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Dust leaves a line-dependent fingerprint in multi-line intensity maps that can survive continuum cleaning and be used to measure attenuation.

Reviewed by Pith at T0; open to challenge. T0 means a machine referee read the full paper against a public rubric. the ladder, T0–T4 →

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2026-08-01 12:33 UTC pith:GDIC43FP

load-bearing objection Careful mock-validation of multi-line LIM with combined dust, continuum, and PCA transfer; the pipeline work is solid, and the recovery tests are honest self-consistency checks, not external validation. the 3 major comments →

arxiv 2607.19504 v1 pith:GDIC43FP submitted 2026-07-21 astro-ph.GA astro-ph.COastro-ph.IM

Reading Between the Lines: Forward Modeling Dust, Continuum, and Spectral Cleaning for Multi-Line Intensity Mapping

Anirban Roy , Rachel S. Somerville , Anthony R. Pullen This is my paper
classification astro-ph.GA astro-ph.COastro-ph.IM
keywords line intensity mappingmulti-line intensity mappingdust attenuationcross-channel power spectrumPCA spectral cleaningsame-redshift ridgessemi-analytic galaxy modelemission lines
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

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

The paper tries to establish that the wavelength-by-wavelength correlation matrix of an optical/near-infrared line intensity mapping survey contains recoverable information about galaxy dust, even though the stellar continuum is orders of magnitude brighter. It builds mock maps of Hα, Hβ, [O III], and [O II] from a cosmological lightcone populated with a semi-analytic galaxy formation model, assigning nebular dust attenuation galaxy by galaxy. It shows that dust suppresses each line-pair cross-power by a redshift- and wavelength-dependent amount, so a single amplitude correction cannot capture the effect. It then shows that removing about 20 principal components from the total cubes suppresses the continuum while preserving enough of the line signal, provided the cleaning's channel-dependent transfer function is included in the analysis. If correct, a near-infrared all-sky spectral survey can constrain effective nebular dust attenuation together with the line amplitude, turning line intensity mapping from a detection tool into a probe of dusty galaxies.

Core claim

The central claim is that the cross-channel power spectrum matrix C_ℓ(λ_i, λ_j) of multi-line intensity maps carries a geometric ridge pattern from same-redshift line pairs, and that this pattern survives continuum cleaning well enough to infer the nebular dust attenuation parameter f_neb (the nebular-to-stellar attenuation factor). Dust suppresses the cross-power along each ridge by a factor that depends on rest wavelength, galaxy properties, and redshift, which the paper encodes in a suppression matrix S_ij; this suppression cannot be absorbed into one global amplitude. After removing about 20 PCA modes from the total cube, the cleaned ridge amplitudes, modeled together with the cleaning t

What carries the argument

The central object is the cross-channel angular power spectrum matrix C_ℓ(λ_i, λ_j), whose off-diagonal same-redshift ridges are fixed by rest-wavelength ratios λ_b = λ_a (λ_b,rest / λ_a,rest). The paper compares this matrix across line-only, dust/no-dust, and PCA-cleaned versions, using the dust-suppression matrix S_ij = C_dust / C_nodust and a line transfer function measured by injecting a line-only cube through exactly the same cleaning operation as the total cube.

Load-bearing premise

The conclusions rest on the assumption that the mock galaxy population reproduces real line emission: each line's luminosity is a fixed multiple of star-formation rate with no metallicity or redshift dependence, and every galaxy shares the same slab-geometry Calzetti attenuation with f_neb=2.27.

What would settle it

Measure Balmer-decrement and [O II]/Hα ratios of resolved star-forming galaxies at z≈1–3 in the same volume as a line intensity mapping survey; if these ratios deviate systematically from the predictions of the slab model with f_neb=2.27 and the Calzetti curve, the predicted ridge-suppression matrix S_ij will not match real data, and the recovered effective dust parameter from cleaned maps would be biased.

Watch this falsifier — get emailed when new claim-graph text bears on it.

