arXiv subjects

Rachel Mandelbaum

Publications and source records attributed to Rachel Mandelbaum.

At least 19 recordsLinked to original sources

arXiv2026

The Cross-Survey Decade: A Call to Action

By 2027, three flagship wide-field surveys will be operating simultaneously from ground and space, observing overlapping sky and representing more than $6 billion in US and European public investment. Together they will produce overlapping petabyte-scale datasets across thousands of square degrees. This is a different class of challenge: the observations are no longer the bottleneck; realizing their joint scientific return now depends on shared computational infrastructure and coordination. Decades of community studies show that combining these datasets does more than improve precision. For science ranging from weak lensing to transient discovery and Galactic-plane astronomy, joint processing and analysis can unlock capabilities no single survey provides alone. Yet the required infrastructure -- joint pixel-level processing, cross-calibration and validation, interoperable data access, and the people to build and sustain it -- falls outside any single mission or institution's mandate. We issue a call to action for cross-survey science infrastructure, built around four pillars: (1) joint pixel-level processing and validation; (2) an AI-ready data substrate for scientific foundation models; (3) standardized, interoperable data access across surveys, democratizing participation in astrophysical discovery; and (4) dedicated personnel and career pathways. We outline concrete steps for policymakers, agencies, observatories, universities, the research community, and philanthropy, and argue that the moment to act is now, while foundational technical choices can still be aligned at a fraction of the cost of reconciling them later.

astro-ph.IM↗

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.

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arXiv2026

Modeling the impact of filter-substrate refraction in the Roman point spread function

For broadband imaging surveys, filter-substrate refraction causes light at different wavelengths to follow slightly different paths through the filter substrate before reaching the detector. This effect produces two chromatic perturbations to the point spread function (PSF): a shift in the effective focal position along the optical axis (longitudinal shift), which manifests as a defocus-like perturbation, and a wavelength-dependent displacement of the image position in the focal plane (lateral shift), which manifests as image decentering. Using image simulations, we provide the first study of these two effects independently across all eight Roman imaging bands and over the full focal plane. We compute the resulting PSF and photometric errors from images with and without the effect included, and compare the magnitude of the effect to the Roman science requirements. We find that the lateral shift is the dominant contribution, producing PSF size and ellipticity residuals in most bands of order ~0.3-0.4%. These exceed the Roman science requirements for weak lensing by roughly an order of magnitude. The effect is also strongly field dependent, increasing toward the edges of the focal plane. By contrast, flux residuals remain below one third of the 1% requirement for most bands, except in R062 and W146. We find the longitudinal shift to be subdominant and negligible in most bands, including the weak lensing bands. Finally, we implement the dominant lateral-shift effect in a framework suitable for large-scale image simulations and validate that the resulting PSF size and shape changes are accurately reproduced. Overall, we find that filter-substrate refraction is a relevant chromatic effect for Roman PSF modeling, and we provide tools to model and incorporate it in large-scale image simulations.

astro-ph.IM↗

arXiv2026

Hyrax: An Extensible Framework for Rapid ML Experimentation and Unsupervised Discovery in the Era of Rubin, Roman, and Euclid

The NSF-DOE Vera C. Rubin Observatory, Roman Space Telescope, Euclid, and other next-generation surveys will deliver imaging, spectroscopic, and time-domain data at scales that increasingly shift the bottleneck in astronomical machine learning (ML) projects from model design to infrastructure. We present Hyrax, an open-source, modular, GPU-enabled Python framework that supports the full ML lifecycle in astronomy: from data acquisition and training to inference and experiment comparison, with capabilities including multimodal dataset support, integrated vector databases for similarity search, and interactive two- and three-dimensional latent-space exploration for unsupervised discovery. We demonstrate Hyrax's versatility through five representative applications on real survey data: (i) unsupervised representation learning on $\sim 4\times10^5$ Rubin Legacy Survey of Space and Time (LSST) Data Preview 1 (DP1) galaxies, surfacing new merger and low-surface-brightness candidates missing from reference Euclid and Dark Energy Survey catalogs, while also isolating imaging artifacts -- all without labeled training data; (ii) hybrid density-based clustering for identifying cluster-scale gravitational lens candidates in DP1 data; (iii) multimodal early-time transient classification in the Zwicky Transient Facility leveraging light curves, spectra, images, and metadata; (iv) supervised false-positive filtering in shift-and-stack searches for distant solar system objects in the Dark Energy Camera Ecliptic Exploration Project survey; and (v) supervised detection of semi-resolved dwarf galaxies in Hyper Suprime-Cam and LSST-like imaging using synthetic source injection. Together, these results demonstrate that Hyrax provides astronomy-specific ML infrastructure that enables systematic discovery and rapid methodological iteration across next-generation astronomical surveys.

