CANUCS/Technicolor Data Release 1: Imaging, Photometry, Slit Spectroscopy, and Stellar Population Parameters

Adam Muzzin; Anishya Harshan; Ben Forrest; Camilla Pacifici; Chris J. Willott; Danilo Marchesini; Gabriel Brammer; Ga\"el Noirot; Gaia Gaspar; Ghassan T. E. Sarrouh

REVIEW 3 major objections 6 minor 1 cited by

CANUCS/Technicolor Data Release 1: Imaging, Photometry, Slit Spectroscopy, and Stellar Population Parameters

T0 review · 3 major / 6 minor · reviewed 2026-08-06 · deepseek-v4-flash

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

Pith's one-line read CANUCS/Technicolor DR1 releases photometry, redshifts, and stellar population parameters for about 121,000 galaxies across five JWST lensing-cluster fields, with photometric redshift scatter of 0.01-0.03 and 4-7% catastrophic outliers.

desk verdict A strong, honest data release that deserves peer review; just don't mistake the photo-z metrics for full-catalog error rates. read the letter →

arxiv 2506.21685 v1 pith:B7TG4HUS submitted 2025-06-26 astro-ph.GA

Ghassan T. E. Sarrouh , Yoshihisa Asada , Nicholas S. Martis , Chris J. Willott , Kartheik G. Iyer , Gaël Noirot , Adam Muzzin , Marcin Sawicki

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Gabriel Brammer Guillaume Desprez Gregor Rihtaršič Johannes Zabl Roberto Abraham Maruša Bradač René Doyon Jacqueline Antwi-Danso Samantha Berek Westley Brown Vince Estrada-Carpenter Jeremy Favaro Giordano Felicioni Ben Forrest Gaia Gaspar Katriona M. L. Gould Rachel Gledhill Anishya Harshan Nusrath Jahan Naadiyah Jagga Jon Judež Danilo Marchesini Vladan Markov Jasleen Matharu Shannon MacFarland Maya Merchant Rosa M. Mérida Lamiya Mowla Katherine Myers Kiyoaki C. Omori Camilla Pacifici Swara Ravindranath Luke Robbins Victoria Strait Visal Sok Vivian Yun Yan Tan Roberta Tripodi Gillian Wilson Sunna Withers

This is my paper

classification astro-ph.GA

keywords galaxysurveysphotometricredshiftsJWSTNIRCamNIRISSimagingNIRSpecspectroscopystronglensingclustersspectralenergydistributionfittingstellarpopulationparameters

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

This paper presents the first public data release of the CANUCS cluster survey and its Technicolor follow-up, built from JWST NIRCam and NIRISS imaging plus NIRSpec prism spectroscopy of five massive lensing clusters and their flanking fields. The authors aim to establish that the released catalogs are reliable enough to serve as community-standard inputs for galaxy evolution science: roughly 121,000 detected sources come with PSF-matched photometry, photometric redshifts, and, for a large subset, stellar population parameters, validated against 1,960 spectroscopic redshifts. Against those spectra the photometric redshifts show scatter $\sigma_{\rm NMAD} = 0.01$--$0.03$ and catastrophic outlier fractions of only 4--7%, with the flanking fields' medium-band filter sets performing noticeably better than the cluster fields' wide-plus-NIRISS filter set. A sympathetic reader would care because the combination of lensing magnification, dense wavelength sampling, and public data products makes this one of the more directly usable JWST datasets for studying how galaxies form and evolve out to $z \sim 11$.

What carries the argument

The load-bearing machinery is the PSF homogenization and color construction. Empirical PSFs, built from median-stacked stars in the central arcsecond and from a model PSF in the outer wings, are used to convolve every filter image to the F444W PSF; total fluxes are then reported either as direct Kron photometry or as COLOR03/COLOR07 fluxes that scale every filter's 0.3" or 0.7" aperture flux by the ratio of that aperture to the F277W Kron flux of the source. This single scale factor makes every color as deep as the small-aperture measurement while preserving a total-flux normalization, and it is what allows up to 29 filters to be combined consistently. A secondary mechanism is the $\chi$-mean detection image with a cold-plus-hot source detection strategy, and the EAzY fitting with a modified template set, which together set the catalog's depth and redshift accuracy.