If this is right

  • A single global dust correction is inadequate for line intensity mapping forecasts; dust suppression must be modeled per line pair, wavelength, and redshift.
  • The normalized correlation matrix r_ij isolates ridge geometry because multiplicative suppression cancels, while the dimensional C_ℓ carries the dust amplitude information, so both statistics should be used together.
  • Removing roughly 15–25 PCA modes, with about 20 as the best single choice, is a workable cleaning prescription for continuum-dominated cubes, but only if the channel-dependent transfer function is part of the likelihood.
  • Using the full wavelength–wavelength matrix rather than ridge elements alone, and combining multiple multipoles, tightens constraints on line amplitude and effective dust.
  • Nebular dust attenuation can be probed statistically with multi-line intensity mapping, not just the total line emission.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • If real oxygen-line ratios vary strongly with metallicity, as observations suggest, the fixed line-SFR coefficients in the mock will make ridge contrasts too sharp; a metallicity-dependent extension should weaken ridge amplitudes and broaden the inferred dust constraints.
  • The same-redshift ridge geometry could serve as a blind 'geometric lock' to separate line emission from interloping lines and residual continuum before any amplitude inference, extending the paper's cleaning diagnostics to real data.
  • The PCA20 optimum is tied to this specific mock; the portable result is the injection-based stopping test, which can be rerun on real observations without truth maps.
  • Applying the transfer-function injection method to actual survey data would test whether the cleaning model is accurate, since the recovered f_neb should agree with dust estimates from resolved galaxies in the same volume.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 5 minor

Summary. The paper presents an end-to-end forward model of multi-line intensity mapping (MLIM) for optical/NIR surveys like SPHEREx. It uses a ~2 deg^2 lightcone from the SMDPL simulation populated with the Santa Cruz SAM, assigns Hα, Hβ, [O III], and [O II] luminosities proportional to SFR with fixed population-averaged coefficients, applies a slab-geometry nebular dust attenuation tied to galaxy properties (f_neb=2.27, Calzetti+00 curve), constructs continuum and line maps, and measures cross-channel angular power spectra and normalized correlation matrices. It shows that dust suppresses the line cross-power in a wavelength- and line-pair-dependent way that cannot be represented by a single amplitude, that PCA cleaning with ~20 removed modes optimally balances continuum suppression and line recovery, and that the cleaned cross-channel matrix can recover injected line amplitude and dust parameters in mock inference tests.

Significance. If the results are robust, this framework is a valuable and much-needed tool for interpreting the line signal in SPHEREx-like spectral cubes. The explicit treatment of the cleaning transfer function, the warning that dust is not a single amplitude rescaling, and the demonstration that the normalized correlation matrix is dust-insensitive while the dimensional cross-power is not, are conceptually important and likely to influence future LIM analyses. The use of a public SAM catalog and the publication of clear mock-recovery diagnostics are strengths. However, the quantitative conclusions are drawn from a single realization, and the inference tests are self-consistency checks with the same model used for both data and template, so the external validity of the dust-recovery claim remains unproven.

