Pith. sign in

REVIEW 3 major objections 4 minor 3 cited by

Double dark matter vision: twice the number of compact-source lenses with narrow-line lensing and the WFC3 grism

T0 review · 3 major / 4 minor · reviewed 2026-08-14 · deepseek-v4-flash

Pith's one-line read Narrow emission lines from lensed quasar nuclei provide a microlensing-free way to measure image fluxes, doubling the compact-source lens sample and showing that smooth lens models fail to explain the observed flux ratios, pointing to…

desk verdict Solid new flux-ratio measurements double the compact-source lens sample, but the abstract's smooth-model rejection claim is not supported by the paper's own simplified p-value. read the letter →

arxiv 1908.06344 v2 pith:DXVONC3F submitted 2019-08-17 astro-ph.GA astro-ph.CO

classification astro-ph.GAastro-ph.CO
keywords gravitationallensingdarkmattersubstructurequasarnarrow-lineregionfluxratiosWFC3grismspectroscopymicrolensingquadruplyimagedquasarshalomassfunction
topics Dark Matter
open problems Dark Matter
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

The paper argues that the narrow forbidden-line emission of quasars can replace radio jets as a microlensing-free probe of dark matter substructure in strong gravitational lenses, and it delivers the measurements that make the case. For eight quadruply imaged quasars, narrow-line fluxes are measured with 2–10% uncertainties, doubling the number of compact-source lenses suitable for this analysis. Fitting the image positions with smooth mass models and comparing the predicted flux ratios to the measured ones rejects the smooth models at $p<0.005$, with deviations larger than macromodel uncertainties. The paper reads this as evidence for perturbations by low-mass dark matter halos along the line of sight, with the quantitative dark matter interpretation left to a companion paper.

What carries the argument

The central object is the quasar narrow-line region, traced by forbidden lines such as [OIII] and [NeIII]: at milliarcsecond scales it is too large to be significantly microlensed by stars, which act on microarcsecond scales, yet compact enough to be treated as a point source at grism resolution and centred on the continuum image position used for lens modelling. The measurement machinery is a forward-modelling spectral extraction that builds a full model of the two-dimensional grism image, including quasar point sources, the lens galaxy, the lensed quasar host, continuum, broad FeII and Balmer emission, and the narrow lines, and fits it in the native detector frames. The statistical machinery is a flux-ratio posterior: image positions are drawn from their measured uncertainties, a smooth power-law ellipsoid plus external shear macromodel is solved for each draw, and the predicted flux ratios are compared with the measured narrow-line flux ratios through a chi-square test.

What would settle it

A decisive check would be diffraction-limited integral-field spectroscopy of one of the six comparison lenses, resolving the narrow-line region and measuring its centroid relative to the quasar continuum. If the region is found to be larger than roughly 100 pc, or offset from the continuum by more than about 10 pc, the measured flux ratios could be diluted or mis-centred, and the $p<0.005$ smooth-model discrepancy would no longer uniquely indicate dark matter.

Watch

Extended reading notes

Core claim

The central claim is that smooth lens models fail to describe the narrow-line flux ratios of a sample of quadruply imaged quasars, and that this failure is a signal of small-scale dark matter structure. After measuring [OIII] 4959/5007 Å and [NeIII] 3869/3969 Å narrow-line fluxes in eight systems with WFC3/IR grism spectroscopy, the authors fit the quasar image positions with flexible power-law ellipsoid mass models plus external shear and found that the flux ratios predicted by those models disagree with the measured ratios. The statistical comparison, made on six lenses (excluding HS 0810, whose fold images blend at magnifications near 120, and SDSS J1330, whose disk requires extra macromodel complexity), rejects the smooth-model flux ratio distribution at $p<0.005$, with typical deviations larger than expected from macromodel uncertainties. The authors interpret this as evidence for perturbations from low-mass dark matter halos along the entire line of sight.

Load-bearing premise

The entire signal rests on the assumption that each quasar's narrow-line region is compact enough (milliarcsecond scale) to be treated as point-like at grism resolution, extended enough to be free of stellar microlensing, and centred on the quasar continuum position used in the lens models.

Editorial extensions

If this is right

  • The usable sample of compact-source lenses for dark matter studies roughly doubles, from about seven radio-loud systems to around fifteen including the eight narrow-line lenses presented here.
  • Because narrow-line ratios are insensitive to stellar microlensing, the discrepancy with smooth models is attributed to low-mass halos rather than to stars in the lens galaxy.
  • Five of the eight lenses show large differential magnification between broad and narrow emission, directly confirming that the narrow lines are the microlensing-free component and that the continuum/broad lines are microlensed.
  • With 2–10% flux measurement precision, the sample approaches the ~4% precision level at which simulations indicate that roughly 10–40 lenses can rule out a 3.3 keV warm dark matter particle, materially strengthening the statistical reach.
  • The same pipeline can be applied to the growing number of quasar lenses from wide-field surveys, which are forecast to contain thousands of such systems in the coming decade.