What would settle it

Measure F277W and F444W fluxes for resolved galaxies (half-light radius > 0.5") separately in a 0.3" aperture and in the Kron aperture, and check whether the small-aperture-to-Kron ratio differs between the two filters by more than the photometric errors; a systematic ratio difference would directly falsify the uniform-color assumption behind the COLOR03/COLOR07 fluxes and require re-fitting photo-zs with per-filter aperture corrections.

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Extended reading notes

Core claim

The central claim is that a carefully homogenized multi-filter dataset, with every filter's image convolved to the F444W point-spread function and colors defined by small-aperture flux ratios scaled to the F277W Kron flux, produces photometric redshifts and stellar population parameters accurate enough for quantitative science. The evidence is the comparison against 1,960 literature and NIRSpec spectroscopic redshifts: catastrophic outlier fractions of 4% (flanking fields) to 7% (cluster fields) at $|\Delta z| > 0.15$ and scatter $\sigma_{\rm NMAD} = 0.01$--$0.03$. The paper also claims that a custom EAzY template set, with [O III] lines boosted to match JWST-observed extreme emission-line galaxies, removes a known failure mode in which photometric redshift solutions place H$\alpha$ at a filter edge to mimic an anomalously high [O III]/H$\alpha$ ratio.

Load-bearing premise

The catalog assumes a galaxy's color is the same in a small 0.3 or 0.7 arcsecond aperture as across its whole Kron aperture, since every filter's total flux is scaled by the F277W aperture-to-Kron ratio, so galaxies with strong color gradients would get systematically biased colors, redshifts, and stellar masses.

Editorial extensions

If this is right

Users can select galaxies by photometric redshift with known field-dependent accuracy: $\sigma_{\rm NMAD} \sim 0.03$ and 7% outliers in cluster fields, and $\sigma_{\rm NMAD} \sim 0.01$ and 4% outliers in flanking fields.
Stellar masses, star formation rates, dust, and metallicities from two independent SED codes are provided for roughly 53,000 cluster-field and 44,000 flanking-field galaxies, with disagreement flags that identify sources whose properties depend on fitting assumptions.
Gravitational lensing magnifications from new cluster models are attached to every catalog source, so the photometry can be used directly for source-plane studies of faint high-redshift galaxies.
The 747 NIRSpec prism redshifts, mostly based on multiple emission lines and extending to $z = 10.8$, anchor the photometric redshift calibration at faint magnitudes where ground-based spectroscopy cannot reach.
The bright-cluster-galaxy and intracluster-light subtraction enlarges the clean area near cluster cores, allowing faint background sources to be measured where cluster light would otherwise dominate.

Reading between the lines

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

The uniform-color assumption behind the COLOR03/COLOR07 fluxes implies that resolved galaxies with strong radial color gradients will have slightly biased total colors; users studying bulge/disk systems or spatially resolved galaxies should test the released colors against the per-filter Kron photometry.
A direct empirical test would compare F277W--F444W colors measured in the 0.3" and 3.0" apertures for resolved sources; the size of the systematic offset would quantify the impact on photometric redshifts and stellar masses.
The flanking fields' superior redshift accuracy ($\sigma_{\rm NMAD} = 0.01$, 4% outliers) is an argument that dense medium-band wavelength coverage, not just depth, drives photometric redshift precision in the JWST era; future photo-z-only survey designs may want to prioritize medium-band filters.
Once the NIRISS wide-field slitless spectra are released, the same PSF-matched photometry and boosted-[O III] template set could be reused to verify redshifts and line fluxes consistently across the cluster fields.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

Best to read this as a data release paper, not a new-physics paper. That's fine: the products are genuinely new and valuable. DR1 delivers NIRCam imaging in up to 29 filters over ~30 arcmin2, NIRSpec prism spectra and redshifts, photometric catalogs for ~121,000 sources, SED-derived physical properties from two codes, and lens models for five clusters. The paper does what a good DR paper should: documents the reduction, bCG/ICL subtraction, PSF matching, photometry, and photo-z/SED fitting, then validates the catalog with number counts, depth maps, and spec-z comparisons. The modified EAzY templates to handle extreme [OIII] emitters are a sensible incremental improvement.

