Pith. sign in

REVIEW 2 major objections 5 minor 1 cited by

The Roman View of Strong Gravitational Lenses

T0 review · 2 major / 5 minor · reviewed 2026-08-07 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗

Pith's one-line read If the simulation holds, Roman's planned wide survey will yield about 160,000 detectable galaxy-galaxy strong lenses, of which roughly 500 are bright enough for dark matter substructure characterization.

desk verdict Solid, reproducible Roman strong-lens yield forecast with a real new pipeline and dataset; the headline numbers need error bars because the stellar-to-halo mass conversion is a large unpropagated lever. read the letter →

arxiv 2506.03390 v1 pith:IQVUUUMI submitted 2025-06-03 astro-ph.CO

Bryce Wedig , Tansu Daylan , Simon Birrer , Francis-Yan Cyr-Racine , Cora Dvorkin , Douglas P. Finkbeiner , Alan Huang , Xiaosheng Huang
show 8 more authors
Rahul Karthik Narayan Khadka Priyamvada Natarajan Anna M. Nierenberg Annika H. G. Peter Justin D. R. Pierel Xianzhe TZ Tang Risa H. Wechsler
This is my paper
classification astro-ph.CO
keywords stronggravitationallensingRomanSpaceTelescopegalaxy-galaxylensesdarkmattersubstructuresubhalomassfunctionsurveyyieldforecastwide-fieldimagesimulationHighLatitudeWideArea
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper argues that the Nancy Grace Roman Space Telescope's planned High Latitude Wide Area Survey (HLWAS) will turn strong gravitational lensing from a rare, hand-verified phenomenon into a population-wide probe. Simulating galaxies out to redshift 6, selecting those that form detectable lens systems, and generating images with realistic detector effects, the authors predict roughly 160,000 detectable galaxy-galaxy strong lenses in the 1700 square degree survey, about 500 of them with signal-to-noise high enough for detailed dark matter substructure analysis. That matters because current space-based lens samples number in the hundreds, and substructure studies need many sharp images to constrain dark matter models such as warm dark matter and self-interacting dark matter. The paper also shows that the field-dependent point-spread function of Roman's wide-field instrument is a systematic any substructure pipeline must model.

What carries the argument

The load-bearing machinery is a population simulation pipeline that starts from the stellar-mass-to-halo-mass conversion $M_\mathrm{vir}/M_* = (51\pm36)(1+z)^{0.9\pm1.8}$ fitted to 63 lenses with $z<0.9$, uses it to assign every lensing galaxy a total halo mass, then generates Einstein radii, source-plane cross-sections, and CDM subhalo populations (truncated NFW dark matter profiles, mass range $10^6$ to $10^{10}$ solar masses, logarithmic slope $-1.9$). Detectability is set by eight criteria including angular separation, magnification, and a signal-to-noise definition that counts lensing-galaxy light as noise; the SNR $>200$ cut selects the roughly 500 characterizable systems. Image simulation flows through ray-shooting to a supersampled surface-brightness grid, PSF convolution at a field-dependent focal-plane position, and a sequence of Wide Field Instrument detector effects. The role of the machinery is to turn survey design parameters such as exposure time, filters, and area into a concrete yield of lenses and a concrete assessment of which systematics must be modeled.

What would settle it

After Roman launches, measure the stellar-mass-to-halo-mass relation for the actual lensing galaxies (e.g., from stacked weak lensing or velocity dispersions) at $z>1$; if at $z\approx2$ the implied $M_\mathrm{vir}/M_*$ falls outside $(51\pm36)(1+z)^{0.9\pm1.8}$ with high significance, the yield simulation's input is wrong. A direct check is to compare the detected HLWAS count of galaxy-galaxy lenses after the survey is complete with the predicted roughly 160,000; a discrepancy of more than a factor of two would falsify the forecast.

Watch

Extended reading notes

Core claim

The central discovery is a forecast: for the fiducial single 146-second exposures of the HLWAS, the survey will contain around 160,000 detectable galaxy-galaxy strong lenses, equivalent to about 95 per square degree, of which approximately 500 systems (0.28 per square degree) will have signal-to-noise ratio (SNR) exceeding 200 and be amenable to detailed substructure characterization. The prediction is built from a simulated galaxy population with lensing masses rooted in a stellar-mass-to-halo-mass relation, a cold dark matter subhalo population with masses from $10^6$ to $10^{10}$ solar masses and logarithmic mass function slope $-1.9$, and full image simulation including Poisson noise, read noise, dark current, nonlinearity, interpixel capacitance, and field-dependent point-spread functions. Coadding four exposures would raise the upper-limit yield to 510,000 detectable and 5,600 characterizable systems. The authors further find that the variation of the point-spread function across the focal plane is not ignorable: across most detectors, PSF variation shifts single-pixel power more than the presence or absence of low-mass subhalos, and the mean mass of the least massive detectable subhalo varies by about 5% (up to 25% in sensitive systems) across the field, tracking wavefront error.

Load-bearing premise

The whole forecast leans on one empirical relation, fitted to 63 lenses below redshift 0.9, that converts stellar mass to halo mass and is extrapolated to lensing galaxies out to redshift 3; if that relation drifts at high redshift, the predicted 160,000 and 500 numbers would shift substantially.

Editorial extensions

If this is right

  • The HLWAS will produce on the order of 160,000 detectable strong lenses, roughly 27 per 0.281 square degree exposure, turning strong lensing into a survey-scale statistic rather than a curated sample.
  • About 500 systems will have SNR greater than 200, sufficient for detailed substructure characterization; population-level dark matter constraints such as the warm dark matter thermal relic mass and the cold dark matter subhalo mass function will move from tens of lenses to hundreds.
  • The sample extends to higher lens redshifts than HST's, with over 12,000 lensing galaxies at $z\geq1$, enabling mass-profile and galaxy-evolution studies beyond the current $z<1$ frontier.
  • Coadding four 146-second exposures could push upper-limit yields to roughly 510,000 detectable and 5,600 characterizable lenses, and the High Latitude Time Domain Survey adds thousands more characterizable systems.
  • Field-dependent PSF variation across the Wide Field Instrument must be modeled in substructure inference; averaged over the focal plane the least-massive detectable subhalo mass varies by about 5% (up to 25%), tracking wavefront error.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • If the 160,000-yield holds, the bottleneck shifts from discovery to confirmation: automated selection and machine-learning vetting will be needed to turn 160,000 candidates into a clean sample, which is exactly what the released simulated images can train.
  • The roughly 500 high-SNR systems will skew toward bright, low-redshift lenses ($z\lesssim0.6$), so the first Roman substructure constraints will likely anchor at low redshift; the high-redshift majority contributes statistical power for population-level analyses but not single-subhalo depth.
  • The predicted overlap between Roman's HLWAS and Euclid's Wide Survey could be exploited for forced photometry and redshift improvement, effectively raising the usable yield and sharpening substructure inference beyond either survey alone.
  • A testable extension of the same machinery is to run it for cluster-scale strong lenses, where PSF variation across the scene matters, and for realistic coadded exposures with correlated noise, which the paper leaves to future work.
Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