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arXiv2026

The Galaxy Luminosity Functions in ASTRID: Predictions for LSST

We present validated and forward-modelled galaxy luminosity functions and photometric predictions for the Vera C. Rubin Observatory Legacy Survey of Space and Time using the ASTRID cosmological hydrodynamical simulation. Galaxy magnitudes are computed by combining stellar population synthesis modeling with a physically motivated dust attenuation prescription in which the optical depth scales with metal surface density. The dust model is calibrated at z = 0 using SDSS luminosity functions and tested at intermediate redshifts (z = 0.5, 1.0, and 1.5) in rest-frame B, V , R, and I bands. We find that the attenuated luminosity functions reproduce observed galaxy statistics across multiple wavelengths and redshifts. Using this calibrated framework, we construct LSST-ready mock photometric catalogs over 0 <= z <= 2 in steps of Delta z = 0.1, containing ~378 million galaxies. We provide predicted apparent-magnitude luminosity functions in the LSST ugrizy bands, derive best-fit Schechter parameters as a compact analytic representation, and compute differential and cumulative galaxy number counts as a function of survey depth from Year 1 to Year 10.

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arXiv2026

LightCurveLynx: Forward Modeling of Time-Domain Surveys with Application to ZTF SN Ia DR2

We present LightCurveLynx, a flexible and extensible software framework for end-to-end forward modeling time-domain light curves. Given the growing need for realistic simulations in the time-domain astronomy community, LightCurveLynx is designed to support a wide range of applications, including the development and validation of analysis pipelines, the optimization of survey strategies, and simulation-based inference studies. Realistic simulations can be generated from real survey metadata, forecasted survey plans, or user-defined mock survey strategies. We demonstrate the functionality of LightCurveLynx by generating a realistic simulation of Type Ia supernovae that is representative of the ZTF SN Ia Data Release 2 dataset and perform extensive comparisons between the simulated and observed samples to validate the software. The simulation shows excellent agreement with the data in parameter distributions (with the Kullback-Leibler divergence values around 0.01-0.02) and in noise properties. The Hubble diagram generated from the simulation also indicates that the sample is complete up to redshift 0.06, which is consistent with previous studies. Our results confirm that LightCurveLynx is robust, accurate, and ready for community use and contribution.

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arXiv2026

cosmo-numba: B-modes and COSEBIs computations accelerated by Numba

Weak gravitational lensing is a widely used probe in cosmological analysis. It allows astrophysists to understand the content and evolution of the Universe. We are entering an era where we are not limited by the data volume but by systematic uncertainties. It is in this context that we present here a simple python-based software package to help in the computation of E-/B-mode decomposition, which can be use for systematic checks or science analysis. As we demonstrate, our implementation has both the high precision and speed required to perform this kind of analysis while avoiding a scenario wherein either numerical precision or computational time is a significant limiting factor.

astro-ph.IM↗

arXiv2026

Optimizing the Roman Space Telescope High-Latitude Wide Area Survey for mitigating chromatic PSF effects on shear measurement