The main caveat is the headline photo-z accuracy. The 4-7% outlier rates and sigma_NMAD 0.01-0.03 are measured only on the ~2,000 sources with spectroscopic redshifts, and that sample is heavily weighted toward cluster cores and special targets (z>7.5 candidates, strong EELGs, quiescent/dusty galaxies). The authors acknowledge this in §5.1, but the abstract still presents the numbers without that context. Users should read them as validation-sample metrics, not full-catalog error rates. Full-catalog accuracy for faint or red sources is likely somewhat worse, though probably not dramatically so.

Two other soft spots, both minor. The COLOR03/COLOR07 total fluxes assume spatially uniform color within the aperture; that's a standard approximation, but color gradients would cause systematic offsets in colors and SED fits. And the 1% flux error assumed for bCGs is a guess, not a measurement. Also, the reduction pipeline code isn't fully released, so exact reproducibility is limited, though the products themselves are public.

None of this undermines the release. The validation that exists is solid: number counts agree with HFF, photo-z scatter is comparable to other JWST catalogs, and the BAGPIPES/DENSEBASIS comparison gives users a handle on systematic uncertainties. This is exactly the kind of paper that deserves a serious referee and publication. I'd bring it to reading group to discuss what makes a data release usable, and I'd cite it if I worked in cluster lensing or high-z galaxy studies.

Referee Report

3 major / 6 minor

Summary. This paper presents the first data release of the CANUCS and JWST-in-Technicolor programs: NIRCam and NIRISS imaging of five lensing clusters and their flanking fields, supplemented by HST archival imaging, together with NIRSpec prism spectroscopy. The authors describe their reduction pipeline, bright-cluster-galaxy and intra-cluster-light subtraction, PSF construction and matching, source detection, and aperture/total photometry, and release catalogs for 121,261 sources. They also release photometric and spectroscopic redshifts, NIRSpec spectra, lens models, and stellar population parameters from BAGPIPES and DENSE BASIS. The headline validation claim is that photometric redshifts have 4-7% catastrophic outlier fractions and sigma_NMAD of 0.01-0.03 when compared with spectroscopic redshifts.

Significance. If the released products are as robust as the validation suggests, this is a valuable community resource. The combination of medium-band NIRCam filters over ~30 arcmin^2 with cluster-lensing depth is unique, and the paper includes several genuinely useful independent checks: empirical PSF convolution tests, depth maps from empty-aperture noise, galaxy number counts, and the spectroscopically unbiased photo-z distribution in Figure 17. The public release infrastructure (DOIs, HLSP products, demonstration notebooks, a detailed flag system) is a clear strength. The principal caveat is that the quantitative photo-z accuracy claim is measured on a spectroscopically selected subset and is not yet demonstrated for the full catalog; this is a presentation and framing issue that can be fixed without changing the underlying products.

major comments (3)

[Abstract and §5.1] The headline accuracy metrics (4-7% catastrophic outliers and sigma_NMAD = 0.01-0.03) are computed only for the 1,960 sources with spectroscopic redshifts that also have all NIRCam filters, not for the full 121,261-source catalog. This validation sample is strongly selected: the CANUCS NIRSpec targets were prioritized for z>7.5 candidates, emission-line excess galaxies, quiescent/dusty galaxies, strongly lensed sources, red/blue F277W-F356W excesses, and z_phot>3 sources (§2.3), and the ancillary spec-z are concentrated in CLU cluster cores (1,769 of 1,960 in CLU versus only 191 in NCF). Because targets were partly chosen using photometric redshifts and colors, the comparison is not an independent test of photo-z performance on typical catalog sources. The closing caveat in §5.1 ('could be somewhat biased by the target selection') is important and should be moved into the Abstract and presented as a limitation of the headline numbers, not as a side remark. I recommend reporting metrics separately for an unbiased subset (e.g., a flux-limited sample selected only by F277W S/N and USE_PHOT) or clearly labeling the current values as validation-sample-specific.
[§4.4] The bCG total fluxes are assigned a fixed 1% flux error for all filters and all three total-flux measurements, with no empirical justification or test. Given that the bCG models involve isophote fitting, PSF deconvolution, ten iterations, and manual adjustments (§3.2), a 1% floor is likely optimistic and understates the covariance between bCG model uncertainty and nearby-source photometry. Please provide a validation (e.g., scatter of the iterated models, comparison to independent photometry, or residual-based estimates) or, failing that, state explicitly that 1% is an assumed systematic floor rather than a measured uncertainty.
[§5.2, Figure 18] The F150W number counts show an unexplained offset at intermediate magnitudes (roughly m = 24-26) relative to Shipley et al. (2018). The text lists three possible causes (PSF matching, Kron parameters from the detection image, zero-point offsets) but does not discriminate among them. Since number counts are used as a catalog-level validation, an unresolved intermediate-magnitude offset leaves open a possible systematic issue in total flux photometry. Please either resolve the offset with a controlled test (e.g., recomputing counts with a common detection image and Kron definition, or comparing F150W directly to F160W) or explicitly downgrade this diagnostic's status in the validation chain.