Reading between the lines

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

  • Beyond the paper, comparing these narrow-line flux ratios with mid-infrared or radio continuum flux ratios for the same lenses would isolate any residual source-size or dust-extinction effects and independently test the dark matter interpretation.
  • A testable extension: re-observing a few lenses at a later epoch should leave narrow-line flux ratios unchanged even while continuum ratios vary; any epoch-dependent narrow-line variation would point to contamination rather than dark matter.
  • The resolved-source analysis of HS 0810 hints that high-magnification fold pairs can serve as physical-size measurements of high-redshift narrow-line regions, turning a discarded system into a useful probe.
  • Coupling the narrow-line flux ratios with lensed host-galaxy arc constraints in the macromodel fit would tighten the predicted flux-ratio posterior and, in principle, lower the halo mass scale the method can detect.
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

3 major / 4 minor

Summary. The paper presents WFC3/IR grism observations of eight quadruply imaged quasar lenses and uses a forward-modeling spectral extraction pipeline to measure narrow-line ([OIII] or [NeIII]) flux ratios with reported uncertainties of 2–10%. The narrow-line fluxes are intended to provide compact-source flux ratios that are free of stellar microlensing. The paper then fits smooth power-law ellipsoid plus external shear lens models to the image positions only, derives model-predicted flux-ratio distributions, and compares them with the measured narrow-line flux ratios. The authors report that the smooth models fail to reproduce the observed flux ratios with p<0.005 and interpret this as evidence for small-scale dark matter structure, with the detailed dark-matter interpretation deferred to a companion paper. HS 0810 and SDSS J1330 are excluded from the comparison, so Figure 4 is based on six lenses. Section 7 contains a resolved-source analysis for HS 0810 indicating a narrow-line source size of order tens of parsecs.

Significance. If the statistical claim is supported, the paper would meaningfully expand the compact-source lens sample for dark-matter substructure studies, doubling the number of systems relative to the radio-loud sample and demonstrating a viable optical path for the technique. The spectral extraction is a genuine advance: it forward-models the 2D grism frame in the native FLT frame, accounts for blending, tests multiple FeII and H-beta templates, and validates the pipeline against alternative models. The use of public tools (grizli, lenstronomy) and the presentation of per-lens model and data flux-ratio contours are strengths. The central interpretive claim, however, rests on a simplified chi-square comparison that is not a calibrated posterior predictive test, and the paper's own caveats are in tension with the abstract's wording. The measurement campaign and the individual flux-ratio measurements are valuable regardless, but the paper in its current form overstates what the statistical test demonstrates.

major comments (3)
  1. [§6.2] The p<0.005 result is not a calibrated posterior predictive test. For each of the 1.4×10^4 position draws the procedure uses only the best-fit macromodel parameters, so the model-predicted flux-ratio distribution does not marginalize over the macromodel posterior and is likely too narrow; several flux ratios within one lens are correlated through the shared macromodel and source parameters, yet they are combined as independent one-degree-of-freedom chi-square variates, inflating the effective sample size; and the appendix contours show strongly asymmetric, non-Gaussian model marginals for which the 1-dof chi-square reference is not appropriate. The text acknowledges that covariance and non-Gaussianity are ignored and asserts that this 'under-represents' the discrepancy, but that directional claim is not demonstrated and the opposite can hold. The abstract's wording that smooth models 'fail' and that the discrepancy 'indicates' dark matter substructure therefore goes beyond what the statistic supports. A joint posterior predictive test, or a substantially weakened claim, is needed before publication.
  2. [Abstract] The abstract states that the smooth models fail to produce the observed flux distribution 'over the entire sample of lenses,' but the comparison in Figure 4 and §6.2 excludes HS 0810 and SDSS J1330 and therefore uses six of the eight lenses. The individual exclusions are motivated (blended high-magnification fold images for HS 0810; a disk galaxy for SDSS J1330), but the sample-wide wording is inaccurate. The abstract and summary should state explicitly that the test is based on six systems, and the reported p-value should be presented together with that sample definition.
  3. [§4] The dark-matter interpretation assumes that the narrow-line emission is unresolved at grism resolution, free of microlensing, and centered on the quasar continuum position used in the lens model. The paper itself notes that the Müller-Sánchez et al. (2011) sample is small and that high-redshift, luminous quasars may have different narrow-line region sizes or centroid offsets. The resolved-source test in §7 is performed only for HS 0810, which is excluded from the main comparison, and no analogous test is presented for the six lenses that drive the p-value. A population of somewhat extended or offset narrow-line regions could produce flux-ratio anomalies that mimic the dark-matter signal, so this systematic should be quantified, or at minimum explicitly budgeted, before the abstract's 'indicates' claim is made.
minor comments (4)
  1. [§3] The G141 grism wavelength range is given as 0.8–1.15 µm, which is the same range listed for G102; WFC3 G141 covers approximately 1.1–1.7 µm. Please correct this typo.
  2. [§6.2] The sentence 'the chi2 values should be Gaussian with one degree of freedom' should read that the chi-square values should follow a chi-square distribution with one degree of freedom.
  3. [Figure 4] The statistic used to obtain p<0.005 is not identified; if it is a Kolmogorov-Smirnov or Anderson-Darling test, name it and report the test statistic so the reader can assess the comparison.
  4. [§7] The resolved-source comparison for HS 0810 reports log-likelihood improvements without a formal correction for the three fewer degrees of freedom or for the noise introduced by the drizzling/blotting procedure; the text discusses the latter qualitatively, but an information-criterion-style comparison would make the source-size constraint easier to evaluate.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the smooth-lens flux-ratio predictions are fitted to image positions only, not to the measured narrow-line flux ratios.

