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BASS XLV: Quantifying AGN Selection Effects in the Chandra COSMOS-Legacy Survey with BASS

T0 review · 4 major / 4 minor · reviewed 2026-08-10 · deepseek-v4-flash

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Pith's one-line read Chandra's deep COSMOS-Legacy survey misses most obscured AGN and mislabels unobscured sources as Compton-thick, so reported obscuration evolution is likely overstated.

desk verdict A careful, transparent forward-model simulation that convincingly shows Chandra-like surveys severely miss obscured AGN and that low-count 2PL fits fabricate CT sources; the headline fractions are conditional on the local BASS prior but the central bias argument holds. read the letter →

arxiv 2501.16708 v2 pith:YWCZXBIJ submitted 2025-01-28 astro-ph.GA

Yarone M. Tokayer , Michael J. Koss , C. Megan Urry , Priyamvada Natarajan , Richard Mushotzky , Mislav Balokovic , Franz E. Bauer , Peter Boorman
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Alessandro Peca Claudio Ricci Federica Ricci Daniel Stern Ezequiel Treister Benny Trakhtenbrot
This is my paper · ORCID
classification astro-ph.GA
keywords AGNobscurationCompton-thickChandraCOSMOS-LegacyBASSsurveyX-rayselectionbiasforwardmodelingcolumndensityobscuredfractionevolution
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 tests how much the deepest Chandra surveys are biased against obscured active galactic nuclei (AGN) by taking 380 well-measured, nearby AGN spectra from the BAT AGN Spectroscopic Survey, moving them to the redshifts, luminosities, and exposure times of the Chandra COSMOS-Legacy Survey, and re-running the survey's own fitting pipeline on the simulated data. It finds that Chandra would detect only 46.7% of simulated sources with obscuring column densities above $10^{22}\ \mathrm{cm}^{-2}$, and only 8.9% of Compton-thick ($N_H \ge 10^{24}\ \mathrm{cm}^{-2}$) sources. Among the sources with enough counts to fit spectra, the fitted column density is systematically overestimated, and the majority of objects classified as Compton-thick—18 of 27—are actually unobscured. The paper concludes that reported increases of the obscured fraction with redshift, and the measured numbers of luminous obscured AGN, have been significantly inflated by these selection effects.

What carries the argument

The forward-modeling pipeline and the double power-law degeneracy, quantified by the primary ratio (PR). The machinery is: (1) 380 BASS X-ray spectral templates with known column density, photon index, and iron line, built from spectra with a median of 1545 counts; (2) each template placed at six CCLS-like redshifts and exposure times and passed through Chandra ACIS response files; (3) the four-step phenomenological fitting procedure from Marchesi et al. (2016b), going from an absorbed power law with fixed photon index to a free photon index, a secondary unabsorbed power law, and an iron K$\alpha$ Gaussian; and (4) the PR diagnostic, $\mathrm{PR} = (n_p - n_s)/(n_p + n_s)$, the fraction of detected photons belonging to the primary versus secondary component. When the energy at which the two power laws cross lies outside Chandra's 0.5–7 keV band, the PR shows that the double power law cannot distinguish an unobscured single power law from a heavily obscured source with soft leakage, which is exactly what produces the false Compton-thick classifications. The PR is what makes the bias systematic and predictable rather than a random fitting artifact.

What would settle it

Re-observe the 27 CCLS sources with best-fit $\log N_H \ge 24$ from Marchesi et al. (2016b) with NuSTAR, or stack their Chandra data at their known redshifts: if most show an unobscured soft continuum with no hard-band excess, the false-Compton-thick claim is supported; if most show flat reflected hard spectra with $\log N_H \ge 24$, the central inference fails.