2 major / 5 minor

Summary. This paper presents a forward simulation pipeline (mejiro, built on SLSim, SkyPy, pyHalo, Lenstronomy, GalSim, and WebbPSF) to forecast the yield of galaxy-galaxy strong gravitational lenses in the Nancy Grace Roman Space Telescope's planned High Latitude Wide Area Survey (HLWAS). The pipeline generates a population of Sersic lens and source galaxies out to z=6, assigns main halo masses through an empirical stellar-to-total-mass relation, adds CDM subhalo populations with pyHalo, applies realistic WFI detector effects, and applies detectability criteria including an SNR>20 threshold. For single 146-second exposures, the authors predict roughly 160,000 detectable strong lenses over the full 1700 deg^2 survey and about 500 systems with SNR>200 that they describe as amenable to detailed substructure characterization. The paper also analyzes the impact of the field-dependent PSF on milli-lensing signals and on single-subhalo detection thresholds, and compares Roman's sensitivity with HST and Euclid. The simulated dataset and code are made publicly available.

Significance. If the forecast is robust, this paper will be a valuable resource for Roman preparatory science: it identifies the expected scale of the strong-lens sample, quantifies a previously underappreciated systematic (the variation of the PSF across the WFI focal plane), and provides a publicly available dataset of 16,214 simulated Roman exposures suitable for training detection and inference pipelines. The methodology is detailed and largely reproducible, with external, independent components (SkyPy, SLSim, pyHalo, Lenstronomy, GalSim, WebbPSF) and clearly stated limitations (no line-of-sight halos, Sersic-only morphologies, single-exposure treatment). The code and data releases are explicit strengths. The main weakness is that the headline yield numbers are point estimates without propagated uncertainties or a sensitivity analysis for the most load-bearing input, the stellar-to-halo-mass conversion of Eq. (1).

major comments (2)
  1. [Section 3.1] The stellar-to-halo-mass conversion Mvir/M* = (51±36)(1+z)^(0.9±1.8) is the single most load-bearing input in the pipeline: it sets the main halo mass for every simulated lens galaxy, which in turn sets the EPL normalization, Einstein radii, image separations, magnifications, and the generated subhalo populations. The 1-sigma normalization spans roughly 15 to 87 at z=0, and the redshift slope is 0.9±1.8, yet the headline yields of ~160,000 detectable and ~500 characterizable systems are reported in Section 3.1 as point estimates with no propagated uncertainty or sensitivity analysis. The paper itself states that inaccuracies in this conversion would skew the subhalo populations, but it does not quantify how the yield responds to the allowed range of Eq. (1). Because the predicted lens galaxies are concentrated at z<1 (<8% at z>1, as the text notes), the low-redshift normalization scatter alone could shift the yield by an order-unity factor. I request a sensitivity analysis that varies the normalization and slope within their quoted uncertainties and reports the resulting ranges for the detectable and characterizable yields.
  2. [Section 3.1] The yield prediction is sensitive to the adopted detectability thresholds, but the dependence is not tested. Specifically, the criteria in Section 2.1.5 include SNR>20, magnification greater than three, and an image separation of at least 0.3 arcsec, and the characterizable yield is defined by SNR>200. Earlier forecasts for Roman quote values between ~8 and ~52 detectable lenses per deg^2 (Weiner et al. 2020; Holloway et al. 2023; Ferrami & Wyithe 2024), and the present work reports ~95 per deg^2, i.e., a factor of two or more above all of them. The authors attribute this to the inclusion of massive early-type galaxies, but a reader cannot tell how much of the difference comes from the specific threshold choices. A robustness test varying the SNR threshold (e.g., 10, 20, 30), the magnification threshold, and the angular separation threshold would materially support the central claim and would clarify the comparison with prior work. Without such a sensitivity analysis, the headline numbers are tied to a single choice of cuts.
minor comments (5)
  1. [Section 2.2.3] The PSF is described as 'achromatic PSF defined at the appropriate effective wavelength.' Please clarify whether the simulated PSF is monochromatic at each filter's effective wavelength rather than a band-integrated PSF; if so, consider quantifying the impact for the broad F184 filter, which has the largest FWHM and is used in the field-dependence demonstration.
  2. [Section 4.2] In the single-subhalo detection study, the added subhalo is modeled as an NFW profile with concentration fixed to c=10, while Section 2.1.2 uses truncated NFW profiles with concentrations from Diemer & Joyce (2019). Please justify the simplified concentration choice and note whether it leads to optimistic or conservative detectability estimates.
  3. [Table 1 caption] The caption contains a typo: 'T able 1' should read 'Table 1'.
  4. [Section 2.1.5] The definition of the SNR>1 pixel mask used to identify 'regions' is given only qualitatively; for reproducibility, please state the exact adjacency rule and any minimum number of pixels required to form a region.
  5. [Section 4.5] The statement that 'Roman is undersampled blueward of about 1.2 microns' is important for the coaddition discussion; please cite the relevant Roman technical document or provide a reference to support this quantitative claim.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the Roman lens yield is a Monte Carlo prediction from an explicit simulation pipeline with external calibrations and independent cross-checks.

full rationale

The paper's central claims (160,000 detectable strong lenses and ~500 characterizable systems in the HLWAS) are outputs of a simulation pipeline, not quantities fitted into the inputs. The main halo mass is set by Eq. (1), an empirical stellar-to-halo mass relation taken from Lagattuta et al. (2010); the detectability criteria (SNR>20, Einstein radius and image separation thresholds, magnification cuts) are fixed a priori with literature justification; and the characterizability threshold SNR>200 is chosen by comparison with Despali et al. (2022), not by tuning to the reported count. The paper explicitly cross-checks its characterizable yield (0.28 per deg²) against the independent Daylan & Birrer (2023) forecast (0.29 per deg²) rather than using it as an input. Self-citations, including pyHalo and mejiro, refer to public code and prior methodological work; they are not invoked as uniqueness theorems or ansatz-forcing results, and the subhalo population does not drive the headline yield because detectability SNR is computed with a smooth macromodel. The acknowledged sensitivity of the yield to Eq. (1) is an input-uncertainty limitation, explicitly discussed in Section 2.1.1, not a circular reduction. The derivation chain is therefore self-contained and externally falsifiable; any concerns about the high-redshift extrapolation of Eq. (1) belong to input uncertainty, not circularity.