major comments (3)
  1. [Sec. 6; Eqs. (43)-(46); Figs. 16-18] The mock inference is circular with respect to the dust and line-luminosity model: the data and the likelihood templates are generated from the same SAM catalog, the same fixed r_i coefficients (Eqs. 3-7), and the same slab/Calzetti dust prescription with f_neb=2.27. Recovering the injected A_dust=0.8 or f_neb=1.7 demonstrates internal consistency only, not that a real SPHEREx-like measurement can constrain effective attenuation if the true galaxy population has different intrinsic line ratios or attenuation curves. The authors acknowledge the simplifications in Secs. 2.2 and 7.5, but the abstract and Sec. 8 state that nebular dust attenuation 'can be probed statistically with MLIM' without this caveat. I request a stress test that uses a different intrinsic line-ratio model (e.g., metallicity-dependent [O II]/Hα from the SAM metallicities) or a different dust prescription, quantifying a
  2. [Sec. 5; Sec. 6; Eq. (40); Eq. (48)] All quantitative results—the PCA20 optimum, the transfer-quality metric Qtransfer, and the posterior contours—are based on a single 2 deg^2 realization. The covariance model used in the Gaussian likelihood (Eq. 48) is not specified, and no sample-variance or realization noise is propagated into Qtransfer or the MCMC uncertainties. It is therefore unclear whether the PCA20 choice is robust to cosmic variance and whether the quoted 1-σ constraints reflect the true statistical power. The authors should describe the covariance model, and at minimum test the stability of the PCA20 recommendation with a jackknife or a second lightcone realization. This is load-bearing because the central methodological claim (PCA20) is a single-realization statistic.
  3. [Sec. 2.2; Sec. 4.3; Fig. 8] The fixed line-SFR coefficients r_i mean that the relative amplitudes of the same-redshift ridges are driven solely by dust once the clustering normalization is fixed. In real galaxies, [O II]/Hα and [O III]/Hα vary strongly with metallicity, ionization parameter, and redshift (Kewley+04, Tremonti+04, Sanders+21), so the observed ridge ratios are a convolution of intrinsic ratio variations and dust. The SAM catalog provides metallicities, so the model could naturally include metallicity-dependent oxygen-line luminosities. Without at least a sensitivity test showing how the dust-suppression matrix and the inferred f_neb change when r_OII and r_OIII vary within the observed range, the paper's claim that dust 'cannot be captured via a single correction factor' conflates dust effects with the chosen constant line-ratio model. The text should be more careful to state that the dust signal is s
minor comments (5)
  1. [Eq. (29)] Please clarify whether f_nu,i,g is the flux density (Jy) or surface-brightness contribution (Jy/sr). The shot-noise formula as written may need a factor of the pixel solid angle to match the power-spectrum units; the current notation is ambiguous.
  2. [Eq. (48)] The covariance model used for the Gaussian likelihood is not described. The authors should state how sigma_ij is computed (instrumental noise only, or also sample variance and cleaning mode coupling) to assess the quoted uncertainties.
  3. [Fig. 6 and Fig. 7] The symmetric logarithmic color scales are helpful, but the captions should explicitly mention how negative values are handled and what the white/zero color represents.
  4. [Sec. 5.1 and Fig. 14] The injection test used as a data-driven stopping criterion injects a line template built from the same line-SFR model. This partially reintroduces the model dependence and should be acknowledged in the text.
  5. [General] The paper would benefit from a short algorithm box summarizing the forward-modeling steps from SAM catalog to cleaned maps and parameter inference, helping reproducibility.

Circularity Check

0 steps flagged

No significant circularity: central claims are consequences of the explicit forward model; recovery tests are transparently labeled closed-loop validations.

full rationale

This paper is a forward-modeling study: it builds mock LIM cubes from an external SAM lightcone (Yung et al. 2023), assigns line luminosities with fixed population-averaged ratios (Eqs. 3-7), applies a slab+Calzetti nebular attenuation (Eqs. 8-13), and examines the resulting cross-channel spectra and PCA cleaning. The central claim that dust suppression is line-pair- and wavelength-dependent is a direct, transparent consequence of the adopted attenuation model (Eq. 11: A_neb_X = R_att(lambda_X) A_neb_V and Eq. 13: f_dust = 10^(-0.4A)), not an inference that returns the model's assumptions under a new name. The mock recovery tests in Section 6 are explicitly labeled closed-loop checks: 'we deliberately inject an off-fiducial truth value, A_dust=0.8' and 'we inject an off-fiducial value f_neb=0.75 f_neb,0~1.7.' Because both the mock data and the likelihood templates (Eqs. 43-46) are drawn from the same forward model, recovering these injected values demonstrates internal consistency of the pipeline (interpolation, likelihood, transfer function), not external validity. The paper itself flags this: 'This work serves as a first end-to-end validation of the modeling and inference framework for a LIM survey, not as a survey forecast' (Sec. 7.5). Citations to the authors' own SAM and dust-curve work provide the simulation infrastructure but are external, calibrated models rather than a self-citation 'uniqueness' chain used to forbid alternatives. No fitted parameter is renamed as a prediction, and no step in the claimed derivation is equivalent by definition to its input.