full rationale

The central comparison is self-contained and not circular. The measured narrow-line flux ratios (Table 2, Section 5) are obtained by forward-modelling WFC3 grism spectra (Section 4.2) with free spectral parameters; the smooth lens model plays no role in that extraction. The predicted flux ratios (last column of Table 2, Section 6.2) are generated by drawing 1.4e4 realizations of the measured image positions, solving for the best-fit power-law ellipsoid plus shear macromodel for each realization, and reading off the implied magnifications; no flux-ratio measurement enters the lens-model objective. Thus the predicted flux-ratio distribution is not equivalent to the measured one by construction. The same-author items in the chain are computational tools (grizli, lenstronomy), a previously measured size constraint on a different lens (N17 on HE 0435, used only to motivate the unresolved-source assumption and explicitly re-tested for HS 0810), or simulation-calibration work (Gilman et al. 2017/2018/2019b) that is not the claim being derived. These are not circular loads. The uncalibrated p-value in Figure 4 (best-fit-only draws, ignored covariances, non-Gaussian marginals) and the unresolved/centroid narrow-line assumptions are real validation concerns, but they are correctness risks, not circularity. No step reduces a prediction to its own input, so the appropriate score is 0.

Assumptions & free parameters 7 free parameters · 5 assumptions · 0 invented entities

The central comparison rests on treating narrow-line flux ratios as clean tracers of dark matter substructure while the lens macromodel is smooth. Key domain assumptions are the NLR size and centroid, the adequacy of power-law ellipsoid models, and the accuracy of the grism and PSF calibrations. The spectral fits introduce many nuisance parameters (continuum, FeII, H-beta components, line widths), and the lens model introduces macromodel and source-size priors; none of these are invented entities, but several are fitted to the same data or set by hand.

free parameters (7)
  • Continuum slope and normalization per quasar image = Free per image
    Allowed to vary independently between images in the [OIII] and [NeIII] spectral fits (Sections 4.2.1 and 4.2.2) to absorb microlensing of the continuum; affects inferred narrow-line flux ratios.
  • FeII template amplitudes and velocity broadening = Free per image
    Kovacevic et al. model with independent iron group amplitudes, fitted per lensed image (Section 4.2.1).
  • H-beta emission model parameters = Free per image and per model choice
    Single Gaussian, Gauss-Hermite, or two-Gaussian models for H-beta; amplitudes and widths vary per image (Section 4.2.1).
  • [OIII] Gaussian width and redshift offset = Fitted, width and offset tied across images
    Narrow forbidden lines fitted with a single Gaussian, with widths and offsets fixed between images (Section 4.2.1).
  • Lens macromodel parameters (power-law slope, ellipticity, orientation, Einstein radius, external shear, centroids) = Posterior ranges in Table 3
    Fitted to image positions via lenstronomy; these determine model-predicted flux ratios used in the comparison.
  • G2 perturber SIS Einstein radii and offsets for RX J0911, PS J1606, WFI 2033 = Uniform priors with ranges given in Section 6.1
    Included as smooth subhalo or nearby galaxy components; affect predicted flux ratios.
  • Narrow-line source size FWHM = 20-50 pc prior
    Drawn independently in flux ratio prediction; finite source size damps small-scale perturbations.
assumptions (5)
  • domain assumption Narrow-line emission region is about milliarcsecond in extent and therefore not microlensed by stars in the lens galaxy.
    Used in Section 4 and throughout to justify narrow-line flux ratios as a dark matter probe; size scaling at high redshift and luminosity is uncertain (Section 7).
  • domain assumption The narrow-line region is centered on the quasar continuum position to within about 0.005 arcsec.
    Section 6.2 uses PSF positions from continuum imaging for lens model fitting and assumes the same centroid for narrow-line flux; local Seyfert offsets are smaller, but high-redshift luminous quasars may differ.
  • domain assumption A single power-law ellipsoid plus external shear, with optional SIS for close galaxies, is an adequate smooth mass model, and baryonic complexities beyond obvious disks produce deviations no larger than about 10%.
    Section 6.1 adopts this model; Section 6.3 argues that disks and dust cannot explain the deviations, citing Gilman et al. 2017 and Hsueh et al. 2017.
  • domain assumption The grizli grism wavelength solutions and Anderson (2016) empirical PSFs accurately model the WFC3/IR data.
    Section 3 describes using these tools; if PSF or wavelength models are biased, measured narrow-line flux ratios could be systematically wrong.
  • domain assumption A flat LCDM cosmology with h=0.7 and Omega_m=0.3 is used for physical size conversions.
    Section 1 states the assumed cosmology; used to convert angular scales to physical sizes for source size priors and physical interpretation.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Double dark matter vision: twice the number of compact-source lenses with narrow-line lensing and the WFC3 grism." pith.science (2026). https://pith.science/paper/DXVONC3F