Assumptions & free parameters 6 free parameters · 5 assumptions · 0 invented entities

The central yield prediction relies on literature-fitted relations such as the stellar-to-halo mass relation and subhalo mass function, plus several hand-chosen thresholds for detectability and characterizability. No new physical entities are introduced. The simulation is careful and transparent, but the quoted numbers are conditional on these inputs.

free parameters (6)
  • Stellar-to-halo mass ratio coefficients = 51±36 and 0.9±1.8 in Mvir/M* = (51±36)(1+z)^(0.9±1.8)
    From Lagattuta et al. (2010), fit to 41 strong lenses with 0.4<z<0.9 and 22 SLACS lenses with 0.063<z<0.513; extrapolated to z<=3 in this paper. Sets lensing galaxy halo mass, which drives Einstein radii and subhalo populations. Section 2.1.1, Eq. (1).
  • Subhalo mass function slope and normalization = alpha = -1.9, Sigma_sub = 0.055 kpc^-2
    From pyHalo CDM preset (Gilman et al. 2020), calibrated to N-body simulations. Used to generate subhalo populations for every lensing galaxy. Section 2.1.2.
  • Subhalo mass range = 10^6 to 10^10 Msun
    Choice for the simulated dataset; affects the low-mass subhalo population and the PSF masking analysis. Section 2.1.2 and Section 4.1.
  • Detectability thresholds = SNR>20, magnification>3, image separation>=0.3 arcsec, source magnitude<25
    Adopted from typical strong lens detection criteria (Collett 2015; Holloway et al. 2023). These define the detectable yield. Section 2.1.5.
  • Substructure characterization threshold = Overall SNR>200 (median per-pixel SNR 3.7±0.5)
    Calibrated against Despali et al. (2022) to select systems amenable to substructure inference. The 500 characterizable systems count depends directly on this cutoff. Section 3.1.
  • Fixed subhalo concentration = c = 10
    Held constant for single-subhalo detectability across the focal plane to isolate PSF effects; this is acknowledged as a simplification. Section 4.2.
assumptions (5)
  • domain assumption Lambda-CDM cosmology and CDM subhalo populations are assumed
    Section 2.1.2 states 'We assume CDM given its empirical success'. The yield and substructure results are conditional on this model.
  • domain assumption Galaxy population is modeled by SkyPy and SLSim with Sersic profiles
    Section 2.1.1: galaxy luminosities, spectra, and sizes come from SkyPy defaults; morphologies are Sersic ellipses. Real high-redshift morphological diversity is not included, as acknowledged in Section 4.5.
  • domain assumption WebbPSF and GalSim Roman module reproduce the true Roman PSF and detector behavior
    Section 2.2.3: PSFs are pre-launch simulations; an in-flight PSF library is expected later. Results on PSF field-dependence depend on the accuracy of the Cycle 9 aberration data.
  • ad hoc to paper Single-pixel power spectrum is a valid proxy for milli-lensing substructure signal
    Section 4.1: 'as a simple proxy of this signal, we consider the power spectra of images at small angular scales'. The conclusion that PSF variation masks low-mass subhalos rests on this proxy.
  • ad hoc to paper The chi-squared statistic with the no-subhalo image as noise model represents an ideal detection algorithm
    Section 4.2: the algorithm 'attributes every difference between the images of the systems with and without the subhalo to the presence of that subhalo'. This gives a best-case single-subhalo sensitivity.

how reviews work

0 comments
Cite this review

Pith. "Pith review of The Roman View of Strong Gravitational Lenses." pith.science (2026). https://pith.science/paper/IQVUUUMI

@misc{pith2026250603390,
  author       = {Pith},
  title        = {Pith review of: The Roman View of Strong Gravitational Lenses},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/IQVUUUMI}},
  note         = {Machine review of arXiv:2506.03390}
}
read the original abstract

Galaxy-galaxy strong gravitational lenses can constrain dark matter models and the Lambda Cold Dark Matter cosmological paradigm at sub-galactic scales. Currently, there is a dearth of images of these rare systems with high signal-to-noise and angular resolution. The Nancy Grace Roman Space Telescope (hereafter, Roman), scheduled for launch in late 2026, will play a transformative role in strong lensing science with its planned wide-field surveys. With its remarkable 0.281 square degree field of view and diffraction-limited angular resolution of ~0.1 arcsec, Roman is uniquely suited to characterizing dark matter substructure from a robust population of strong lenses. We present a yield simulation of detectable strong lenses in Roman's planned High Latitude Wide Area Survey (HLWAS). We simulate a population of galaxy-galaxy strong lenses across cosmic time with Cold Dark Matter subhalo populations, select those detectable in the HLWAS, and generate simulated images accounting for realistic Wide Field Instrument detector effects. For a fiducial case of single 146-second exposures, we predict around 160,000 detectable strong lenses in the HLWAS, of which about 500 will have sufficient signal-to-noise to be amenable to detailed substructure characterization. We investigate the effect of the variation of the point-spread function across Roman's field of view on detecting individual subhalos and the suppression of the subhalo mass function at low masses. Our simulation products are available to support strong lens science with Roman, such as training neural networks and validating dark matter substructure analysis pipelines.

Figures

Figures reproduced from arXiv: 2506.03390 by the authors.

Figure 1
Figure 1. The galaxy-galaxy strong lensing configuration. Light from a distant source galaxy is gravitationally lensed by the foreground lensing galaxy, forming multiple distorted and magnified images of the source. The lensing potential is due to a main halo and a population of subhalos in the lens plane. Additionally, shear and external convergence are added to the mass model to simulate the effect of massive, distant line-… view at source ↗
Figure 2
Figure 2. Simulated exposure of a sample strong lens de￾tectable by Roman: F184, F129, and F106 are mapped to red, green, and blue, respectively. The source and lens po￾sitions are denoted by the orange and red dots, respectively. The caustic and critical curves are denoted by the green and blue lines, respectively [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗
Figure 3
Figure 3. A rough comparison between Roman and HST of time spent observing strong lenses. We compare the planned duration of the HLWAS (left bar) with the total amount of time on HST devoted to observing strong lenses as part of the CASTLES project (Mu˜noz et al. 1998; Falco et al. 1999), Sloan Lens ACS Survey (Bolton et al. 2008), Strong Lens￾ing in the Legacy Survey (SL2S) project (Sonnenfeld et al. 2013a), and BELLS GALLER… view at source ↗
Figures from the paper (9 more)
Figure 4
Figure 4. Figure 4: Residuals between PSFs for the F129 filter across the focal plane and PSF radial profiles are plotted in the telescope (V-frame) coordinate system. The intensity of the PSF residual is stretched using an arcsinh scale to show the spatial dependence of these noise sourc…
Figure 5
Figure 5. Figure 5: Demonstration of the field-dependence of the PSF across the WFI focal plane. The F184 image of the strong lens with log-scaled intensity is given in the bottom-right corner of SCA04, where wavefront error is minimized (see [PITH_FULL_IMAGE:figures/full_fig_p010_5.png]
Figure 6
Figure 6. Figure 6: Applying Roman WFI detector effects in order to an image of the system in [PITH_FULL_IMAGE:figures/full_fig_p011_6.png]
Figure 7
Figure 7. Figure 7: Parameters of the detectable strong lenses from the survey simulation, which is about 16% of the ∼1700 deg2 HLWAS. Top row, from left to right: velocity dispersion of lensing galaxies, redshifts of source and lensing galaxies, magnitudes of lensing and (unlensed) sourc…
Figure 8
Figure 8. Figure 8: A subset of the simulated images. The synthetic images are on the left, while the corresponding images with sky background and WFI detector effects are on the right. The cutouts are 10.01 arcsec on a side at the native pixel scale of 0.11 arcsec pixel−1 . effect of the…
Figure 9
Figure 9. Figure 9: Comparison of the radially-averaged power spectra at the single-pixel scale when varying the PSF and the lower-mass limit of the subhalo population. The radial bars and dotted circles represent the absolute value of the deviation at the single-pixel scale of the power …
Figure 10
Figure 10. Figure 10: Demonstration of the increase in detection sig￾nificance of single subhalos as their masses increase. Each line represents a random instance of a high-SNR system with a single subhalo of varying mass located at an image posi￾tion. The shaded region represents variatio…
Figure 11
Figure 11. Figure 11: Mean mass of least massive detectable subhalo in high-SNR strong lenses across the WFI focal plane at 3σ confidence by an ideal detection algorithm with deep Roman observations. Each subhalo is placed at an image position, representing the best-case scenario position …
Figure 12
Figure 12. Figure 12: Comparing the distribution of HST single subhalo detection candidates reported in Nightingale et al. (2024) from the SLACS and BELLS GALLERY samples with the sensitivity of Roman to single subhalos, including the ef￾fect of concentration scatter with mass and redshift…