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Extended reading notes

Core claim

The central claim, stated as the authors would state it: at the depth of the Chandra COSMOS-Legacy Survey, the standard Chandra-based method for measuring AGN obscuration is not merely incomplete but asymmetrically biased. Given the CCLS redshift-luminosity distribution, Chandra would fail to detect 53.3% (563/1056) of obscured simulated BASS AGN ($N_H \ge 10^{22}\ \mathrm{cm}^{-2}$) and 91.1% (175/192) of Compton-thick AGN ($N_H \ge 10^{24}\ \mathrm{cm}^{-2}$). For detected spectra with at least 30 counts, fitting exactly as in Marchesi et al. (2016b) recovers column densities accurately on average (median $\Delta \log N_H = -0.10$ dex) but with a large tail of overestimates; among 27 spectra whose best fit placed them in Compton-thick territory, 18 (66.7%) were simulated from completely unobscured templates ($N_H \le 10^{20}\ \mathrm{cm}^{-2}$). A double power-law model with a scattered secondary component is the culprit: it lets a low-count unobscured spectrum masquerade as a heavily absorbed one. Consequently, the measured obscured fraction increases with redshift and declines with luminosity even when the underlying simulated population is flat or declining, so earlier X-ray surveys likely overstate obscuration evolution.

Load-bearing premise

The intrinsic X-ray spectra of local BASS AGN—continuum slope, reflection hump, soft excess, and iron line—are exactly what high-redshift CCLS AGN look like once redshifted, with no cosmic evolution of spectral shape or of the obscuration–luminosity relation.

Editorial extensions

If this is right

  • Any flux-limited Chandra survey at CCLS-like depth will miss more than half of obscured AGN and nearly all Compton-thick AGN at $z \sim 0.5\text{--}3$, so census numbers drawn from such surveys are lower limits.
  • Best-fit column densities from the standard phenomenological pipeline are unreliable for individual low-count sources; measured Compton-thick classifications need a physical torus refit, such as MYTorus, before being believed.
  • The reported rise of the obscured fraction with redshift and the measured abundance of luminous obscured AGN are inflated; the true evolution is weaker than X-ray fitting alone suggests.
  • A single power-law fit estimates $N_H$ at least as well as a double power-law fit for Chandra-quality data, so simpler models are preferable unless the data demand otherwise.
  • A next-generation X-ray mission with roughly ten times Chandra's effective area, such as AXIS, would detect 97.5% of the simulated sources and 51.0% of Compton-thick sources at 30 or more counts, making the bias tractable.

Reading between the lines

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

  • If the local-template assumption holds, the same selection function can be inverted to produce a debiased obscured fraction as a function of redshift and luminosity; the paper stops at demonstrating the bias, so constructing that corrected distribution is a natural next step.
  • The false Compton-thick mechanism implies that multi-wavelength indicators—mid-infrared colors, X-ray-to-infrared flux ratios, or broad H$\beta$ presence—should be used to down-select X-ray Compton-thick candidates, especially at $z > 1$, rather than trusting X-ray best fits alone.
  • The primary-ratio diagnostic could be turned into a survey-design tool: for any future mission bandpass and redshift window, one can precompute the range of $N_H$ values a double power-law fit can actually recover and quote completeness cells instead of point estimates.
  • Because the templates are local and no cosmic evolution of X-ray spectra is modeled, if high-redshift AGN have systematically weaker soft excesses or different reflection, the quantitative biases would shift; this sensitivity argues for stacking tests on real CCLS spectra to check the template assumption.
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Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

4 major / 4 minor

Summary. The paper quantifies how the Chandra COSMOS-Legacy Survey (CCLS) selects against obscured AGN by forward-modeling 380 well-measured local BASS AGN spectral templates, placing them at CCLS redshifts, luminosities, and exposure times, simulating Chandra observations with fakeit, and then fitting the simulated spectra with the same phenomenological pipeline used by Marchesi et al. (2016b). The main quantitative results are that 53.3% (563/1056) of simulated obscured AGN (log N_H >= 22) are undetected, and that among the 27 simulated sources whose best fit implies Compton-thick column densities (log N_H >= 24), 66.7% (18/27) are actually unobscured (log N_H <= 22). The paper also studies photon-index recovery, the role of double power-law fits in producing false CT classifications, refits the false CT sources with MYTorus, and repeats the detection simulation for the proposed AXIS mission. The authors conclude that previous X-ray survey results may have significantly overestimated the growth of the obscured fraction with redshift and the fraction of luminous obscured AGN.