@misc{pith2026190806344,
  author       = {Pith},
  title        = {Pith review of: Double dark matter vision: twice the number of compact-source lenses with narrow-line lensing and the WFC3 grism},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/DXVONC3F}},
  note         = {Machine review of arXiv:1908.06344}
}
read the original abstract

The magnifications of compact-source lenses are extremely sensitive to the presence of low mass dark matter halos along the entire sight line from the source to the observer. Traditionally, the study of dark matter structure in compact-source strong gravitational lenses has been limited to radio-loud systems, as the radio emission is extended and thus unaffected by microlensing which can mimic the signal of dark matter structure. An alternate approach is to measure quasar nuclear-narrow line emission, which is free from microlensing and present in virtually all quasar lenses. In this paper, we double the number of systems which can be used for gravitational lensing analyses by presenting measurements of narrow-line emission from a sample of 8 quadruply imaged quasar lens systems, WGD J0405-3308, HS 0810+2554, RX J0911+0551, SDSS J1330+1810, PS J1606-2333, WFI 2026-4536, WFI 2033-4723 and WGD J2038-4008. We describe our updated grism spectral modelling pipeline, which we use to measure narrow-line fluxes with uncertainties of 2-10\%, presented here. We fit the lensed image positions with smooth mass models and demonstrate that these models fail to produce the observed distribution of image fluxes over the entire sample of lenses. Furthermore, typical deviations are larger than those expected from macromodel uncertainties. This discrepancy indicates the presence of perturbations caused by small-scale dark matter structure. The interpretation of this result in terms of dark matter models is presented in a companion paper.

Figures

Figures reproduced from arXiv: 1908.06344 by the authors.

Figure 1
Figure 1. Drizzled direct F140W (F105W for SDSS J1330 and WGD J2038) images of the lenses, along with quasar image subtracted residuals. Quasar images are modelled as point sources using the Effective Point Spread Function (Anderson 2016) in the native FLT frame (see Section 4.1). All images are rotated relative to the observing frame such that North is up and East left. Bars indicate one arcsecond. With the exception of SDSS… view at source ↗
Figure 2
Figure 2. Model fit to the broad and narrow emission lines normalized to the peak of the [OIII] flux to highlight differential magnification between the broad Hβ emission and [OIII]. Line widths represent one sigma posterior confidence intervals. Hβ is emitted from a region of ∼ µas in extent making it subject to magnification by stars in the plane of the lens galaxy. WGD J0405, HS 0810 and PS J1606 in particular show signifi… view at source ↗
Figure 3
Figure 3. Model fit to the broad and narrow emission lines normalized to the peak of the [NeIII] flux to highlight differential magnification between the broad and forbidden components. Line widths represent one sigma posterior confidence intervals. Broad Hγ, Hδ and H are all emitted from a regions of ∼ µas in extent making them subject to magnification by stars in the plane of the lens galaxy. Both lenses show significant d… view at source ↗
Figures from the paper (1 more)
Figure 4
Figure 4. Figure 4: Cumulative distribution of χ 2 values between the mea￾sured flux ratios, and the 1D marginalized posterior distributions of smooth gravitational lens model predicted flux ratios for the lenses in this paper excluding SDSS J1330 and HS 0810 (see Sec￾tions 6.2 and 7). Sm…

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 3 Pith papers

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

  1. Warm dark matter chills out: constraints on the halo mass function and the free-streaming length of dark matter with 8 quadruple-image strong gravitational lenses

    astro-ph.CO 2019-08 conditional novelty 7.0 of 10

    Eight quadruple-image lenses bound the dark matter half-mode mass to below 10^7.8 solar masses, corresponding to a thermal relic mass above 5.2 keV, with substructure abundance consistent with cold dark matter.

  2. JWST lensed quasar dark matter survey IV: Stringent warm dark matter constraints from the joint reconstruction of extended lensed arcs and quasar flux ratios

    astro-ph.CO 2025-11 conditional novelty 6.0 of 10

    Adding extended lensed arcs to quasar flux ratios in 28 JWST lenses tightens the dark-matter free-streaming limit to m_hm < 10^7.4 M⊙ (galacticus prior) and gives the most precise lensing measurement of subhalo abundance.

  3. Constraints on the mass-concentration relation of cold dark matter halos with 11 strong gravitational lenses

    astro-ph.CO 2019-09 conditional novelty 6.0 of 10

    First measurement of the dark matter halo mass-concentration relation below 10^9 solar masses, using flux ratios of 11 strongly lensed quasars.

Reference graph

Works this paper leans on

121 extracted references · 60 canonical work pages · cited by 3 Pith papers

  1. [1]

    A., Popović, L

    Abajas, C., Mediavilla, E., Muñoz, J. A., Popović, L. Č., & Oscoz, A. 2002, ApJ, 576, 640

  2. [2]

    2006, Phys

    Abazajian, K. 2006, Phys. Rev. D., 73, 063506

  3. [3]

    R., Dodelson, S., Heitmann, K., & Habib, S

    Abazajian, K., Switzer, E. R., Dodelson, S., Heitmann, K., & Habib, S. 2005, Phys. Rev. D., 71, 043507

  4. [4]

    L., Morgan, N

    Agnello, A., Schechter, P. L., Morgan, N. D., et al. 2018c, MNRAS, 475, 2086

  5. [5]