Axiom & Free-Parameter Ledger

4 free parameters · 6 axioms · 0 invented entities

The central claimed effects - line-pair-dependent dust suppression, PCA cleaning trade-offs, and parameter recovery - rest almost entirely on the SAM lightcone and the adopted line/dust prescriptions. No new physical entity is introduced. The most consequential free choices are the line-SFR coefficients, the f_neb=2.27 nebular enhancement, the Calzetti attenuation curve, and the post hoc choice of 20 PCA modes.

free parameters (4)
  • Line-SFR coefficients r_i = r_Ha=1.27e41, r_Hb=0.44e41, r_[OIII]=1.32e41, r_[OII]=0.71e41 (erg/s per Msun/yr)
    Adopted from Kennicutt (1998) and Gong et al. (2014, 2017); fixed for all galaxies and redshifts. These set the intrinsic line ratios that control the ridge amplitudes in the cross-channel matrices.
  • Nebular-to-stellar attenuation factor f_neb = 2.27
    Used in Eq. 10 to scale stellar attenuation into nebular attenuation, following Calzetti et al. (2000). Directly sets the overall strength of the dust suppression effect.
  • Attenuation curve shape (Calzetti+00 parameters) = (c1,c2,c3,c4) = (44.9,7.56,61.2,0) in Eq. 12
    Adopted from Li et al. (2008) / Sommovigo et al. (2025). The wavelength dependence of R_att is the root of the claim that dust cannot be a single amplitude.
  • Number of PCA modes removed N_PCA = 20
    Selected post hoc by maximizing Qtransfer on the mock truth (Figs. 12-13). It is a diagnostic-based choice, not an independently predicted value, and carries no realization-level uncertainty.
axioms (6)
  • standard math Case B recombination gives fixed intrinsic H-alpha/H-beta ~ 2.86 and subordinate Balmer line ratios.
    Used to set r_Hb relative to r_Ha in Eqs. 4-7; atomic-physics standard assumption invoked in Section 2.2.
  • domain assumption Line luminosities are exactly proportional to SFR with constant coefficients r_i for all galaxies and redshifts.
    Eq. 3 with Eqs. 4-7. The authors explicitly note this neglects metallicity and ionization-parameter dependence; required for the ridge amplitudes and A_dust recovery.
  • domain assumption The SMDPL/Santa Cruz SAM lightcone accurately represents the galaxy population relevant for LIM, including clustering and dust properties.
    Section 2.1 relies on the SAM being calibrated at z~0 and tested to higher redshift in cited papers. The simulation truth is the foundation for all map-level claims.
  • domain assumption Nebular attenuation follows the Calzetti+00 curve with f_neb=2.27 and a slab inclination model for all galaxies.
    Eqs. 8-13. This is a literature-based prescription, not derived in this paper, and it directly controls the dust-suppression results.
  • domain assumption PCA cleaning transfer can be measured by a linear small-amplitude injection (finite difference in Eq. 33).
    Assumes the cleaning operator behaves linearly enough around the operating point; no nonlinearity check is provided.
  • domain assumption A Gaussian likelihood with the adopted mock covariance adequately represents cleaned-cube uncertainties.
    Eq. 48. The covariance is not fully specified and does not include survey masks, beam, calibration, or cleaning-induced mode coupling.

pith-pipeline@v1.3.0-alltime-deepseek · 33565 in / 15545 out tokens · 164338 ms · 2026-08-01T12:33:13.535999+00:00 · methodology

0 comments
read the original abstract

Line intensity mapping (LIM) offers a tomographic view of galaxy evolution by measuring the aggregate emission from unresolved galaxies. In the optical and near-infrared, the line emission is accompanied by much brighter continuum emission that must be removed to recover line auto- and cross-power spectra. We develop a forward-modeling framework using a $\sim2$deg$^2$ lightcone drawn from a cosmological $N$-body simulation populated with galaxies using a physics-based semi-analytic model (SAM). We construct intensity maps for the stellar continuum and for the strongest optical lines, H$\alpha$, H$\beta$, [O III] $\lambda5007$, and [O II] $\lambda3727$, including nebular dust attenuation tied to galaxy properties. From these cubes, we measure cross-channel angular power spectra, $C_\ell(\lambda_i,\lambda_j)$, and the normalized correlation matrix $r_{ij}$. In line-only maps, the correlation matrices show same-redshift ridges between emission lines, demonstrating how multi-line intensity mapping (MLIM) can isolate large-scale structure and probe dust attenuation. We show that dust suppresses the line cross-power by an amount that depends on galaxy properties, wavelength, and line pair, so a single overall amplitude cannot capture its effect. In total maps, however, the continuum dominates the raw correlations and hides much of the line-ridge structure. We therefore apply principal component analysis (PCA)-based spectral cleaning and quantify the line-transfer function using the simulation truth. Removing 20 PCA modes gives the best trade-off between line recovery and continuum suppression in our mock maps. Our results demonstrate the promise and challenges of extracting dust-sensitive LIM observables from SPHEREx-like observations, and highlight the need to model continuum cleaning and its transfer function in quantitative inference pipelines.