minor comments (6)

[Figure 13 caption] The figure caption contains the internal editing note 'To reconsider: F160W might be better to show instead of F606W.' This should be removed or resolved before publication.
[§2.2] The list of Cycle 1 NCF medium-band filters omits F182M, which appears in Table 2 and is needed to reach the stated '9 medium band filters'; please correct the list.
[§6.3 and Figure 20 caption] The text attributes the star-forming main sequence to Speagle et al. (2014), while the Figure 20 caption attributes it to Iyer et al. (2018); please reconcile the reference.
[Abstract and §3.3] The abstract quotes 733 NIRSpec spectra while §3.3 reports 747 NIRSpec redshift measurements (733 with z>0 plus 14 stars); please harmonize these numbers.
[§4.3 and Figure 12] The text refers to '0.′′3 and 0.′′7 diameter apertures' while the Figure 12 caption labels the same quantities as '0.′′15 and 0.′′35 radial apertures'; please use consistent aperture notation.
[§4.2] The PSF construction combines an empirical core with WebbPSF outer regions; the description would benefit from stating how many stars enter each filter's PSF and how the 1% outlier rejection affects the final PSF uncertainty, since Figure 12 already shows roughly 1% residuals at the photometry apertures.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: photo-z validation uses independent spectroscopic redshifts and explicitly removes photo-assisted single-line redshifts.

full rationale

The paper's central claims are empirical validation metrics for a public data release, not first-principles derivations. The photo-z accuracy numbers in the Abstract and Section 5.1 are obtained by comparing EAzY template fits to independent spectroscopic redshifts: 747 CANUCS-NIRSpec redshifts (mostly multi-line, Z_Q_REF=1 or break-based Z_Q_REF=3) plus ancillary literature redshifts from MUSE, HST/grism, CLASH, GLASS, and Keck/MOSFIRE. The paper explicitly guards against the main circular channel: 'In this comparison, we remove our NIRSpec zspec that are based on single emission line with the aid of photometry (see Sec. 3.3) to avoid circular reasoning' (Section 5.1). The custom photo-z templates are described as modifications of external Larson et al. (2023) templates, with a boosted [OIII] component and a line-free component; these are modeling choices motivated by known JWST populations, not parameters fitted to the validation sample. The overlapping-author citations that appear in the photo-z methodology (e.g., the Asada et al. 2025 IGM/CGM attenuation prescription and Willott et al. 2024) are substantive model inputs or literature context, and the validation metrics do not reduce to those citations by construction. The paper also includes an explicit caveat that the spec-z comparison 'could be somewhat biased by the target selection of spectroscopic observations' (end of Section 5.1); that is a representativeness limitation, not circular reasoning. Independent external checks are present, including galaxy number counts compared with Shipley et al. (2018) and UNCOVER/Weaver et al. (2023). No equation, fitted parameter, or defined quantity is renamed as a prediction, and no uniqueness theorem is imported from the authors' prior work to force a choice. The derivation chain is self-contained against external benchmarks, so the appropriate finding is no significant circularity.