    2016, Empirical Models for the WFC3/IR

    Anderson, J. 2016, Empirical Models for the WFC3/IR

  6. [6]

    2008, A&A, 481, 615

    Anguita, T., Faure, C., Yonehara, A., et al. 2008, A&A, 481, 615

  7. [7]

    L., Kuropatkin, N., et al

    Anguita, T., Schechter, P. L., Kuropatkin, N., et al. 2018, MNRAS, 480, 5017

  8. [8]

    W., Treu, T., Bolton, A

    Auger, M. W., Treu, T., Bolton, A. S., et al. 2010, ApJ, 724, 511

Show all 121 references
  1. [9]

    1997, A&A, 317, L13

    Bade, N., Siebert, J., Lopez, S., Voges, W., & Reimers, D. 1997, A&A, 317, L13

  2. [10]

    1999, ApJ, 523, 54

    Barkana, R., & Loeb, A. 1999, ApJ, 523, 54

  3. [11]

    F., Vernardos, G., O’Dowd, M

    Bate, N. F., Vernardos, G., O’Dowd, M. J., et al. 2018, MNRAS, 479, 4796

  4. [12]

    2016, JCAP, 8, 012

    Baur, J., Palanque-Delabrouille, N., Yèche, C., Magneville, C., & Viel, M. 2016, JCAP, 8, 012

  5. [13]

    Bayer, D., Chatterjee, S., Koopmans, L. V. E., et al. 2018, arXiv e-prints, arXiv:1803.05952

  6. [14]

    S., Conroy, C., & Wechsler, R

    Behroozi, P. S., Conroy, C., & Wechsler, R. H. 2010, ApJ, 717, 379 Benítez-Llambay, A., Navarro, J. F., Abadi, M. G., et al. 2015, MNRAS, 450, 4207

  7. [15]

    Benson, A. J. 2010, Physics Reports, 495, 33

  8. [16]

    2002, MNRAS, 333, 177

    Cole, S. 2002, MNRAS, 333, 177

  9. [17]

    2018, Physics of the Dark Universe, 22, 189

    Birrer, S., & Amara, A. 2018, Physics of the Dark Universe, 22, 189

  10. [18]

    2015, ApJ, 813, 102 —

    Birrer, S., Amara, A., & Refregier, A. 2015, ApJ, 813, 102 —. 2017, JCAP, 2017, 037

  11. [19]

    2019, arXiv e-prints, arXiv:1904.10965

    Birrer, S., & Treu, T. 2019, arXiv e-prints, arXiv:1904.10965

  12. [20]

    A., Kochanek, C

    Blackburne, J. A., Kochanek, C. S., Chen, B., Dai, X., & c© 2016 RAS, MNRAS000, 1–16 14 Nierenberg et al

  13. [21]

    2014, ApJ, 789, 125

    Chartas, G. 2014, ApJ, 789, 125

  14. [22]

    A., Pooley, D., Rappaport, S., & Schechter, P

    Blackburne, J. A., Pooley, D., Rappaport, S., & Schechter, P. L. 2011, ApJ, 729, 34

  15. [23]

    P., & Turok, N

    Bode, P., Ostriker, J. P., & Turok, N. 2001, ApJ, 556, 93

  16. [24]

    A., & Green, R

    Boroson, T. A., & Green, R. F. 1992, ApJS, 80, 109

  17. [25]

    J., & Frenk, C

    Bose, S., Deason, A. J., & Frenk, C. S. 2018, ApJ, 863, 123

  18. [26]

    2019, MNRAS, 487, 522

    Bose, S., Vogelsberger, M., Zavala, J., et al. 2019, MNRAS, 487, 522

  19. [27]

    B., van Dokkum, P

    Brammer, G. B., van Dokkum, P. G., Franx, M., et al. 2012, ApJS, 200, 13

  20. [28]

    R., & Peter, A

    Buckley, M. R., & Peter, A. H. G. 2018, Physics Reports, 761, 1

  21. [29]

    S., Kravtsov, A

    Bullock, J. S., Kravtsov, A. V., & Weinberg, D. H. 2000, ApJ, 539, 517

  22. [30]

    2019, arXiv e-prints, arXiv:1903.12580

    Castellano, M., Menci, N., Grazian, A., et al. 2019, arXiv e-prints, arXiv:1903.12580

  23. [31]

    Colombi, S., Dodelson, S., & Widrow, L. M. 1996, ApJ, 458, 1

  24. [32]

    V., & Wise, J

    Corlies, L., Johnston, K. V., & Wise, J. H. 2018, MNRAS, 475, 4868

  25. [33]

    2014, Phys

    Cyr-Racine, F.-Y., de Putter, R., Raccanelli, A., & Sigurd- son, K. 2014, Phys. Rev. D., 89, 063517

  26. [34]

    R., & , L

    Cyr-Racine, F.-Y., Keeton, C. R., & , L. A. 2018, arXiv e-prints, arXiv:1806.07897

  27. [35]

    2016, Phys

    Cyr-Racine, F.-Y., Sigurdson, K., Zavala, J., et al. 2016, Phys. Rev. D., 93, 123527

  28. [36]