Discussion (0). Sign in to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Strong-lensing Perturber Signatures in Self-interacting Dark Matter Simulations

    astro-ph.CO 2025-10 conditional novelty 6.0 of 10

    Core-collapsed self-interacting dark matter halos in the Concerto simulations reach high enough central densities to match the perturbing masses inferred in J0946, B1938, SDP.81, and SPT2147-50.

Reference graph

Works this paper leans on

126 extracted references · 21 canonical work pages · cited by 1 Pith paper

  1. [1]

    A., O’Riordan, C

    38 https://github.com/spacetelescope/romanisim 39 https://github.com/Roman-Supernova-PIT/phrosty 40 https://github.com/spacetelescope/STScI-STIPS/releases/ tag/v2.1.0 Acevedo Barroso, J. A., O’Riordan, C. M., Cl´ ement, B., et al. 2024, arXiv e-prints, arXiv:2408.06217, doi: 10.48550/arXiv.2408.06217

    doi:10.48550/arxiv.2408.06217 2024

  2. [2]

    M., Gorjian, V., & Kimball, A

    Agol, E., Gogarten, S. M., Gorjian, V., & Kimball, A. 2009, ApJ, 697, 1010, doi: 10.1088/0004-637X/697/2/1010 20

    work page doi:10.1088/0004-637x/697/2/1010 2009

  3. [3]

    V., et al

    Ahvazi, N., Benson, A., Sales, L. V., et al. 2024, MNRAS, 529, 3387, doi: 10.1093/mnras/stae761

    doi:10.1093/mnras/stae761 2024

  4. [4]

    2017, MNRAS, 470, 2617, doi: 10.1093/mnras/stx721

    Alam, S., Ata, M., Bailey, S., et al. 2017, MNRAS, 470, 2617, doi: 10.1093/mnras/stx721

    doi:10.1093/mnras/stx721 2017

  5. [5]

    2021, The Journal of Open Source Software, 6, 3056, doi: 10.21105/joss.03056

    Amara, A., de la Bella, L., Birrer, S., et al. 2021, The Journal of Open Source Software, 6, 3056, doi: 10.21105/joss.03056

    doi:10.21105/joss.03056 2021

  6. [6]

    C., Nightingale, J., He, Q., et al

    Amorisco, N. C., Nightingale, J., He, Q., et al. 2022, MNRAS, 510, 2464, doi: 10.1093/mnras/stab3527 Astropy Collaboration, Robitaille, T. P., Tollerud, E. J., et al. 2013, A&A, 558, A33, doi: 10.1051/0004-6361/201322068 Astropy Collaboration, Price-Whelan, A. M., Sip˝ ocz, B. M., et al. 2018, AJ, 156, 123, doi: 10.3847/1538-3881/aabc4f Astropy Collaborat...

    doi:10.1093/mnras/stab3527 2022

  7. [7]

    J., Enzi, W

    Ballard, D. J., Enzi, W. J. R., Collett, T. E., Turner, H. C., & Smith, R. J. 2024, MNRAS, 528, 7564, doi: 10.1093/mnras/stae514

    doi:10.1093/mnras/stae514 2024

  8. [8]

    A., Marshall, P., & Oguri, M

    Baltz, E. A., Marshall, P., & Oguri, M. 2009, JCAP, 2009, 015, doi: 10.1088/1475-7516/2009/01/015

    doi:10.1088/1475-7516/2009/01/015 2009

Show all 126 references
  1. [9]

    Benson, A. J. 2012, NewA, 17, 175, doi: 10.1016/j.newast.2011.07.004

    2012 doi

  2. [11]

    2018, Physics of the Dark Universe, 22, 189, doi: 10.1016/j.dark.2018.11.002

    Birrer, S., & Amara, A. 2018, Physics of the Dark Universe, 22, 189, doi: 10.1016/j.dark.2018.11.002

    2018 doi

  3. [12]

    2017, JCAP, 2017, 037, doi: 10.1088/1475-7516/2017/05/037

    Birrer, S., Amara, A., & Refregier, A. 2017, JCAP, 2017, 037, doi: 10.1088/1475-7516/2017/05/037

    2017 doi

  4. [13]

    2021, The Journal of Open Source Software, 6, 3283, doi: 10.21105/joss.03283

    Birrer, S., Shajib, A., Gilman, D., et al. 2021, The Journal of Open Source Software, 6, 3283, doi: 10.21105/joss.03283

    2021 doi

  5. [14]

    2024, SSRv, 220, 48, doi: 10.1007/s11214-024-01079-w

    Birrer, S., Millon, M., Sluse, D., et al. 2024, SSRv, 220, 48, doi: 10.1007/s11214-024-01079-w

    2024 doi

  6. [15]

    P., & Turok, N

    Bode, P., Ostriker, J. P., & Turok, N. 2001, ApJ, 556, 93, doi: 10.1086/321541

    2001 doi

  7. [16]

    S., Burles, S., Koopmans, L

    Bolton, A. S., Burles, S., Koopmans, L. V. E., et al. 2008, The Astrophysical Journal, 682, 964, doi: 10.1086/589327

    2008 doi

  8. [17]

    Moustakas, L. A. 2006, ApJ, 638, 703, doi: 10.1086/498884

    2006 doi

  9. [18]

    W., Price-Whelan, A

    Bonaca, A., Hogg, D. W., Price-Whelan, A. M., & Conroy, C. 2019, ApJ, 880, 38, doi: 10.3847/1538-4357/ab2873

    2019 doi

  10. [19]

    S., & Boylan-Kolchin, M

    Bullock, J. S., & Boylan-Kolchin, M. 2017, ARA&A, 55, 343, doi: 10.1146/annurev-astro-091916-055313

    2017 doi

  11. [20]

    S., Kravtsov, A

    Bullock, J. S., Kravtsov, A. V., & Weinberg, D. H. 2000, ApJ, 539, 517, doi: 10.1086/309279