Assumptions & free parameters 4 free parameters · 5 assumptions · 0 invented entities

The central products rest on standard photometric and SED-fitting assumptions; no new physical entities are introduced. The most load-bearing hand-set parameters are the [OIII] template boost and the assumed 1% bCG flux errors.

free parameters (4)

[OIII] emission line boost factor = 3
Section 4.6: 'boosting the [OIII]4959,5007 lines with a factor of 3' to reproduce extreme emitters; affects photo-z fits.
bCG total flux error = 1%
Section 4.4: 'We assume 1 % flux errors on the bCG total fluxes', chosen by hand without empirical justification.
Emission line FWHM = 200 km/s
Section 4.6: 'assign reasonable emission line width of FWHM = 200 km/s' in the modified template.
Photometric error added in quadrature = 5%
Section 4.6: 'add 5 % of the flux in each filter to the error budget in quadrature as the systematic uncertainty'.

assumptions (5)

domain assumption The galaxy SED templates (Larson et al. set 3 plus modified spectra) adequately represent the true SEDs of galaxies at all redshifts in the sample.
Invoked in Section 4.6 for photo-z fitting; if templates miss key spectral features, photo-z and derived properties are biased.
domain assumption The PSF is spatially invariant within each field/filter and well described by the empirical+WebbPSF hybrid.
Section 4.2 constructs a single PSF per filter and uses it for all sources; field-dependent PSF variations would bias photometry.
domain assumption The COLOR03/COLOR07 total flux scaling assumes no color gradients within the photometric aperture.
Section 4.3 scales all filter fluxes by the F277W aperture-to-Kron ratio; strong color gradients would break this assumption.
domain assumption The bCG and ICL subtraction removes all cluster light without removing or biasing background source flux.
Section 3.2 describes the isophote modeling and subtraction; residuals would contaminate photometry near cluster centers.
domain assumption The chi-mean detection image provides a complete and unbiased source catalog.
Section 4.1 uses the chi-mean co-add for source detection; the detection completeness depends on this assumption.

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Cite this review

Pith. "Pith review of CANUCS/Technicolor Data Release 1: Imaging, Photometry, Slit Spectroscopy, and Stellar Population Parameters." pith.science (2026). https://pith.science/paper/B7TG4HUS

@misc{pith2026250621685,
  author       = {Pith},
  title        = {Pith review of: CANUCS/Technicolor Data Release 1: Imaging, Photometry, Slit Spectroscopy, and Stellar Population Parameters},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/B7TG4HUS}},
  note         = {Machine review of arXiv:2506.21685}
}

abstract

We present the first data release of the CAnadian NIRISS Unbiased Cluster Survey (CANUCS), a JWST Cycle 1 GTO program targeting 5 lensing clusters and flanking fields in parallel (Abell 370, MACS0416, MACS0417, MACS1149, MACS1423; survey area \tilda100 arcmin$^{2}$), with NIRCam imaging, NIRISS slitless spectroscopy, and NIRSpec prism multi-object spectroscopy. Fields centered on cluster cores include imaging in 8 bands from 0.9-4.4$\mu$m, alongside continuous NIRISS coverage from 1.15-2$\mu$m, while the NIRCam flanking fields provide 5 wide and 9 medium band filters for exceptional spectral sampling, all to \tilda29 mag$_{AB}$. We also present JWST in Technicolor, a Cycle 2 follow-up GO program targeting 3 CANUCS clusters (Abell 370, MACS0416, MACS1149). The Technicolor program adds NIRISS slitless spectroscopy in F090W to the cluster fields while adding 8 wide, medium, and narrow band filters to the flanking fields. This provides NIRCam imaging in all wide and medium band filters over \tilda30 arcmin$^{2}$. This paper describes our data reduction and photometry methodology. We release NIRCam, NIRISS, and HST imaging, PSFs, PSF-matched imaging, photometric catalogs, and photometric and spectroscopic redshifts. We provide lens models and stellar population parameters in up to 19 filters for \tilda53,000 galaxies in the cluster fields, and \tilda44,000 galaxies in up to 29 filters in the flanking fields. We further present 733 NIRSpec spectra and redshift measurements up to $z=10.8$. Comparing against our photometric redshifts, we find catastrophic outlier rates of only 4-7\% and scatter of $\sigma_{\rm NMAD}$ of 0.01-0.03.