    Dalal, N., & Kochanek, C. S. 2002, ApJ, 572, 25

  29. [37]

    L., Kewley, L

    Davies, R. L., Kewley, L. J., Ho, I. T., & Dopita, M. A. 2014, MNRAS, 444, 3961

  30. [38]

    R., Ocvirk, P., et al

    Dawoodbhoy, T., Shapiro, P. R., Ocvirk, P., et al. 2018, MNRAS, 480, 1740

  31. [39]

    Despali, G., Vegetti, S., White, S. D. M., Giocoli, C., & van den Bosch, F. C. 2018, MNRAS, 475, 5424

  32. [40]

    2008, Nature, 454, 735

    Diemand, J., Kuhlen, M., Madau, P., et al. 2008, Nature, 454, 735

  33. [41]

    2005, Nature, 433, 389

    Diemand, J., Moore, B., & Stadel, J. 2005, Nature, 433, 389

  34. [42]

    2019, arXiv e-prints, arXiv:1902.01055

    Drlica-Wagner, A., Mao, Y.-Y., Adhikari, S., et al. 2019, arXiv e-prints, arXiv:1902.01055

  35. [43]

    2006, A&A, 451, 759

    Magain, P. 2006, A&A, 451, 759

  36. [44]

    E., Impey, C

    Falco, E. E., Impey, C. D., Kochanek, C. S., et al. 1999, ApJ, 523, 617

  37. [45]

    G., Macchetto, F., & Caon, N

    Ferrari, F., Pastoriza, M. G., Macchetto, F., & Caon, N. 1999, A&AS, 136, 269

  38. [46]

    2018, ApJ, 859, 50

    Fian, C., Guerras, E., Mediavilla, E., et al. 2018, ApJ, 859, 50

  39. [47]

    P., Cooper, M

    Fillingham, S. P., Cooper, M. C., Pace, A. B., et al. 2016, MNRAS, 463, 1916

  40. [48]

    F., et al

    Garrison-Kimmel, S., Wetzel, A., Hopkins, P. F., et al. 2019, arXiv e-prints, arXiv:1903.10515

  41. [49]

    D., et al

    Gavazzi, R., Treu, T., Rhodes, J. D., et al. 2007, ApJ, 667, 176

  42. [50]

    R., & Nieren- berg, A

    Gilman, D., Agnello, A., Treu, T., Keeton, C. R., & Nieren- berg, A. M. 2017, MNRAS, 467, 3970

  43. [51]

    2019a, arXiv e-prints, arXiv:1908.06983

    Gilman, D., Birrer, S., Nierenberg, A., et al. 2019a, arXiv e-prints, arXiv:1908.06983

  44. [52]

    R., & Nieren- berg, A

    Gilman, D., Birrer, S., Treu, T., Keeton, C. R., & Nieren- berg, A. 2018, MNRAS, 481, 819

  45. [53]

    Gnedin, N. Y. 2000, ApJL, 535, L75

  46. [54]

    2019, MNRAS, 485, 3009

    Arias, H. 2019, MNRAS, 485, 3009

  47. [55]

    2014, ArXiv e- prints

    Hezaveh, Y., Dalal, N., Holder, G., et al. 2014, ArXiv e- prints

  48. [56]

    F., Kereš, D., Oñorbe, J., et al

    Hopkins, P. F., Kereš, D., Oñorbe, J., et al. 2014, MNRAS, 445, 581

  49. [57]

    2018, MNRAS, 475, 2438

    Hsueh, J.-W., Despali, G., Vegetti, S., et al. 2018, MNRAS, 475, 2438

  50. [58]

    2019, arXiv e- prints, arXiv:1905.04182

    Hsueh, J.-W., Enzi, W., Vegetti, S., et al. 2019, arXiv e- prints, arXiv:1905.04182

  51. [59]

    D., Vegetti, S., et al

    Hsueh, J.-W., Fassnacht, C. D., Vegetti, S., et al. 2016, MNRAS, 463, L51

  52. [60]

    2017, MN- RAS, 469, 3713

    Hsueh, J.-W., Oldham, L., Spingola, C., et al. 2017, MN- RAS, 469, 3713

  53. [61]

    2000, Phys

    Hu, W., Barkana, R., & Gruzinov, A. 2000, Phys. Rev. Lett., 85, 1158

  54. [62]

    P., Tremaine, S., & Witten, E

    Hui, L., Ostriker, J. P., Tremaine, S., & Witten, E. 2017, Phys. Rev. D., 95, 043541 Iršič, V., Viel, M., Haehnelt, M. G., et al. 2017, Phys. Rev. D., 96, 023522

  55. [63]

    S., Roberts, C., et al

    Jackson, N., Tagore, A. S., Roberts, C., et al. 2015, MN- RAS, 454, 287

  56. [64]

    2018, MNRAS, 473, 2060 Jiménez-Vicente, J., Mediavilla, E., Kochanek, C

    Jethwa, P., Erkal, D., & Belokurov, V. 2018, MNRAS, 473, 2060 Jiménez-Vicente, J., Mediavilla, E., Kochanek, C. S., et al. 2014, ApJ, 783, 47

  57. [65]