    2000 doi

  12. [21]

    M., Laliotis, K., et al

    Cao, K., Hirata, C. M., Laliotis, K., et al. 2024, arXiv e-prints, arXiv:2410.05442, doi: 10.48550/arXiv.2410.05442

    2024 doi

  13. [22]

    2011, The Messenger, 146, 2

    Capaccioli, M., & Schipani, P. 2011, The Messenger, 146, 2

    2011

  14. [23]

    Choi, A., & Hirata, C. M. 2020, PASP, 132, 014502, doi: 10.1088/1538-3873/ab4504 Col ´ ın, P., Avila-Reese, V., & Valenzuela, O. 2000, ApJ, 542, 622, doi: 10.1086/317057

    2020 doi

  15. [24]

    Collett, T. E. 2015, ApJ, 811, 20, doi: 10.1088/0004-637X/811/1/20

    2015 doi

  16. [25]

    Sigurdson, K., & Gilman, D. A. 2016, PhRvD, 94, 043505, doi: 10.1103/PhysRevD.94.043505

    2016 doi

  17. [26]

    2023, arXiv e-prints, arXiv:2306.12864, doi: 10.48550/arXiv.2306.12864

    Daylan, T., & Birrer, S. 2023, arXiv e-prints, arXiv:2306.12864, doi: 10.48550/arXiv.2306.12864

    2023 doi arXiv

  18. [27]

    Daylan, T., Cyr-Racine, F.-Y., Diaz Rivero, A., Dvorkin, C., & Finkbeiner, D. P. 2018, The Astrophysical Journal, 854, 141, doi: 10.3847/1538-4357/aaaa1e

    2018 doi

  19. [28]

    M., Poci, A., et al

    Derkenne, C., McDermid, R. M., Poci, A., et al. 2021, MNRAS, 506, 3691, doi: 10.1093/mnras/stab1996

    2021 doi

  20. [29]

    M., Fassnacht, C

    Despali, G., Heinze, F. M., Fassnacht, C. D., et al. 2024, arXiv e-prints, arXiv:2407.12910, doi: 10.48550/arXiv.2407.12910

    2024 doi arXiv

  21. [30]

    Despali, G., Vegetti, S., White, S. D. M., Giocoli, C., & van den Bosch, F. C. 2018, MNRAS, 475, 5424, doi: 10.1093/mnras/sty159

    2018 doi

  22. [31]

    Despali, G., Vegetti, S., White, S. D. M., et al. 2022, MNRAS, 510, 2480, doi: 10.1093/mnras/stab3537

    2022 doi

  23. [32]

    2023, MNRAS, 518, 5843, doi: 10.1093/mnras/stac2993

    Benson, A., & Gilman, D. 2023, MNRAS, 518, 5843, doi: 10.1093/mnras/stac2993

    2023 doi

  24. [33]

    2019, ApJ, 871, 168, doi: 10.3847/1538-4357/aafad6 Dor´ e, O., Hirata, C., Wang, Y., et al

    Diemer, B., & Joyce, M. 2019, ApJ, 871, 168, doi: 10.3847/1538-4357/aafad6 Dor´ e, O., Hirata, C., Wang, Y., et al. 2018, arXiv e-prints, arXiv:1804.03628, doi: 10.48550/arXiv.1804.03628

    2019 doi arXiv

  25. [34]

    E., & Belokurov, V

    Erkal, D., Koposov, S. E., & Belokurov, V. 2017, MNRAS, 470, 60, doi: 10.1093/mnras/stx1208

    2017 doi

  26. [35]

    2024, MNRAS, 532, 2248, doi: 10.1093/mnras/stae1593

    Fagin, J., Vernardos, G., Tsagkatakis, G., et al. 2024, MNRAS, 532, 2248, doi: 10.1093/mnras/stae1593

    2024 doi

  27. [36]

    E., Kochanek, C

    Falco, E. E., Kochanek, C. S., Lehar, J., et al. 1999, arXiv e-prints, astro, doi: 10.48550/arXiv.astro-ph/9910025

    1999 doi arXiv

  28. [37]

    2008, ApJS, 176, 19, doi: 10.1086/526426

    Faure, C., Kneib, J.-P., Covone, G., et al. 2008, ApJS, 176, 19, doi: 10.1086/526426

    2008 doi

  29. [38]

    Ferrami, G., & Wyithe, J. S. B. 2024, MNRAS, 532, 1832, doi: 10.1093/mnras/stae1607

    2024 doi

  30. [39]

    T., Honscheid, K., et al

    Flaugher, B., Diehl, H. T., Honscheid, K., et al. 2015, AJ, 150, 150, doi: 10.1088/0004-6256/150/5/150

    2015 doi

  31. [40]

    P., Mather, J

    Gardner, J. P., Mather, J. C., Clampin, M., et al. 2006, SSRv, 123, 485, doi: 10.1007/s11214-006-8315-7 21

    2006 doi

  32. [41]

    O., Kruk, S., Cornen, C., et al

    Garvin, E. O., Kruk, S., Cornen, C., et al. 2022, A&A, 667, A141, doi: 10.1051/0004-6361/202243745

    2022 doi

  33. [42]

    D., et al

    Gavazzi, R., Treu, T., Rhodes, J. D., et al. 2007, ApJ, 667, 176, doi: 10.1086/519237

    2007 doi

  34. [43]

    Nierenberg, A. M. 2017, MNRAS, 467, 3970, doi: 10.1093/mnras/stx158

    2017 doi

  35. [44]

    2022, MNRAS, 512, 3163, doi: 10.1093/mnras/stac670

    Gilman, D., Benson, A., Bovy, J., et al. 2022, MNRAS, 512, 3163, doi: 10.1093/mnras/stac670

    2022 doi

  36. [45]

    2020, MNRAS, 491, 6077, doi: 10.1093/mnras/stz3480

    Gilman, D., Birrer, S., Nierenberg, A., et al. 2020, MNRAS, 491, 6077, doi: 10.1093/mnras/stz3480

    2020 doi

  37. [46]

    R., Millman, K

    Harris, C. R., Millman, K. J., van der Walt, S. J., et al. 2020, Nature, 585, 357, doi: 10.1038/s41586-020-2649-2

    2020 doi

  38. [47]

    S., et al

    He, Q., Li, R., Frenk, C. S., et al. 2022, MNRAS, 512, 5862, doi: 10.1093/mnras/stac759

    2022 doi

  39. [48]

    2023, MNRAS, 518, 220, doi: 10.1093/mnras/stac2779

    He, Q., Nightingale, J., Robertson, A., et al. 2023, MNRAS, 518, 220, doi: 10.1093/mnras/stac2779

    2023 doi

  40. [49]

    D., Dalal, N., Marrone, D

    Hezaveh, Y. D., Dalal, N., Marrone, D. P., et al. 2016, ApJ, 823, 37, doi: 10.3847/0004-637X/823/1/37

    2016 doi

  41. [50]