Figures

Figures reproduced from arXiv: 2506.21685 by the authors.

**Figure 1.** Spectral features as a function of redshift. Colored bars represent NIRCam photometric filters, and span the range of redshifts where the observed wavelength of the spectral feature falls within the bandwidth of the filter. or “mini-quenched” galaxies (e.g. Strait et al. 2023; Looser et al. 2024). Most early JWST imaging surveys focused on wide band photometry, with limited exceptions (JADES ORIGINS, Eisenstein et a… view at source ↗

**Figure 2.** Layouts for the CANUCS observations in the five cluster target fields. For each cluster, the background greyscale images are HST optical F606W (F606W and F606WU combined for MACS0417 and MACS1423). NIRCam and NIRISS coverage when prime are shaded different colors from when the parallel instrument. This shows how the set of two coordinated parallel observations lead to three fields per cluster; the central cluster fi… view at source ↗

**Figure 3.** Image depths and available filter coverage of the CANUCS observations supplemented with archival HST images. The MACS0416 CLU and NCF fields are shown as the example, but depths and filter sets are almost identical across the five pointings for CLU fields, but some filters in this plot are missing in some NCF fields (see [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗

Figures from the paper (27 more)

**Figure 4.** Figure 4: RGB image of the center of Abell 370 CLU module B using NIRCam F090W, F115W, and F150W for the blue channel, F200W, F277W, and F356W for the green one, and F356W, F410M, and F444W for the red. North is up and East is toward the left. F356W+F410M+F444W for the red. Colo…

**Figure 5.** Figure 5: RGB image of MACS1149 NCF in NIRCam F115W, F200W+F277W, and F444W. Example RGB cutouts highlighting the mediumband excesses (left, F430M-F460M-F480M) and SEDs (below) are shown for select emission line galaxies at 5 < z < 9. SEDs for two sources show individual cutout…

**Figure 6.** Figure 6: Example SEDs in the CANUCS fields in up to 29 filters from HST and JWST. Horizontal errorbars denote filter width, with transmission curves displayed below each SED. The four galaxies on the left are in CLU fields and the four on the right are in NCF/Technicolor fields…

**Figure 7.** Figure 7: Original, drizzled science image (left), bCGs model (center), and residual image (right) for each indicated filter for the Abell 370 cluster. The filters shown highlight a range of instruments: F814W (HST/ACS), F125W (HST/WFC3/IR), F150WN (NIRISS), F200W (NIRCam SW), a…

**Figure 8.** Figure 8: Color images constructed from F277W, F356W, and F444W for the central region of each cluster. As in [PITH_FULL_IMAGE:figures/full_fig_p015_8.png]

**Figure 9.** Figure 9 [PITH_FULL_IMAGE:figures/full_fig_p016_9.png]

**Figure 10.** Figure 10: A demonstration of the PSF construction method. Empirical PSFs are measured in each filter and then combined with the WebbPSF model.; Left: Empirical PSF constructed from median stacking bright stars. The central 1′′of the empirical PSF is used in the final PSF (insid…

**Figure 11.** Figure 11: 4 ′′x4′′PSF cutouts for F200W NIRCam (top left) and NIRISS (top right). The aperture which encloses 80% of the total energy is marked with a red dashed circle, and a 1D slice of the PSF profile along the horizontal axis is shown below. 4′′x4′′PSF cutouts for all filte…

**Figure 12.** Figure 12: Top: PSF encircled energies relative to the F444W PSF for MACS0416 CLU (left) and NCF (right), pre-convolution; Middle: Convolved PSFs relative to the target F444W PSF; Bottom: Convolved empirical stars, measured relative to empirical stars in F444W. In all panels the…

**Figure 13.** Figure 13: Flux error measurements in the MACS0416 CLU field for the JWST/NIRCam F277W image (top) and HST/ACS F606W image (bottom). We perform 2000 random empty aperture photometry measurements in the noise-normalized image with each fixed aperture size, and measure the RMS of …