Chromatic point-spread-function (PSF) effects arise from differences between the spectral energy distributions (SEDs) of stars, used to model the PSF, and galaxies, used to measure shape distortions due to weak gravitational lensing, or shear. For the Roman Space Telescope, these effects can bias shear measurement and cosmological inference, making them an important systematic effect for shear calibration. These biases depend sensitively on survey design choices, particularly filter coverage and the availability of color information. In this work, we investigate how different Roman survey strategies affect the ability to mitigate chromatic PSF effects and whether residual biases in shear propagate into cosmological inference. Using realistic image simulations, we infer per-galaxy near-infrared SED slopes via radial basis function regression for four-, three-, two-, and single-band survey configurations. We quantify residual shear calibration biases under representative and non-representative training assumptions and propagate these biases into Markov Chain Monte Carlo analyses of cosmic shear and $3\times2$-point statistics. We find that three- and four-band strategies can reduce residual shear biases to $|m|\lesssim10^{-3}$, lowering the induced shifts in the lensing amplitude from $\Delta S_8 \sim 0.6\sigma$ (cosmic shear) and $\Delta S_8 \sim 0.7\sigma$ ($3\times2$-pt) in the uncorrected case to $\Delta S_8 \lesssim 0.07\sigma$. Single-band surveys remain limited, with residual shear biases reaching or exceeding $|m|\sim 2\times 10^{-3}$ in some tomographic bins. Average, sample-wide corrections reduce but do not eliminate chromatic systematics, leaving residual biases of $\Delta S_8 \sim 0.1\sigma$. Overall, our results demonstrate that we can robustly correct for these effects in the recommended three-band medium tier, but may encounter residual biases in a single-band wide tier.

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.

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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.

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arXiv2025

AT2025ulz and S250818k: Investigating early time observations of a subsolar mass gravitational-wave binary neutron star merger candidate

Over the past LIGO--Virgo--KAGRA (LVK) observing runs, it has become increasingly clear that identifying the next electromagnetic counterparts to gravitational-wave (GW) neutron star mergers will likely be more challenging compared to the case of GW170817. The rarity of these GW events, and their electromagnetic counterparts, motivates rapid searches of any candidate binary neutron star (BNS) merger detected by the LVK. We present our extensive photometric and spectroscopic campaign of the candidate counterpart AT2025ulz to the low-significance GW event S250818k, which had a ${\sim} 29\%$ probability of being a BNS merger. We demonstrate that during the first five days, the luminosity and color evolution of AT2025ulz are consistent with both kilonova and shock cooling models, although a Bayesian model comparison shows preference for the shock cooling model, underscoring the ambiguity inherent to early data obtained over only a few days. Continued monitoring beyond this window reveals a rise and color evolution incompatible with kilonova models and instead consistent with a supernova. This event emphasizes the difficulty in identifying the electromagnetic counterparts to BNS mergers and the significant allotment of observing time necessary to robustly differentiate kilonovae from impostors.

astro-ph.HE↗

arXiv2025

Deep-Field Analytical Calibration

The next generation of imaging surveys, including the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST), Euclid, and the Nancy Grace Roman Space Telescope, will place unprecedented constraints on cosmology using weak gravitational lensing. To fully exploit their statistical power, shear measurement methods must achieve sub-percent accuracy while mitigating systematic biases from noise, the point-spread function (PSF), blending, and shear-dependent detection. The analytical calibration framework (\texttt{AnaCal}) has demonstrated such accuracy but requires adding noise to images, reducing their effective depth. We introduce Deep-Field Analytical Calibration (\textsc{deep-field~}\texttt{AnaCal}), an extension of \texttt{AnaCal} that leverages deep-field images to compute shear responses while preserving wide-field statistical power. We validate the method on isolated and blended galaxy simulations with LSST-like seeing and noise, showing it meets the stringent requirement of multiplicative bias $|m| < 3\times10^{-3}$ at 99.7% confidence. Relative to standard \texttt{AnaCal} on wide-field images, this method improves effective galaxy number density from $17$ to $30$ arcmin$^{-2}$ for simulated 10-year LSST data. Assuming deep fields with $10\times$ the exposure of wide fields, we find the pixel noise variance in shear estimation is reduced by $30%$ and the overall shear uncertainty by $\sim 25%$. Finally, we assess sample variance impacts using the LSST Deep Drilling Fields strategy, finding an equivalent calibration uncertainty of $\lesssim 0.3%$. These results establish \textsc{deep-field~}\texttt{AnaCal} as a promising approach for shear calibration in upcoming weak lensing surveys.