    2013, ApJS, 208, 19, doi: 10.1088/0067-0049/208/2/19

    Hinshaw, G., Larson, D., Komatsu, E., et al. 2013, ApJS, 208, 19, doi: 10.1088/0067-0049/208/2/19

    2013 doi

  42. [51]

    B., Fleury, P., Larena, J., & Martinelli, M

    Hogg, N. B., Fleury, P., Larena, J., & Martinelli, M. 2023, MNRAS, 520, 5982, doi: 10.1093/mnras/stad512

    2023 doi

  43. [52]

    2023, MNRAS, 525, 2341, doi: 10.1093/mnras/stad2371

    Tecza, M. 2023, MNRAS, 525, 2341, doi: 10.1093/mnras/stad2371

    2023 doi

  44. [53]

    2018, MNRAS, 475, 2438, doi: 10.1093/mnras/stx3320

    Hsueh, J.-W., Despali, G., Vegetti, S., et al. 2018, MNRAS, 475, 2438, doi: 10.1093/mnras/stx3320

    2018 doi

  45. [54]

    2020, ApJ, 894, 78, doi: 10.3847/1538-4357/ab7ffb

    Huang, X., Storfer, C., Ravi, V., et al. 2020, ApJ, 894, 78, doi: 10.3847/1538-4357/ab7ffb

    2020 doi

  46. [55]

    2021, ApJ, 909, 27, doi: 10.3847/1538-4357/abd62b

    Huang, X., Storfer, C., Gu, A., et al. 2021, ApJ, 909, 27, doi: 10.3847/1538-4357/abd62b

    2021 doi

  47. [56]

    J., Glazebrook, K., & Jacobs, C

    Hughes, T. J., Glazebrook, K., & Jacobs, C. 2024, arXiv e-prints, arXiv:2403.04349, doi: 10.48550/arXiv.2403.04349 Irˇ siˇ c, V., Viel, M., Haehnelt, M. G., et al. 2017, PhRvD, 96, 023522, doi: 10.1103/PhysRevD.96.023522 —. 2024, PhRvD, 109, 043511, doi: 10.1103/PhysRevD.109.043511

    2024 doi arXiv

  48. [57]

    2008, MNRAS, 389, 1311, doi: 10.1111/j.1365-2966.2008.13629.x

    Jackson, N. 2008, MNRAS, 389, 1311, doi: 10.1111/j.1365-2966.2008.13629.x

    2008

  49. [58]

    E., Nierenberg, A

    Keeley, R. E., Nierenberg, A. M., Gilman, D., et al. 2024, MNRAS, 535, 1652, doi: 10.1093/mnras/stae2458

    2024 doi

  50. [59]

    Y., & Peter, A

    Kim, S. Y., & Peter, A. H. G. 2021, arXiv e-prints, arXiv:2106.09050, doi: 10.48550/arXiv.2106.09050

    2021 doi arXiv

  51. [60]

    A., et al

    Kirkby, D., Robitaille, T., Weaver, B. A., et al. 2024, desihub/speclite: Several new filter sets, v0.18, Zenodo, doi: 10.5281/zenodo.10520287

    2024 doi

  52. [61]

    M., Aussel, H., Calzetti, D., et al

    Koekemoer, A. M., Aussel, H., Calzetti, D., et al. 2007, ApJS, 172, 196, doi: 10.1086/520086

    2007 doi

  53. [62]

    Moustakas, L. A. 2006, ApJ, 649, 599, doi: 10.1086/505696

    2006 doi

  54. [63]

    J., Fassnacht, C

    Lagattuta, D. J., Fassnacht, C. D., Auger, M. W., et al. 2010, ApJ, 716, 1579, doi: 10.1088/0004-637X/716/2/1579

    2010 doi

  55. [64]

    M., et al

    Laliotis, K., Macbeth, E., Hirata, C. M., et al. 2024, Publications of the Astronomical Society of the Pacific, 136, 124506, doi: 10.1088/1538-3873/ad9bec

    2024 doi

  56. [65]

    2011, arXiv e-prints, arXiv:1110.3193

    Laureijs, R., Amiaux, J., Arduini, S., et al. 2011, arXiv e-prints, arXiv:1110.3193. https://arxiv.org/abs/1110.3193

    2011 arXiv

  57. [66]

    2024, SSRv, 220, 23, doi: 10.1007/s11214-024-01042-9

    Lemon, C., Courbin, F., More, A., et al. 2024, SSRv, 220, 23, doi: 10.1007/s11214-024-01042-9

    2024 doi

  58. [67]

    J., Treu, T., Melbourne, J., et al

    Marshall, P. J., Treu, T., Melbourne, J., et al. 2007, ApJ, 671, 1196, doi: 10.1086/523091

    2007 doi

  59. [68]

    P., Koopmans, L

    McKean, J. P., Koopmans, L. V. E., Flack, C. E., et al. 2007, MNRAS, 378, 109, doi: 10.1111/j.1365-2966.2007.11744.x

    2007

  60. [69]

    2021, MNRAS, 507, 1662, doi: 10.1093/mnras/stab2247

    Minor, Q., Gad-Nasr, S., Kaplinghat, M., & Vegetti, S. 2021, MNRAS, 507, 1662, doi: 10.1093/mnras/stab2247

    2021 doi

  61. [70]

    Minor, Q. E. 2024, arXiv e-prints, arXiv:2408.11090, doi: 10.48550/arXiv.2408.11090

    2024 doi arXiv

  62. [71]

    1999, ApJL, 524, L19, doi: 10.1086/312287

    Moore, B., Ghigna, S., Governato, F., et al. 1999, ApJL, 524, L19, doi: 10.1086/312287

    1999 doi

  63. [72]

    J., Bennett, C., et al

    Mosby, G., Rauscher, B. J., Bennett, C., et al. 2020, Journal of Astronomical Telescopes, Instruments, and Systems, 6, 046001, doi: 10.1117/1.JATIS.6.4.046001 Mu˜ noz, J. A., Falco, E. E., Kochanek, C. S., et al. 1998, Ap&SS, 263, 51, doi: 10.1023/A:1002120921330

    2020 doi

  64. [73]

    O., Birrer, S., Gilman, D., et al

    Nadler, E. O., Birrer, S., Gilman, D., et al. 2021, ApJ, 917, 7, doi: 10.3847/1538-4357/abf9a3

    2021 doi

  65. [74]

    2021, JCAP, 2021, 062, doi: 10.1088/1475-7516/2021/08/062

    Newton, O., Leo, M., Cautun, M., et al. 2021, JCAP, 2021, 062, doi: 10.1088/1475-7516/2021/08/062

    2021 doi

  66. [75]

    M., Treu, T., Menci, N., Lu, Y., & Wang, W

    Nierenberg, A. M., Treu, T., Menci, N., Lu, Y., & Wang, W. 2013, ApJ, 772, 146, doi: 10.1088/0004-637X/772/2/146

    2013 doi

  67. [76]

    M., Keeley, R

    Nierenberg, A. M., Keeley, R. E., Sluse, D., et al. 2024, MNRAS, 530, 2960, doi: 10.1093/mnras/stae499

