. We provide an illustration of how linear coaddition schemes can produce a coadd that lacks a well-defined PSF even for relatively simple scenarios and choices of weight functions. 2. We provide a more formal demonstration of the fact that a linear coadd only has a well-defined PSF in the case that either (a) each input image has the same PSF or (b) the coadd is produced with weights that are independent of the signal. 3. We discuss some reasons that two plausible nonlinear coaddition algorithms (median and clipped-mean) fail to produce a consistent PSF profile for stars. 4. We demonstrate that all nonlinear coaddition procedures fail to produce a well-defined PSF for extended objects. In the end, we conclude that, for any purpose where a well-defined PSF is desired, one should use a linear coaddition scheme with weights that do not correlate with the signal and are approximately uniform across typical objects of interest.

astro-ph.IM↗
arXiv2022

The Third Gravitational Lensing Accuracy Testing (GREAT3) Challenge Handbook

The GRavitational lEnsing Accuracy Testing 3 (GREAT3) challenge is the third in a series of image analysis challenges, with a goal of testing and facilitating the development of methods for analyzing astronomical images that will be used to measure weak gravitational lensing. This measurement requires extremely precise estimation of very small galaxy shape distortions, in the presence of far larger intrinsic galaxy shapes and distortions due to the blurring kernel caused by the atmosphere, telescope optics, and instrumental effects. The GREAT3 challenge is posed to the astronomy, machine learning, and statistics communities, and includes tests of three specific effects that are of immediate relevance to upcoming weak lensing surveys, two of which have never been tested in a community challenge before. These effects include realistically complex galaxy models based on high-resolution imaging from space; spatially varying, physically-motivated blurring kernel; and combination of multiple different exposures. To facilitate entry by people new to the field, and for use as a diagnostic tool, the simulation software for the challenge is publicly available, though the exact parameters used for the challenge are blinded. Sample scripts to analyze the challenge data using existing methods will also be provided. See http://great3challenge.info and http://great3.projects.phys.ucl.ac.uk/leaderboard/ for more information.

astro-ph.CO↗
arXiv2022

GREAT3 results I: systematic errors in shear estimation and the impact of real galaxy morphology

We present first results from the third GRavitational lEnsing Accuracy Testing (GREAT3) challenge, the third in a sequence of challenges for testing methods of inferring weak gravitational lensing shear distortions from simulated galaxy images. GREAT3 was divided into experiments to test three specific questions, and included simulated space- and ground-based data with constant or cosmologically-varying shear fields. The simplest (control) experiment included parametric galaxies with a realistic distribution of signal-to-noise, size, and ellipticity, and a complex point spread function (PSF). The other experiments tested the additional impact of realistic galaxy morphology, multiple exposure imaging, and the uncertainty about a spatially-varying PSF; the last two questions will be explored in Paper II. The 24 participating teams competed to estimate lensing shears to within systematic error tolerances for upcoming Stage-IV dark energy surveys, making 1525 submissions overall. GREAT3 saw considerable variety and innovation in the types of methods applied. Several teams now meet or exceed the targets in many of the tests conducted (to within the statistical errors). We conclude that the presence of realistic galaxy morphology in simulations changes shear calibration biases by $\sim 1$ per cent for a wide range of methods. Other effects such as truncation biases due to finite galaxy postage stamps, and the impact of galaxy type as measured by the Sérsic index, are quantified for the first time. Our results generalize previous studies regarding sensitivities to galaxy size and signal-to-noise, and to PSF properties such as seeing and defocus. Almost all methods' results support the simple model in which additive shear biases depend linearly on PSF ellipticity.

astro-ph.CO↗
arXiv2022

Modeling halo and central galaxy orientations on the SO(3) manifold with score-based generative models

Upcoming cosmological weak lensing surveys are expected to constrain cosmological parameters with unprecedented precision. In preparation for these surveys, large simulations with realistic galaxy populations are required to test and validate analysis pipelines. However, these simulations are computationally very costly -- and at the volumes and resolutions demanded by upcoming cosmological surveys, they are computationally infeasible. Here, we propose a Deep Generative Modeling approach to address the specific problem of emulating realistic 3D galaxy orientations in synthetic catalogs. For this purpose, we develop a novel Score-Based Diffusion Model specifically for the SO(3) manifold. The model accurately learns and reproduces correlated orientations of galaxies and dark matter halos that are statistically consistent with those of a reference high-resolution hydrodynamical simulation.

astro-ph.GA↗
arXiv2022

Galaxies on graph neural networks: towards robust synthetic galaxy catalogs with deep generative models

The future astronomical imaging surveys are set to provide precise constraints on cosmological parameters, such as dark energy. However, production of synthetic data for these surveys, to test and validate analysis methods, suffers from a very high computational cost. In particular, generating mock galaxy catalogs at sufficiently large volume and high resolution will soon become computationally unreachable. In this paper, we address this problem with a Deep Generative Model to create robust mock galaxy catalogs that may be used to test and develop the analysis pipelines of future weak lensing surveys. We build our model on a custom built Graph Convolutional Networks, by placing each galaxy on a graph node and then connecting the graphs within each gravitationally bound system. We train our model on a cosmological simulation with realistic galaxy populations to capture the 2D and 3D orientations of galaxies. The samples from the model exhibit comparable statistical properties to those in the simulations. To the best of our knowledge, this is the first instance of a generative model on graphs in an astrophysical/cosmological context.

astro-ph.GA↗
arXiv2022

An empirical approach to model selection: weak lensing and intrinsic alignments

In cosmology, we routinely choose between models to describe our data, and can incur biases due to insufficient models or lose constraining power with overly complex models. In this paper we propose an empirical approach to model selection that explicitly balances parameter bias against model complexity. Our method uses synthetic data to calibrate the relation between bias and the $χ^2$ difference between models. This allows us to interpret $χ^2$ values obtained from real data (even if catalogues are blinded) and choose a model accordingly. We apply our method to the problem of intrinsic alignments -- one of the most significant weak lensing systematics, and a major contributor to the error budget in modern lensing surveys. Specifically, we consider the example of the Dark Energy Survey Year 3 (DES Y3), and compare the commonly used nonlinear alignment (NLA) and tidal alignment & tidal torque (TATT) models. The models are calibrated against bias in the $Ω_m - S_8$ plane. Once noise is accounted for, we find that it is possible to set a threshold $Δχ^2$ that guarantees an analysis using NLA is unbiased at some specified level $Nσ$ and confidence level. By contrast, we find that theoretically defined thresholds (based on, e.g., $p-$values for $χ^2$) tend to be overly optimistic, and do not reliably rule out cosmological biases up to $\sim 1-2σ$. Considering the real DES Y3 cosmic shear results, based on the reported difference in $χ^2$ from NLA and TATT analyses, we find a roughly $30\%$ chance that were NLA to be the fiducial model, the results would be biased (in the $Ω_m - S_8$ plane) by more than $0.3σ$. More broadly, the method we propose here is simple and general, and requires a relatively low level of resources. We foresee applications to future analyses as a model selection tool in many contexts.

astro-ph.CO↗