Figures

Figures reproduced from arXiv: 2607.19504 by Anirban Roy, Anthony R. Pullen, Rachel S. Somerville.

Figure 1
Figure 1. Figure 1: Geometry of the SAM lightcone catalogue. Each point is a halo, plotted by redshift and transverse comoving offset from the field center and colored by mass. For clarity, only a random subsample of the full catalogue is plotted. The widening of the cone with redshift reflects the fixed angular footprint projected into comoving coordinates, while the concentration of massive haloes at low redshift reflects h… view at source ↗
Figure 2
Figure 2. Figure 2: Redshift coverage of the SAM lightcone and the modeled rest-frame optical lines. The histogram shows the galaxy redshift distribution in the catalog, while the colored bars mark the redshift intervals over which Hα, Hβ, [O iii], and [O ii] fall in the 0.75–5 µm observed-frame band. The shaded region marks the interval 1.0 < z < 6.6 where all four modeled lines are simultaneously observable, setting the red… view at source ↗
Figure 3
Figure 3. Figure 3: Attenuation curves used to illustrate the wavelength dependence of dust suppression. The curves show Aλ/AV as a function of rest-frame wavelength for several commonly used prescriptions: Calzetti, SMC, Milky Way, LMC, and the TNG median curve from Sommovigo et al. (2025). Teal dotted lines mark the rest-frame wavelengths of the four emission lines in our LIM model, while magenta dotted lines mark prominent… view at source ↗
Figure 4
Figure 4. Figure 4: Dust transmission for the four rest-frame optical lines used in this work, shown as a function of the stellar– continuum attenuation A star V . Solid curves show the model prediction for the Calzetti attenuation curve with the nebu￾lar enhancement A neb V = 2.27 A star V . Points show the median transmission for star-forming galaxies in the selected SAM redshift slice, binned by A star V , and shaded regio… view at source ↗
Figure 5
Figure 5. Figure 5 [PITH_FULL_IMAGE:figures/full_fig_p011_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Left: auto-spectra D ii ℓ = ℓ(ℓ + 1)C ii ℓ /2π for representative observed wavelength channels. Right: cross-spectra between an Hα-selected anchor channel at λi = 1.98 µm, corresponding to z ≃ 2.02, and several other observed wavelength chan￾nels λj . Solid curves mark channels corresponding to the same-redshift [O ii], Hβ, [O iii], and Hα lines for this anchor redshift; dashed curves show comparison wavel… view at source ↗
Figure 7
Figure 7. Figure 7: Cross-channel angular power spectra and correlation matrices for the SPHEREx-like observed-frame intensity maps at target multipole ℓ = 12000, using the nearest saved bin ℓ = 11571, over the full 0.75–5 µm band. Columns compare the no-dust case (left) and the fiducial dust-attenuated case (right). Top: line-only cross-power for Hα, Hβ, [O iii], and [O ii] in units of (Jy sr−1 ) 2 sr, on a symmetric logarit… view at source ↗
Figure 8
Figure 8. Figure 8: Redshift evolution of same redshift line-ridge observables for the line-only maps at ℓ ≃ 11571. Each curve follows the geometric locus where two observed wavelength channels correspond to two different rest-frame lines emitted at the same redshift. Points show medians in ∆z = 0.5 bins. The top panel shows the normalized correlation coefficient. Solid curves include the fiducial SAM nebular dust attenua￾tio… view at source ↗
Figure 9
Figure 9. Figure 9: Cumulative source-level contribution to the observed integrated line flux over the full lightcone. The three panels rank galaxies by star-formation rate, stellar mass, and halo mass, respectively. The quantity plotted is the fraction of the total observed line flux produced by galaxies above the threshold on the horizontal axis. Solid curves include the fiducial SAM dust attenuation, while dashed curves sh… view at source ↗
Figure 10
Figure 10. Figure 10: Dust-suppression matrix for the line-only LIM maps. We show Sij (ℓ) ≡ C ij ℓ,dust/Cij ℓ,no dust as a function of observed wavelength channels (λi, λj ) at ℓ = 4930 and ℓ = 11571. The numerator and denominator are measured from the same SAM lightcone, with the only change being the inclusion or removal of nebular dust attenuation in the line luminosities. Values below unity indicate suppression of the line… view at source ↗