    2024 doi

  68. [77]

    W., He, Q., Cao, X., et al

    Nightingale, J. W., He, Q., Cao, X., et al. 2024, MNRAS, 527, 10480, doi: 10.1093/mnras/stad3694

    2024 doi

  69. [78]

    2025, arXiv e-prints, arXiv:2501.05632, doi: 10.48550/arXiv.2501.05632 O’Riordan, C

    Collaboration, The Roman HLIS Project Infrastructure Team, et al. 2025, arXiv e-prints, arXiv:2501.05632, doi: 10.48550/arXiv.2501.05632 O’Riordan, C. M., Despali, G., Vegetti, S., Lovell, M. R., & Molin´ e,´A. 2023, MNRAS, 521, 2342, doi: 10.1093/mnras/stad650 22

    2025 doi

  70. [79]

    2024, spacetelescope/pysiaf: Update to PRDOPSSOC-067, 0.23.3, Zenodo, doi: 10.5281/zenodo.13761170

    Osborne, S., Fix, M., Long, D., et al. 2024, spacetelescope/pysiaf: Update to PRDOPSSOC-067, 0.23.3, Zenodo, doi: 10.5281/zenodo.13761170

    2024 doi

  71. [80]

    A., Casey, T., Marx, C., et al

    Pasquale, B. A., Casey, T., Marx, C., et al. 2018, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol. 10745, Current Developments in Lens Design and Optical Engineering XIX, ed. R. B

    2018

  72. [81]

    Johnson, V. N. Mahajan, & S. Thibault, 107450K, doi: 10.1117/12.2325859

    doi

  73. [82]

    2012, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol

    Sivaramakrishnan, A. 2012, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, Vol. 8442, Space Telescopes and Instrumentation 2012:

    2012

  74. [83]

    Clampin, G. G. Fazio, H. A. MacEwen, & J. Oschmann, Jacobus M., 84423D, doi: 10.1117/12.925230

    doi

  75. [84]

    E., Tortora, C., Vernardos, G., et al

    Petrillo, C. E., Tortora, C., Vernardos, G., et al. 2019, MNRAS, 484, 3879, doi: 10.1093/mnras/stz189

    2019 doi

  76. [85]

    Pierel, J. D. R., Rodney, S., Vernardos, G., et al. 2021, ApJ, 908, 190, doi: 10.3847/1538-4357/abd8d3 Planck Collaboration, Aghanim, N., Akrami, Y., et al. 2020, A&A, 641, A6, doi: 10.1051/0004-6361/201833910

    2021 doi

  77. [86]

    2018, ApJ, 856, 68, doi: 10.3847/1538-4357/aaae6a

    Pourrahmani, M., Nayyeri, H., & Cooray, A. 2018, ApJ, 856, 68, doi: 10.3847/1538-4357/aaae6a

    2018 doi

  78. [87]

    R., Benson, A

    Pullen, A. R., Benson, A. J., & Moustakas, L. A. 2014, ApJ, 792, 24, doi: 10.1088/0004-637X/792/1/24

    2014 doi

  79. [88]

    2024, in American Astronomical Society Meeting Abstracts, Vol

    Rigby, J., Hutchison, T., Welch, B., Vieira, J., & Templates. 2024, in American Astronomical Society Meeting Abstracts, Vol. 243, American Astronomical Society Meeting Abstracts, 227.07

    2024

  80. [89]

    2019, MNRAS, 485, 2179, doi: 10.1093/mnras/stz464

    Ritondale, E., Vegetti, S., Despali, G., et al. 2019, MNRAS, 485, 2179, doi: 10.1093/mnras/stz464

    2019 doi

  81. [90]

    Rocha, M., Peter, A. H. G., Bullock, J. S., et al. 2013, MNRAS, 430, 81, doi: 10.1093/mnras/sts514

    2013 doi

  82. [91]

    M., Baltay, C., Hounsell, R., et al

    Rose, B. M., Baltay, C., Hounsell, R., et al. 2021a, arXiv e-prints, arXiv:2111.03081, doi: 10.48550/arXiv.2111.03081

    doi arXiv

  83. [92]

    M., Aldering, G., Dai, M., et al

    Rose, B. M., Aldering, G., Dai, M., et al. 2021b, arXiv e-prints, arXiv:2104.01199, doi: 10.48550/arXiv.2104.01199

    doi arXiv

  84. [93]

    Rowe, B. T. P., Jarvis, M., Mandelbaum, R., et al. 2015, Astronomy and Computing, 10, 121, doi: 10.1016/j.ascom.2015.02.002

    2015 doi

  85. [94]

    E., Macci` o, A

    Schneider, A., Smith, R. E., Macci` o, A. V., & Moore, B. 2012, MNRAS, 424, 684, doi: 10.1111/j.1365-2966.2012.21252.x

    2012

  86. [95]

    E., & Reed, D

    Schneider, A., Smith, R. E., & Reed, D. 2013, MNRAS, 433, 1573, doi: 10.1093/mnras/stt829 Seng¨ ul, A. C ¸ ., Dvorkin, C., Ostdiek, B., & Tsang, A. 2022, MNRAS, 515, 4391, doi: 10.1093/mnras/stac1967

    2013 doi

  87. [96]

    J., Treu, T., Birrer, S., & Sonnenfeld, A

    Shajib, A. J., Treu, T., Birrer, S., & Sonnenfeld, A. 2021, MNRAS, 503, 2380, doi: 10.1093/mnras/stab536

    2021 doi

  88. [97]

    J., White, S

    Shen, S., Mo, H. J., White, S. D. M., et al. 2003, MNRAS, 343, 978, doi: 10.1046/j.1365-8711.2003.06740.x

    2003

  89. [98]

    S., Kochanek, C

    Shu, Y., Bolton, A. S., Kochanek, C. S., et al. 2016a, ApJ, 824, 86, doi: 10.3847/0004-637X/824/2/86

    doi

  90. [99]

    S., Mao, S., et al

    Shu, Y., Bolton, A. S., Mao, S., et al. 2016b, ApJ, 833, 264, doi: 10.3847/1538-4357/833/2/264

    doi

  91. [100]

    J., Gavazzi, R., et al

    Shuntov, M., McCracken, H. J., Gavazzi, R., et al. 2022, A&A, 664, A61, doi: 10.1051/0004-6361/202243136 SkyPy Collaboration, Amara, A., de la Bella, L. F., et al. 2023, SkyPy, v0.5.3, Zenodo, doi: 10.5281/zenodo.10156337

    2022 doi

  92. [101]

    Somerville, R. S. 2002, ApJL, 572, L23, doi: 10.1086/341444

    2002 doi

  93. [102]

    Marshall, P. J. 2013a, ApJ, 777, 97, doi: 10.1088/0004-637X/777/2/97

    doi

  94. [103]

    2013b, ApJ, 777, 98, doi: 10.1088/0004-637X/777/2/98

    Sonnenfeld, A., Treu, T., Gavazzi, R., et al. 2013b, ApJ, 777, 98, doi: 10.1088/0004-637X/777/2/98

    doi

  95. [104]