    R., Burles, S., Schechter, P

    Keeton, C. R., Burles, S., Schechter, P. L., & Wambsganss, J. 2006, ApJ, 639, 1

  58. [66]

    2014, MN- RAS, 442, 2487

    Kennedy, R., Frenk, C., Cole, S., & Benson, A. 2014, MN- RAS, 442, 2487

  59. [67]

    Y., Peter, A

    Kim, S. Y., Peter, A. H. G., & Hargis, J. R. 2018, Physical Review Letters, 121, 211302

  60. [68]

    G., & Hjorth, J

    Kneib, J.-P., Cohen, J. G., & Hjorth, J. 2000, ApJL, 544, L35 Kovačević, J., Popović, L. Č., & Dimitrijević, M. S. 2010, ApJS, 189, 15

  61. [69]

    R., & MacKenty, J

    Kuntschner, H., Bushouse, H., Kümmel, M., Walsh, J. R., & MacKenty, J. 2010, in Society of Photo-Optical Instru- mentation Engineers (SPIE) Conference Series, Vol. 7731, Proceedings of SPIE, 77313A

  62. [70]

    A., Auger, M

    Lemon, C. A., Auger, M. W., & McMahon, R. G. 2019, MNRAS, 483, 4242

  63. [71]

    A., Auger, M

    Lemon, C. A., Auger, M. W., McMahon, R. G., & Ostro- vski, F. 2018, MNRAS, 479, 5060

  64. [72]

    S., Cole, S., Wang, Q., & Gao, L

    Li, R., Frenk, C. S., Cole, S., Wang, Q., & Gao, L. 2017, MNRAS, 468, 1426 Lovell,M.R.,Frenk,C.S.,Eke,V.R., etal.2014, MNRAS, 439, 300

  65. [73]

    R., Eke, V., Frenk, C

    Lovell, M. R., Eke, V., Frenk, C. S., et al. 2012, MNRAS, 420, 2318 Macciò, A. V., & Fontanot, F. 2010, MNRAS, 404, L16

  66. [74]

    2018, MNRAS, 480, 5203

    Maddox, N. 2018, MNRAS, 480, 5203

  67. [75]

    R., Wong, K

    McCully, C., Keeton, C. R., Wong, K. C., & Zabludoff, A. I. 2017, ApJ, 836, 141

  68. [76]

    2012, MNRAS, 421, 2384

    Menci, N., Fiore, F., & Lamastra, A. 2012, MNRAS, 421, 2384

  69. [77]

    2017, ApJ, 836, 61

    Menci, N., Merle, A., Totzauer, M., et al. 2017, ApJ, 836, 61

  70. [78]

    G., Brammer, G

    Momcheva, I. G., Brammer, G. B., van Dokkum, P. G., et al. 2016, ApJS, 225, 27

  71. [79]

    D., Caldwell, J

    Morgan, N. D., Caldwell, J. A. R., Schechter, P. L., et al. c© 2016 RAS, MNRAS000, 1–16 Narrow-line lensing with the WFC3 grism 15 2004, AJ, 127, 2617

  72. [80]

    M., & Kochanek, C

    Mosquera, A. M., & Kochanek, C. S. 2011, ApJ, 738, 96

  73. [81]

    A., & Metcalf, R

    Moustakas, L. A., & Metcalf, R. B. 2003, MNRAS, 339, 607 Müller-Sánchez, F., Prieto, M. A., Hicks, E. K. S., et al. 2011, ApJ, 739, 69

  74. [82]

    M., Treu, T., Wright, S

    Nierenberg, A. M., Treu, T., Wright, S. A., Fassnacht, C. D., & Auger, M. W. 2014, MNRAS, 442, 2434

  75. [83]

    M., Treu, T., Brammer, G., et al

    Nierenberg, A. M., Treu, T., Brammer, G., et al. 2017, MNRAS, 471, 2224

  76. [84]

    A., et al

    Oguri, M., Inada, N., Blackburne, J. A., et al. 2008, MN- RAS, 391, 1973

  77. [85]

    Oguri, M., & Marshall, P. J. 2010, MNRAS, 405, 2579

  78. [86]

    E., & Ferland, G

    Osterbrock, D. E., & Ferland, G. J. 2006, Astrophysics of gaseous nebulae and active galactic nuclei

  79. [87]

    A., Auger, M

    Ostrovski, F., Lemon, C. A., Auger, M. W., et al. 2018, MNRAS, 473, L116

  80. [88]

    Peter, A. H. G., & Benson, A. J. 2010, Phys. Rev. D., 82, 123521

  81. [89]

    M., Denney, K

    Peterson, B. M., Denney, K. D., De Rosa, G., et al. 2013, ApJ, 779, 109

  82. [90]

    2011, Phys

    Polisensky, E., & Ricotti, M. 2011, Phys. Rev. D., 83, 043506

  83. [91]

    M., Wechsler, R

    Reddick, R. M., Wechsler, R. H., Tinker, J. L., & Behroozi, P. S. 2012, ArXiv e-prints

  84. [92]

    J., Baade, R., Lopez, S., & Tytler, D

    Reimers, D., Hagen, H. J., Baade, R., Lopez, S., & Tytler, D. 2002, A&A, 382, L26

  85. [93]