Figure 11
Figure 11. Figure 11: Shot noise decomposition of the line-only cross-channel power matrix at ℓ ≃ 11571. From left to right we show the total line power, the estimated shot noise term, and the residual clustering contribution C ij ℓ,cluster = C ij ℓ,total − C ij ℓ,shot. The signed logarithmic color scale displays positive and negative matrix elements while preserving the line-ridge morphology. The shot term is not limited to t… view at source ↗
Figure 12
Figure 12. Figure 12: PCA-cleaning performance as a function of the number of spectral modes removed, evaluated near the target multipole ℓ = 12000 using the nearest available bin, ℓ = 11571. The red star symbols show the combined trans￾fer quality metric, Qtransfer, plotted on the right-hand axis. The dashed vertical line marks the preferred choice, PCA20, which gives the best balance between recovering the line sig￾nal and s… view at source ↗
Figure 13
Figure 13. Figure 13: Transfer-quality metric, Qtransfer(ℓ, NPCA), evaluated over a grid of target multipoles and numbers of PCA modes removed. Each cell gives the value of Qtransfer for the corresponding cleaning choice. The red box marks the best-performing configuration in this grid, PCA20 at target ℓ = 12000. The metric is largest for intermediate cleaning strength: removing too few modes leaves continuum resid￾uals, while… view at source ↗
Figure 14
Figure 14. Figure 14: Real-data-like PCA stopping test at ℓ ≃ 11571. The total map is treated as the observed data, and a known line-only template is injected into this map before rerunning the same PCA cleaning procedure. The blue curve shows the median off-ridge correlation in the cleaned wavelength–wave￾length matrix, which traces broad continuum-like residuals. The orange curve shows the median recovered transfer of the in… view at source ↗
Figure 15
Figure 15. Figure 15: Pre-cleaning continuum-parameter inference from the wavelength cross-power matrix. The contours show the posterior constraints on the two continuum model param￾eters, Acont and αcont. The correlation between parameters reflects the fact that changes in the continuum amplitude and broad spectral tilt both affect the broadband continuum power. duced. For σinst = 104 Jy sr−1 , the marginalized 1σ uncertainti… view at source ↗
Figure 17
Figure 17. Figure 17: Dependence of the post-cleaning LIM parame￾ter constraints on the instrumental noise level. The contours show the inferred line amplitude, Aline, and dust parame￾ter, Adust, after forward PCA cleaning for different choices of the instrumental noise amplitude. As the instrumental noise increases, the posterior broadens, while the degeneracy direction remains similar. This confirms that the inference respon… view at source ↗
Figure 16
Figure 16. Figure 16: Post-cleaning inference of the LIM line-sec￾tor parameters using the forward PCA-cleaning model. Top: constraints on the line amplitude, Aline, and dust parame￾ter, Adust, comparing the ridge-only and full-matrix analy￾ses at fixed multipole. Bottom: comparison of constraints obtained from different multipole choices, including single-ℓ and multi-ℓ analyses. The agreement between the contours and the fidu… view at source ↗
Figure 18
Figure 18. Figure 18: Recovery of the nebular dust parameter af￾ter PCA cleaning. The contours show the joint posterior of the line-amplitude parameter Aline and the effective dust parameter fneb, using the forward-cleaned PCA20 templates at ℓ ≃ 11571. Blue contours use only the same-redshift line-ridge elements of the wavelength–wavelength matrix, while orange contours use the full selected matrix. The dot￾ted lines mark the … view at source ↗

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Forward citations

Cited by 1 Pith paper

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

  1. Interlopers as Signal in Line Intensity Mapping

    astro-ph.CO 2026-07 conditional novelty 6.0

    Jointly modeling LIM interlopers as projected multi-redshift tracers often beats cleaning for Ω_m h² and BAO distances, yielding a transverse BAO ladder over 0.7≲z≲5.8 in SPHEREx/FYST forecasts.

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