    2015, arXiv e-prints, arXiv:1503.03757, doi: 10.48550/arXiv.1503.03757

    Spergel, D., Gehrels, N., Baltay, C., et al. 2015, arXiv e-prints, arXiv:1503.03757, doi: 10.48550/arXiv.1503.03757

    2015 doi arXiv

  96. [105]

    N., & Steinhardt, P

    Spergel, D. N., & Steinhardt, P. J. 2000, PhRvL, 84, 3760, doi: 10.1103/PhysRevLett.84.3760 STIPS Development Team, Gomez, S., Bellini, A., et al. 2024, PASP, 136, 124502, doi: 10.1088/1538-3873/ad9524

    2000 doi

  97. [106]

    2024, ApJS, 274, 16, doi: 10.3847/1538-4365/ad527e

    Storfer, C., Huang, X., Gu, A., et al. 2024, ApJS, 274, 16, doi: 10.3847/1538-4365/ad527e

    2024 doi

  98. [107]

    H., Bonvin, V., Courbin, F., et al

    Suyu, S. H., Bonvin, V., Courbin, F., et al. 2017, MNRAS, 468, 2590, doi: 10.1093/mnras/stx483

    2017 doi

  99. [108]

    Y., Shajib, A

    Tan, C. Y., Shajib, A. J., Birrer, S., et al. 2024, MNRAS, 530, 1474, doi: 10.1093/mnras/stae884

    2024 doi

  100. [109]

    Treu, T., & Shajib, A. J. 2024, in The Hubble Constant Tension, ed. E. Di Valentino & D. Brout (Singapore: Springer Nature Singapore), 251–276, doi: 10.1007/978-981-99-0177-7 14

    2024 doi

  101. [110]

    A., Long, H., Hirata, C

    Troxel, M. A., Long, H., Hirata, C. M., et al. 2021, MNRAS, 501, 2044, doi: 10.1093/mnras/staa3658

    2021 doi

  102. [111]

    2024, arXiv e-prints, arXiv:2401.16624, doi: 10.48550/arXiv.2401.16624 van Dokkum, P., Brammer, G., Wang, B., Leja, J., &

    Tsang, A., C ¸ a˘ gan S ¸eng¨ ul, A., & Dvorkin, C. 2024, arXiv e-prints, arXiv:2401.16624, doi: 10.48550/arXiv.2401.16624 van Dokkum, P., Brammer, G., Wang, B., Leja, J., &

    2024 doi arXiv

  103. [112]

    2024, Nature Astronomy, 8, 119, doi: 10.1038/s41550-023-02103-9

    Conroy, C. 2024, Nature Astronomy, 8, 119, doi: 10.1038/s41550-023-02103-9

    2024 doi

  104. [113]

    R., & Enzi, W

    Vegetti, S., Despali, G., Lovell, M. R., & Enzi, W. 2018, MNRAS, 481, 3661, doi: 10.1093/mnras/sty2393

    2018 doi

  105. [114]

    Bolton, A. S. 2014, MNRAS, 442, 2017, doi: 10.1093/mnras/stu943 23

    2014 doi

  106. [115]

    2010, MNRAS, 408, 1969, doi: 10.1111/j.1365-2966.2010.16865.x

    Gavazzi, R. 2010, MNRAS, 408, 1969, doi: 10.1111/j.1365-2966.2010.16865.x

    2010

  107. [116]

    J., McKean, J

    Vegetti, S., Lagattuta, D. J., McKean, J. P., et al. 2012, Nature, 481, 341, doi: 10.1038/nature10669

    2012 doi

  108. [117]

    2024, Space Science Reviews, 220, 58, doi: 10.1007/s11214-024-01087-w

    Vegetti, S., Birrer, S., Despali, G., et al. 2024, Space Science Reviews, 220, 58, doi: 10.1007/s11214-024-01087-w

    2024 doi

  109. [118]

    2023, PhRvD, 108, 023502, doi: 10.1103/PhysRevD.108.023502

    Villasenor, B., Robertson, B., Madau, P., & Schneider, E. 2023, PhRvD, 108, 023502, doi: 10.1103/PhysRevD.108.023502

    2023 doi

  110. [119]

    E., et al

    Virtanen, P., Gommers, R., Oliphant, T. E., et al. 2020, Nature Methods, 17, 261, doi: 10.1038/s41592-019-0686-2

    2020 doi

  111. [120]

    2023, The Astrophysical Journal, 942, 75, doi: 10.3847/1538-4357/aca525

    Wagner-Carena, S., Aalbers, J., Birrer, S., et al. 2023, The Astrophysical Journal, 942, 75, doi: 10.3847/1538-4357/aca525

    2023 doi

  112. [121]

    2024, ApJ, 975, 297, doi: 10.3847/1538-4357/ad6e70

    Wagner-Carena, S., Lee, J., Pennington, J., et al. 2024, ApJ, 975, 297, doi: 10.3847/1538-4357/ad6e70

    2024 doi

  113. [122]

    H., & Tinker, J

    Wechsler, R. H., & Tinker, J. L. 2018, ARA&A, 56, 435, doi: 10.1146/annurev-astro-081817-051756

    2018 doi

  114. [123]

    2024, Data for The Roman View of Strong Gravitational Lenses, Version 1.0.0, Zenodo, doi: 10.5281/ZENODO.14216840 —

    Wedig, B., & Daylan, T. 2024, Data for The Roman View of Strong Gravitational Lenses, Version 1.0.0, Zenodo, doi: 10.5281/ZENODO.14216840 —. 2025, mejiro, Version 1.0.0, Zenodo, doi: 10.5281/ZENODO.15377388

    2024 doi

  115. [124]

    2020, Research Notes of the AAS, 4, 190, doi: 10.3847/2515-5172/abc4ea

    Weiner, C., Serjeant, S., & Sedgwick, C. 2020, Research Notes of the AAS, 4, 190, doi: 10.3847/2515-5172/abc4ea

    2020 doi

  116. [125]

    2024, MNRAS, 528, 6680, doi: 10.1093/mnras/stae177

    Yamamoto, M., Laliotis, K., Macbeth, E., et al. 2024, MNRAS, 528, 6680, doi: 10.1093/mnras/stae177

    2024 doi

  117. [126]

    A., Marchesini, D., et al

    Zaidi, K., Wake, D. A., Marchesini, D., et al. 2024, arXiv e-prints, arXiv:2411.04256, doi: 10.48550/arXiv.2411.04256

    2024 doi arXiv

  118. [127]

    Zavala, J., & Frenk, C. S. 2019, Galaxies, 7, 81, doi: 10.3390/galaxies7040081 ˇZeljko Ivezi´ c, Kahn, S. M., Tyson, J. A., et al. 2019, The Astrophysical Journal, 873, 111, doi: 10.3847/1538-4357/ab042c

    2019 doi

Pith tools

Reviewed August 7, 2026 · model on record in the stance chip above.