**Figure 14.** Figure 14: Point source identification in the F150W size-magnitude diagram. Black points represent all photometric sources in our catalogs, and red points are the point sources identified with the criteria described in Section 4.5.1 (red dashed lines). Blue stars are those iden…

**Figure 15.** Figure 15: Centre-left panel: F444W AB magnitude (0. ′′3 aperture) versus redshift for all sources in the CANUCS catalog with F444W S/N> 4. Sources with spectroscopic redshifts are shown with colored circles: orange for CANUCS-NIRSpec redshifts and blue for ancillary. Sources wi…

**Figure 16.** Figure 16: Comparisons of photometric redshifts and zspec in CLU fields (left) and NCF fields (right). Orange points represent the zspec measurements based on our NIRSpec observations, while blue points denote those from literature. Note that our NIRSpec observation targets only…

**Figure 17.** Figure 17: Photometric redshift distributions in all 10 fields. Only S/N > 10 sources in the NIRCam F277W filter are used. The vertical dashed lines represent the cluster redshifts in each galaxy cluster field. which are defined as the number fraction of objects with |∆z| = |zs…

**Figure 18.** Figure 18: The galaxy number counts in the MACS1149 CLU field as a function of the KRON total magnitudes in NIRCam F150W, F356W, and F444W filters. Error bars correspond to 1σ Poisson uncertainties. The black open squares are the number count measurements in the same field from …

**Figure 19.** Figure 19: Top: Stellar mass versus redshift for the full CANUCS sample using the BAGPIPES SED fits. Galaxies in the CLU (left) and NCF (right) fields are shown separately. The color bar indicates the specific SFR (sSFR). Bottom: As above using the DENSEBASIS SED fits. 5.3. Dept…

**Figure 20.** Figure 20: Top: Star formation rate versus stellar mass relation for the full CANUCS sample using the DENSEBASIS SED fits. Galaxies in the CLU (left) and NCF (right) fields are shown separately. The color bar indicates redshift. The star-forming main sequence derived in Iyer et …

**Figure 21.** Figure 21: SED fitting results for the same selection of galaxies in [PITH_FULL_IMAGE:figures/full_fig_p032_21.png]

**Figure 22.** Figure 22: Comparisons of the inferred stellar mass (top) and star formation rate averaged over 100 Myr (bottom) from BAGPIPES and DENSEBASIS SED fits. The left and right columns show galaxies in the CLU and NCF fields respectively. The solid black line shows the 1:1 line and da…

**Figure 23.** Figure 23: UVJ (left) and ugi (right) 2D histograms showing source density in each respective rest-frame color space. Crosses show the 1-σ uncertainty on rest-frame colors for quiescent (upper left, inside of the brown wedge) and star-forming galaxies (outside of wedge), respect…

**Figure 24.** Figure 24: RGB image of the center of MACS0416 CLU North is up and East is toward the left [PITH_FULL_IMAGE:figures/full_fig_p038_24.png]

**Figure 25.** Figure 25: RGB image of the center of MACS1149 CLU North is up and East is toward the left [PITH_FULL_IMAGE:figures/full_fig_p039_25.png]

**Figure 26.** Figure 26: RGB image of the center of MACS0417 CLU North is up and East is toward the left [PITH_FULL_IMAGE:figures/full_fig_p040_26.png]

**Figure 27.** Figure 27: RGB image of the center of MACS1423 CLU North is up and East is toward the left [PITH_FULL_IMAGE:figures/full_fig_p041_27.png]

**Figure 28.** Figure 28: Same as [PITH_FULL_IMAGE:figures/full_fig_p045_28.png]

**Figure 29.** Figure 29: Same as [PITH_FULL_IMAGE:figures/full_fig_p046_29.png]

**Figure 30.** Figure 30: Same as [PITH_FULL_IMAGE:figures/full_fig_p047_30.png]

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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. Full citation record

MINERVA: A NIRCam Medium Band and MIRI Imaging Survey to Unlock the Hidden Gems of the Distant Universe
astro-ph.GA 2025-07 conditional novelty 6.0 of 10

The MINERVA survey will expand JWST medium-band imaging area by about 7 times and is forecast to cut photometric-redshift scatter and outlier fractions by factors of 3.7 and 2.6.

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Pith tools

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