Significance. If the quantitative claims are taken as statements about the CCLS population, the paper is an important, first-of-its-kind large-sample quantification of obscuration bias in a deep Chandra survey, with clear implications for interpreting existing AGN population studies and for designing future missions such as AXIS. The forward-modeling design is a genuine strength: known BASS spectra are passed through a known detector response and a well-defined external fitting pipeline, so the inference is not circular. The paper is also transparent, with simulation catalogs (Tables 4 and 5), appendices testing the fakeit procedure and background assumptions, and concrete AXIS projections. The main limitation is that the simulated intrinsic N_H distribution is deliberately taken from the local BASS parent population rather than from CCLS or from a model of the high-redshift AGN population; all headline percentages are therefore conditional on that prior. This does not invalidate the forward-modeling approach, but it does mean the headline numbers and the redshift-evolution interpretation in Sec. 4.1 are not yet direct statements about the intrinsic CCLS population.

major comments (4)
  1. [Sec. 2.2 and Appendix C] The headline missed-obscured fraction (53.3%; 563/1056) is computed from a simulated population whose intrinsic N_H distribution is the local BASS distribution, not the CCLS distribution. The authors state in Sec. 2.2 that they matched luminosity, redshift, and exposure time but explicitly did not match the CCLS N_H distribution, and Appendix C shows that the simulated N_H distribution differs strongly from CCLS (e.g., 204 of 380 templates have log N_H < 22). Because the detection fraction in Fig. 5 varies steeply with N_H (for example, only 5.6% of sources with log N_H >= 24 are detected at log L = 43.0-43.5, versus 80.3% for log N_H < 21), the 53.3% figure depends directly on the adopted BASS N_H prior. The paper should either state the headline result as conditional on that prior and quantify the sensitivity to alternative intrinsic N_H distributions (e.g., an increasing obscured fraction with redshift), or reweight the simulation to a plausible CCLS-like N_H prior. Without this, the abstract's phrasing that Chandra would fail to detect the majority of obscured sources 'given the observed redshift and luminosity distribution of the CCLS' is incomplete, since the N_H distribution is not observed in the same sense.
  2. [Sec. 3.4, Fig. 7, and abstract] The false-CT fraction (66.7%; 18/27) is prior-dominated and based on small numbers. The 27 best-fit CT sources are drawn from a simulation in which more than half of the templates are unobscured (204/380 with log N_H < 22), so the statement that most best-fit CT sources are actually unobscured is a property of the assumed BASS input population, not of CCLS. If the intrinsic high-redshift population has a higher obscured/CT fraction, as argued by X-ray background synthesis models and multiwavelength studies, the false-CT rate would be lower. The paper should report this fraction with an explicit binomial uncertainty (27 sources gives a 95% interval of roughly 46-83% for the 66.7% estimate) and should present the false-CT probability as a function of the assumed intrinsic obscured fraction, or at least prominently state that the number is conditional on the BASS N_H prior and is not a measured CCLS property.
  3. [Sec. 3.1 and Appendix D] The detection-fraction calculation relies on an incompletely specified 'selection function' correction for sources with fewer than 30 counts. The text says that 'we included a function to randomly reduce the number of sources at the same rate that Chandra would not detect because of background,' but the exact algorithm, the background model, and the matching procedure are not described in the main text, and Appendix D only gives a qualitative discussion (e.g., ~1.4 background counts per aperture, a 3-sigma threshold of ~12 net counts). Since the 67.2% overall detection fraction and thus the 53.3% missed-obscured headline both depend on this correction, the paper should specify the exact correction function, its parameters, and the resulting count-distribution match, and should show how the headline numbers change if the correction is varied or omitted.
  4. [Sec. 4.1 and Fig. 9] The inference that the observed increase in the obscured fraction with redshift is largely a measurement bias assumes no cosmic evolution of the intrinsic N_H distribution or of AGN spectral shapes. The manuscript acknowledges this in Sec. 4.1 ('does not account for any potential change to the intrinsic population of AGN over cosmic time'), but the discussion nevertheless concludes that 'the trend of increasing obscuration with redshift may be much less significant than previously believed' and that previous studies 'may have significantly overestimated' the redshift growth of the obscured fraction. These statements go beyond what the simulation alone can establish, because the simulated 'true' obscured fraction in Fig. 9 is itself derived from the local BASS N_H-luminosity relation. The paper should either restrict the conclusion to the conditional statement 'if the high-redshift population has the same intrinsic N_H distribution and spectral shapes as local BASS AGN, then the X-ray-measured trends overestimate the intrinsic trends,' or include a quantitative exploration of how the bias changes under simple evolutionary scenarios for the intrinsic N_H distribution.
minor comments (4)
  1. [Appendices G.3 and G.4] The figure cross-references appear to be swapped: Sec. G.3 ('high secondary normalization') refers to Fig. 23, but Fig. 23 shows a very low secondary normalization (ratio = 7.03e-05), while Sec. G.4 ('low secondary normalization') refers to Fig. 22, which shows ratio = 0.503. The text and figures should be matched.
  2. [Sec. 3.4 and Sec. 3.5] The paper uses 'unobscured' in two senses: in Sec. 3.4 the 18/27 false-CT sources are described as 'unobscured' with log N_H <= 22, whereas Sec. 3.5 defines false-CT sources as those with log N_H_sim <= 20. These definitions should be reconciled or explicitly distinguished to avoid confusion about which quantity is being reported.
  3. [Sec. 3.5 and Fig. 8 caption] The sentence 'It is notable that almost all of our fits that settled on a secondary normalization very near the upper limit turned out to be false CT source' is missing a period and should read 'false CT sources'; more generally, the caption would benefit from stating the number of false CT sources shown.
  4. [Sec. 3.1, footnote 3] The statement that 'the CCLS notes that there is significant incompleteness below 20 counts' should include a specific reference (e.g., Civano et al. 2016, with section or figure) so the reader can verify the incompleteness claim.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the paper is a forward-model sensitivity analysis whose headline percentages are conditional statements about simulated BASS AGN, not conclusions assumed in the inputs.