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arXiv2025

Forecasting the Impact of Source Galaxy Photometric Redshift Uncertainties on the LSST $3\times2$pt Analysis

Photometric redshifts of the source galaxies are a key source of systematic uncertainty in the Rubin Observatory Legacy Survey of Space and Time (LSST)'s galaxy clustering and weak lensing analysis, i.e., the $3\times 2$pt analysis. This paper introduces a Fisher forecast code FisherA2Z for the LSST Year 10 (Y10) $3 \times 2$pt and cosmic shear analyses, utilizing a 15-parameter redshift distribution model, with one redshift bias, variance, and outlier rate per tomographic bin. FisherA2Z employs the Core Cosmology Library CCL to compute the large-scale structure power spectrum and incorporates a four-parameter nonlinear alignment model for intrinsic alignments. We evaluate the impact of marginalizing over redshift distribution parameters on weak lensing, forecast biases in cosmological parameters due to redshift errors, and assess cosmological parameter sensitivity to redshift systematic parameters using decision trees. The sensitivity study reveals that for LSST $3\times2$pt analysis, $S_8$ is most sensitive to the mean redshift of the fourth out of the five source tomographic bins, while other cosmological parameters possess different sensitivities. Additionally, we provide cosmological analysis forecasts based on different scenarios of spectroscopic training datasets. We find that the figures-of-merit for the cosmological results increase with the number of spectroscopic training galaxies, and with the completeness of the training set above $z=1.6$, assuming the redshift information comes solely from the training set galaxies without other external constraints.

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arXiv2025

Modelling Galaxy Clustering and Tomographic Galaxy-Galaxy Lensing with HSC Y3 and SDSS using the Point-Mass Correction Model and Redshift Self-Calibration

The combination of galaxy-galaxy weak lensing and galaxy clustering is a powerful probe of the cosmological model, and exploration of how to best model and extract this information from the signals is essential. We present the measurement of the galaxy-galaxy weak lensing signals using the SDSS DR11 spectroscopic galaxies as lens galaxies, and the HSC Y3 shear catalog as source galaxies, binned into four tomographic bins by their photometric redshift. The SDSS DR11 galaxies, with a redshift range $0.15<z<0.7$, are binned into three redshift bins, each as a probe for measuring the projected correlation function, $w_p(R_p)$. We measure the galaxy-galaxy lensing signal $\Delta \Sigma (R_p)$ in 12 lens-source bin pairs and show that there is no evidence for significant systematic biases in the measurement with null testing. We combine our $w_p(R_p)$ and $\Delta \Sigma (R_p)$ ($2\times2$pt) data vectors and perform likelihood inference with a flat $\Lambda$CDM model. For $\Delta \Sigma (R_p)$, we extend the lower limit of the scale cut compared to previous HSC Y3 analyses to $2 h^{-1}$Mpc by including a point-mass correction term in addition to the minimal bias model. We present various tests to validate our model and provide extended consistency tests. In the $\Lambda$CDM context, our fiducial model yields $S_8 = 0.804^{+0.051}_{-0.051}$. The $2\times2$pt data vector provides redshift parameter constraints for the third and fourth redshift bins $\Delta z_3 = -0.079^{+0.074}_{-0.084}$, and $\Delta z_4 = -0.203^{+0.167}_{-0.206}$, which is consistent with results from the previous tomographic cosmic shear studies, and serves as the foundation for a future $3\times 2$pt analysis.

astro-ph.CO↗

arXiv2025

Cosmology and Source Redshift Constraints from Galaxy Clustering and Tomographic Weak Lensing with HSC Y3 and SDSS using the Point-Mass Correction Model