    2019, MNRAS, 485, 2179

    Ritondale, E., Vegetti, S., Despali, G., et al. 2019, MNRAS, 485, 2179

  86. [94]

    Rocha, M., Peter, A. H. G., Bullock, J. S., et al. 2013, MNRAS, 430, 81

  87. [95]

    S., & Keeton, C

    Rusin, D., Kochanek, C. S., & Keeton, C. R. 2003, ApJ, 595, 29

  88. [96]

    E., Berghea, C

    Rusu, C. E., Berghea, C. T., Fassnacht, C. D., et al. 2019, MNRAS, 486, 4987

  89. [97]

    E., Oguri, M., Minowa, Y., et al

    Rusu, C. E., Oguri, M., Minowa, Y., et al. 2016, MNRAS, 458, 2

  90. [98]

    J., Sales, L

    Sameie, O., Benson, A. J., Sales, L. V., et al. 2019, ApJ, 874, 101

  91. [99]

    S., Fattahi, A., et al

    Sawala, T., Frenk, C. S., Fattahi, A., et al. 2016, MNRAS, 457, 1931

  92. [100]

    L., Morgan, N

    Schechter, P. L., Morgan, N. D., Chehade, B., et al. 2017, AJ, 153, 219

  93. [101]

    S., & Lake, G

    Schneider, A., Trujillo-Gomez, S., Papastergis, E., Reed, D. S., & Lake, G. 2017, MNRAS, 470, 1542

  94. [102]

    J., Birrer, S., Treu, T., et al

    Shajib, A. J., Birrer, S., Treu, T., et al. 2019, MNRAS, 483, 5649

  95. [103]

    F., Hutsemékers, D., & Surdej, J

    Sluse, D., Claeskens, J. F., Hutsemékers, D., & Surdej, J. 2007, A&A, 468, 885

  96. [104]

    2012, A&A, 544, A62

    Wambsganss, J. 2012, A&A, 544, A62

  97. [105]

    2014, A&A, 571, A60

    Sluse, D., & Tewes, M. 2014, A&A, 571, A60

  98. [106]

    2011, A&A, 528, A100

    Sluse, D., Schmidt, R., Courbin, F., et al. 2011, A&A, 528, A100

  99. [107]

    Somerville, R. S. 2002, ApJL, 572, L23

  100. [108]

    2008, MN- RAS, 391, 1685

    Springel, V., Wang, J., Vogelsberger, M., et al. 2008, MN- RAS, 391, 1685

  101. [109]

    E., Bullock, J

    Strigari, L. E., Bullock, J. S., Kaplinghat, M., et al. 2007, ApJ, 669, 676

  102. [110]

    J., Bullock, J

    Tollerud, E. J., Bullock, J. S., Strigari, L. E., & Willman, B. 2008, ApJ, 688, 277

  103. [111]

    A., et al

    Treu, T., Agnello, A., Baumer, M. A., et al. 2018, MNRAS, 481, 1041 Vanden Berk, D. E., Richards, G. T., Bauer, A., et al. 2001, AJ, 122, 549

  104. [112]

    Vegetti, S., Koopmans, L. V. E., Auger, M. W., Treu, T., & Bolton, A. S. 2014, MNRAS, 442, 2017

  105. [113]

    J., McKean, J

    Vegetti, S., Lagattuta, D. J., McKean, J. P., et al. 2012, Nature, 481, 341

  106. [114]

    Hirata, C. M. 2016, Phys. Rev. D., 94, 043515

  107. [115]

    D., Bolton, J

    Viel, M., Becker, G. D., Bolton, J. S., & Haehnelt, M. G. 2013, Phys. Rev. D., 88, 043502

  108. [116]

    2016, MNRAS, 460, 1399

    Vogelsberger, M., Zavala, J., Cyr-Racine, F.-Y., et al. 2016, MNRAS, 460, 1399

  109. [117]

    2012, MNRAS, 423, 3740

    Vogelsberger, M., Zavala, J., & Loeb, A. 2012, MNRAS, 423, 3740

  110. [118]

    Wang, M.-Y., & Zentner, A. R. 2012, Phys. Rev. D., 85, 043514

  111. [119]

    D., Sluse, D., Gao, L., et al

    Xu, D. D., Sluse, D., Gao, L., et al. 2013, ArXiv e-prints

  112. [120]

    H., Norman, M

    Xu, H., Wise, J. H., Norman, M. L., Ahn, K., & O’Shea, B. W. 2016, ApJ, 833, 84

  113. [121]

    2008, A&A, 478, 95 c© 2016 RAS, MNRAS000, 1–16 16 Nierenberg et al

    Yonehara, A., Hirashita, H., & Richter, P. 2008, A&A, 478, 95 c© 2016 RAS, MNRAS000, 1–16 16 Nierenberg et al. 1 2 3 C/A 0.5 1.0 1.5 2.0 2.5 3.0B/A Model Data 1 2 3 D/A 0.5 1.0 1.5 2.0 2.5 3.0 3.5C/A 0.5 1.0 1.5 2.0 B/A 0.25 0.50 0.75 1.00 1.25 1.50 1.75 2.00D/A Figure A1. Spe...

Pith tools

Reviewed August 14, 2026 · model on record in the stance chip above.