full rationale

The central derivation is a self-contained forward model: known BASS X-ray spectral models are redshifted, passed through Chandra response files, and refit with the M16 pipeline, and recovery is measured against the known simulation inputs. The headline claim that Chandra would miss 53.3% of obscured sources is explicitly conditional on the simulated BASS AGN population placed at CCLS redshifts, luminosities, and exposure times; it is not a claim that the CCLS intrinsic NH distribution has been measured or predicted. The false-CT fraction (66.7%; 18/27) is a posterior over simulation truth under the stated BASS NH prior, and the paper explicitly declines to match the CCLS NH distribution (Appendix C), so the statistic is presented as conditional rather than as an unconditional CCLS property. The paper also openly acknowledges the no-evolution assumption in Sec. 4.1 ('does not account for any potential change to the intrinsic population of AGN over cosmic time'), which is a stated limitation, not a hidden circular reduction. Self-citations to BASS/R17 and Koss et al. (2016) supply external template spectra and the hard-X-ray unbiased-selection rationale; these are independent data and evidence, not conclusions derived from the present paper's own outputs. No equation or fitting step reduces the reported detection or recovery fractions to the inputs by construction.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The paper introduces no new free parameters in the sense of fitting data; the quantities listed are model choices and thresholds taken from the literature that affect the central false CT result. The dominating assumption is that local BASS AGN templates are representative of CCLS AGN at matched luminosity, with no cosmic evolution.