The combination of galaxy clustering and weak lensing is a powerful probe of the cosmology model. We present a joint analysis of galaxy clustering and weak lensing cosmology using SDSS data as the tracer of dark matter (lens sample) and the HSC Y3 dataset as source galaxies. The analysis divides HSC Y3 galaxies into four tomographic bins for both galaxy-galaxy lensing and cosmic shear measurements, and employs a point-mass correction model to utilize galaxy-galaxy lensing signals down to 2$h^{-1}$Mpc, extending up to 70$h^{-1}$Mpc. These strategies enhance the signal-to-noise ratio of the galaxy-galaxy lensing data vector. Using a flat $\Lambda$CDM model, we find $S_8 = 0.780^{+0.029}_{-0.030}$, and using a $w$CDM model, we obtain $S_8 = 0.756^{+0.038}_{-0.036}$ with $w = -1.176^{+0.310}_{-0.346}$. We apply uninformative priors on the redshift mean-shift parameters for the third and fourth tomographic bins. Leveraging the self-calibration power of tomographic weak lensing, we measure $\Delta z_3 = -0.112^{+0.046}_{-0.049}$ and $\Delta z_4 = -0.185^{+0.071}_{-0.081}$, in agreement with previous HSC Y3 results. This demonstrates that weak lensing self-calibration can achieve redshift constraints comparable to other methods such as photometric and clustering redshift calibration.

astro-ph.CO↗

arXiv2025

Variability-finding in Rubin Data Preview 1 with LSDB

The Vera C. Rubin Observatory recently released Data Preview 1 (DP1) in advance of the upcoming Legacy Survey of Space and Time (LSST), which will enable boundless discoveries in time-domain astronomy over the next ten years. DP1 provides an ideal sandbox for validating innovative data analysis approaches for the LSST mission, whose scale challenges established software infrastructure paradigms. This note presents a pair of such pipelines for variability-finding using powerful software infrastructure suited to LSST data, namely the HATS (Hierarchical Adaptive Tiling Scheme) format and the LSDB framework, developed by the LSST Interdisciplinary Network for Collaboration and Computing (LINCC) Frameworks team. This article presents a pair of variability-finding pipelines built on LSDB, the HATS catalog of DP1 data, and preliminary results of detected variable objects, two of which are novel discoveries.

astro-ph.IM↗

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↗

arXiv2025

Chromatic Effects on the PSF and Shear Measurement for the Roman Space Telescope High-Latitude Wide Area Survey

Weak gravitational lensing (WL) is a key cosmological probe that requires precise measurement of galaxy images to infer shape distortions, or shear, and constrain cosmology. Accurate estimation of the Point Spread Function (PSF) is crucial for shear measurement, but the wavelength dependence of the PSF introduces chromatic biases that can systematically impact shear inference. We focus on biases arising from spectral energy distribution (SED) differences between stars, used for PSF modeling, and galaxies, used for shear measurement. We investigate these effects in $\textit{Roman's}$ four design reference mission WL bands (Y106, J129, H158, F184) and wide filter (W146). Using $\textit{Roman}$-like image simulations, we quantify the induced shear biases and compare them to requirements on those biases. Multiplicative biases over all galaxies hover around $\sim$0.2% in the WL bands and 2% in the wide filter, exceeding the mission requirement of $|m| < 0.032\%$ and relaxed requirement of $|m| < 0.1\%$. In individual redshift bins, biases can reach 0.4$\unicode{x2013}$0.9% for the WL bands and 3$\unicode{x2013}$6% for the wide filter. Additive biases remain acceptable in the WL bands but exceed systematic limits in the wide filter. We develop and test PSF-level corrections, showing that a first-order correction reduces biases within survey requirements for the WL bands; however, higher-order terms are necessary for the wide filter. Our results highlight the necessity of chromatic corrections for precision WL with $\textit{Roman}$ and provide a framework for mitigating these biases. Finally, we compare analytical color-based corrections to self-organizing maps (SOMs) and find that both methods effectively reduce biases.

astro-ph.CO↗