free parameters (3)
  • constant.factor upper limit = 0.15
    Relative normalization of the secondary power law in 2PL fits, fixed to <0.15 following M16. This choice strongly affects the number of false CT sources (Sec 4.2).
  • constant.factor lower limit = 0.001
    Lower limit from Gupta et al. 2021, set to avoid over-indexing on high-energy background bins. Affects the 2PL fit selection.
  • Delta C improvement threshold = 2.71
    Criterion for accepting a more complex model, following M16. Sec 4.2 shows that raising it to 6.0 or 9.0 reduces false CT from 11 to 4 to 1.
assumptions (4)
  • domain assumption Local BASS AGN are representative analogs for high-redshift CCLS AGN at matched intrinsic 2-10 keV luminosity, both in spectral shape and in the intrinsic N_H-luminosity relation.
    The entire simulation and the conclusion that obscured fraction trends are overestimated rely on this. Stated in Sec 2.2 and Sec 4.1: methodology does not account for cosmic evolution.
  • domain assumption The X-ray spectral models of R17 (absorbed power law, pexrav reflection, soft excess, Fe line) are correct descriptions of the intrinsic AGN emission.
    Used as 'truth' in simulations; e.g., Appendix B. If these phenomenological models are wrong, the simulated biases differ.
  • domain assumption The Chandra response files and Poisson statistics used in fakeit accurately represent CCLS observations, including the effect of vignetting and off-axis positions.
    Sec 2.2 and Appendix D. The background is ignored, and the effect is argued to be small for >=30 cts.
  • domain assumption The CCLS source catalog (M16) and the CCLS* subset with well-constrained luminosities are unbiased samples of the X-ray detected population.
    Used to define luminosity, redshift, and exposure distributions; the selection function below 30 counts is matched to CCLS.

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Cite this review

Pith. "Pith review of BASS XLV: Quantifying AGN Selection Effects in the Chandra COSMOS-Legacy Survey with BASS." pith.science (2026). https://pith.science/paper/YWCZXBIJ

@misc{pith2026250116708,
  author       = {Pith},
  title        = {Pith review of: BASS XLV: Quantifying AGN Selection Effects in the Chandra COSMOS-Legacy Survey with BASS},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/YWCZXBIJ}},
  note         = {Machine review of arXiv:2501.16708}
}
abstract

Deep extragalactic X-ray surveys, such as the Chandra COSMOS-Legacy field (CCLS), are prone to be biased against active galactic nuclei (AGN) with high column densities due to their lower count rates at a given luminosity. To quantify this selection effect, we forward model nearby ($z\sim0.05$) AGN from the BAT AGN Spectroscopic Survey (BASS) with well-characterized ($\gtrsim$1000 cts) broadband X-ray spectra (0.5-195 keV) to simulate the CCLS absorption distribution. We utilize the BASS low-redshift analogs with similar luminosities to the CCLS ($L_\mathrm{2-10\ keV}^\mathrm{int}\sim10^{42-45}\ \mathrm{erg}\ \mathrm{s}^{-1}$), which are much less affected by obscuration and low-count statistics, as the seed for our simulations, and follow the spectral fitting of the CCLS. Our simulations reveal that Chandra would fail to detect the majority (53.3%; 563/1056) of obscured ($N_\mathrm{H}>10^{22}\ \mathrm{cm}^{-2}$) simulated BASS AGN given the observed redshift and luminosity distribution of the CCLS. Even for detected sources with sufficient counts ($\geq30$) for spectral modeling, the level of obscuration is significantly overestimated. This bias is most extreme for objects whose best fit indicates a high-column density AGN ($N_\mathrm{H}\geq10^{24}\ \mathrm{cm}^{-2}$), since the majority (66.7%; 18/27) of these are actually unobscured sources ($N_\mathrm{H}<10^{22}\ \mathrm{cm}^{-2}$). This implies that previous studies may have significantly overestimated the increase in the obscured fraction with redshift and the fraction of luminous obscured AGN. Our findings highlight the importance of directly considering obscuration biases and forward modeling in X-ray surveys, as well as the need for higher-sensitivity X-ray missions such as the Advanced X-ray Imaging Satellite (AXIS), and the importance of multi-wavelength indicators to estimate obscuration in distant supermassive black holes.

Figures

Figures reproduced from arXiv: 2501.16708 by the authors.

Figure 1
Figure 1. Intrinsic 2-10 keV X-ray luminosity versus [PITH_FULL_IMAGE:figures/full_fig_p004_1.png] view at source ↗
Figure 2
Figure 2. (a) Luminosity matching. The intrinsic X-ray luminosity distributions of the CCLS* catalog (blue) and the selected template models (black). (b) Redshift matching. Comparison of the CCLS* catalog (blue) and the simulated data set (black) in luminosity-redshift space. 95% of the sources lie within the solid curves and 68% of the sources lie within the dashed curves. (c) Exposure time matching. Distribution of the expo… view at source ↗
Figure 3
Figure 3. Demonstration of the importance of ob￾scuration on the detection and count fraction as a function of redshift using nearby BAT AGN. We com￾pare three pairs of BASS models, each with an unobscured model (light blue) and a CT model (dark blue). The three pairs are at log L2−10,int = 43.15 (dotted), 43.71 (dashed), and 44.20 (solid), which correspond to the first, second, and third quartiles of the CCLS* luminosity dis… view at source ↗
Figures from the paper (17 more)
Figure 4
Figure 4. Figure 4: Net counts of simulated spectra. (a) Comparison with CCLS*. [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]
Figure 5
Figure 5. Figure 5: Chandra detection fractions of the simulated dataset [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]
Figure 6
Figure 6. Figure 6: Error histograms. For each of Γ and log NH, we calculate the error of the measured value relative to the simulated value. (a) Accuracy of measured Γ. The vertical red dashed line indicates the median ∆Γ at +0.03. 68% of the spectra lie between the black dashed lines at…
Figure 7
Figure 7. Figure 7: CT overestimation. We investigate the accuracy of the best-fit NH estimations and the 90% confidence intervals. (a) Accuracy of best-fit NH measurements. In bins of best-fit measured NH, we show the proportion of fits that are accurate to different dex values. Among th…
Figure 8
Figure 8. Figure 8: Properties of 2PL fits. For each of our 60 2PL fits, we show the relative normalization of the secondary power law versus the net counts in the 0.5–7.0 keV band. 2PL fits that measured NH accurately to within 1 dex are shown in black circles, while those that were inac…
Figure 9
Figure 9. Figure 9: Implications for the evolution of the obscured fraction. [PITH_FULL_IMAGE:figures/full_fig_p014_9.png]
Figure 10
Figure 10. Figure 10: AXIS detections of the simulated dataset [PITH_FULL_IMAGE:figures/full_fig_p017_10.png]
Figure 11
Figure 11. Figure 11: Effective area of Chandra ACIS-I and AXIS as a function of redshift. [PITH_FULL_IMAGE:figures/full_fig_p019_11.png]
Figure 12
Figure 12. Figure 12: Counts distribution of BASS models used. Photons below 10 keV from which the BASS mod￾els were built are from combined ASCA, Chandra, Suzaku, Swift/XRT, and XMM-Newton data. The blue (black) his￾togram represents the spectra used to build the unobscured (obscured) BAS…
Figure 13
Figure 13. Figure 13: NH distributions of the simulated sample and CCLS. The NH distribution of the BASS models se￾lected for simulations (black) is compared with that of the CCLS (blue). The latter NH distribution was obtained as described in the text. We also include the entire sample of…
Figure 14
Figure 14. Figure 14: Fitting statistics for the simulated data [PITH_FULL_IMAGE:figures/full_fig_p021_14.png]
Figure 15
Figure 15. Figure 15: Double power law model. A typical 2PL model with an obscured primary component (red) and an unobscured secondary component with an identical slope (blue). Here the normalization of the secondary component is chosen to be 5% of the primary normalization. The main featu…
Figure 16
Figure 16. Figure 16: Double power laws at varying redshift and [PITH_FULL_IMAGE:figures/full_fig_p023_16.png]
Figure 17
Figure 17. Figure 17: Primary ratio as a function of redshift and [PITH_FULL_IMAGE:figures/full_fig_p023_17.png]
Figure 19
Figure 19. Figure 19: NH accuracy of the double power law fits using the obscuration criterion. A comparison of the ∆NH histograms for the 2PL fits in the original procedure (black) and when only attempting the 2PL model for sources with the additional criterion that the single power law e…
Figure 21
Figure 21. Figure 21: False CT example 2: out of band pass, few photons. See Appendix G for an explanation of the panels and Sec. G.2 for a discussion of this example. pass allows for a range of NH approximately 2 dex wide at each redshift for the which the 2PL is appropriate, as illustrat…
Figure 23
Figure 23. Figure 23: False CT example 4: low secondary nor￾malization. See Appendix G for an explanation of the pan￾els and Sec. G.4 for a discussion of this example. to have log NH,meas > 20. Note that log NH ≤ 20 is consistent with zero. Top: The 2PL fit settled on by the fitting algori…

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