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A SPectroscopic survey of biased halos In the Reionization Era (ASPIRE): Spectroscopically Complete Census of Obscured Cosmic Star Formation Rate Density at $z=4-6$

T0 review · 3 major / 5 minor · reviewed 2026-08-11 · deepseek-v4-flash

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Pith's one-line read Using 25 independent quasar sightlines, the ASPIRE program measures the dust-obscured star-formation rate density at $z=4$–$6$ and finds that $66\pm7\%$ of cosmic star formation at $z\sim5$ is hidden by dust.

desk verdict First spectroscopically complete IR luminosity function at z=4-6: small sample, careful completeness, real result, but the absolute SFRD scale still leans on one ALMA band and a dust-temperature prior. read the letter →

arxiv 2412.06894 v1 pith:SYA5E3W6 submitted 2024-12-09 astro-ph.GA

Fengwu Sun , Feige Wang , Jinyi Yang , Jaclyn B. Champagne , Roberto Decarli , Xiaohui Fan , Eduardo Bañados , Zheng Cai
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Luis Colina Eiichi Egami Joseph F. Hennawi Xiangyu Jin Hyunsung D. Jun Yana Khusanova Mingyu Li Zihao Li Xiaojing Lin Weizhe Liu Romain A. Meyer Maria A. Pudoka George H. Rieke Yue Shen Wei Leong Tee Bram Venemans Fabian Walter Yunjing Wu Huanian Zhang Siwei Zou
This is my paper · ORCID
classification astro-ph.GA
keywords dustystar-forminggalaxiesinfraredluminosityfunctionobscuredstarformationratedensityJWSTNIRCamgrismspectroscopyALMA1.2mmcontinuumz=4-6cosmichistoryvariance
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 tries to settle how much of the star formation happening when the Universe was roughly one billion years old is hidden inside dusty galaxies. Using a survey of 25 independent quasar sightlines with JWST near-infrared grism spectroscopy and ALMA 1.2 mm imaging, the authors identify eight dusty star-forming galaxies at spectroscopic redshifts $z=4$–$6$ and build an infrared luminosity function that is complete down to $L_{\rm IR}\sim 2\times10^{11}\,L_\odot$. From that function they derive the dust-obscured star-formation rate density at $z\sim5$ as $\log[\rho_{\rm SFR,IR}/(M_\odot\,{\rm yr}^{-1}\,{\rm Mpc}^{-3})] = -1.52$ and conclude that $66\pm7\%$ of all star formation at that epoch is obscured by dust. If correct, earlier deep-field censuses of early star formation missed most of the action because of cosmic variance.

What carries the argument

The machinery is the pairing of two survey instruments over the same footprints: ALMA 1.2 mm continuum mosaics find the dust emission, and JWST/NIRCam slitless grism spectra in F356W (3.1–4.0 $\mu$m) catch H$\alpha$ or [O III] emission lines that pin each source's redshift spectroscopically. Eight such sources at $z=4$–$6$, six of them confirmed by at least a second line, form a sample whose selection is claimed to be spectroscopically complete down to $L_{\rm IR}\sim 2\times10^{11}\,L_\odot$. The analysis then applies a $1/V_{\rm max}$ estimator to build the infrared luminosity function, fits it with a double-power-law model with a free faint-end slope, and integrates the fitted function to obtain the obscured star-formation rate density.

What would settle it

Observe the eight ASPIRE DSFGs in a second ALMA band on the Rayleigh-Jeans tail (for example at 2 mm or 870 $\mu$m) to measure their dust temperatures directly, then recompute $L_{\rm IR}$ from the measured SEDs; if the resulting obscured star-formation rate density at $z=4$–$6$ differs from $\log\rho_{\rm SFR,IR} = -1.52$ by more than the quoted uncertainty, the energy-balance assumption is the reason.

Watch

Extended reading notes

Core claim

On its own terms, the paper establishes that the infrared luminosity function at $z=4$–$6$ is flatter at the faint end than previously modeled (power-law slope $\alpha = 0.59^{+0.39}_{-0.45}$), so luminous and ultra-luminous infrared galaxies ($L_{\rm IR}\sim 10^{11}$–$10^{13}\,L_\odot$) carry most of the obscured star formation. Integrating this function down to $L_{\rm IR}=10^{10}\,L_\odot$ gives an obscured star-formation rate density about five times the value inferred from the ASPECS/HUDF-based backward modeling and consistent with the ALPINE-based and lensing-cluster measurements. The authors attribute the earlier low values to cosmic variance: the Hubble Ultra Deep Field happens to sit in a void of $z>4$ dusty galaxies, while the 25 independent ASPIRE sightlines average out such fluctuations. They conclude that at $z\sim5$ the majority of cosmic star formation, $66\pm7\%$, is dust-obscured, so the total star-formation rate density at this epoch is close to or above the canonical total-SFRD curve.

Load-bearing premise

Everything rests on the infrared luminosities, and those come from an energy-balance model that fits the UV-to-millimeter photometry with only one far-infrared data point; if the dust is patchy or the dust temperature falls outside the assumed 30–50 K range, the luminosity scale and the reported obscured star-formation rate density move with it.

Editorial extensions

If this is right

  • The obscured star-formation rate density at $z\sim5$ is about 0.3 dex higher than the canonical total-SFRD curve at that redshift, meaning the broadly used Madau–Dickinson curve may undercount early star formation.
  • Previous deep single-field millimeter surveys (notably ASPECS in the Hubble Ultra Deep Field) can miss the $z=4$–$6$ dusty population entirely because of cosmic variance; multi-field surveys are necessary at this epoch.
  • Luminous and ultra-luminous infrared galaxies with $L_{\rm IR}$ between $10^{11}$ and $10^{13}\,L_\odot$ contribute about $81\%$ of the obscured star-formation rate density at $z=4$–$6$, so future surveys must cover enough volume to sample the bright end of the luminosity function.
  • Because the faint-end slope of the infrared luminosity function is flat ($\alpha\approx0.6$), the integrated obscured star-formation rate density does not depend strongly on the exact low-luminosity cutoff, making the measurement robust to survey depth.
  • Future JWST NIRCam grism surveys over much larger volumes can spectroscopically constrain the bright end of the $z=4$–$6$ infrared luminosity function, resolving the main remaining uncertainty in the obscured star-formation history.

Reading between the lines

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

  • Editorial inference: the survey targets quasar sightlines, so the eight DSFGs could trace overdense environments around already rare quasars; the authors estimate cosmic variance is small, but a blank-field analog survey would be the clean test of whether this averaged obscured star-formation rate density applies to typical fields.
  • Editorial inference: the F356W grism has a redshift desert near $z\approx5.0$–$5.3$ where neither H$\alpha$ nor [O III] falls in the band; the paper corrects for it statistically, but redder F444W grism data covering that gap would directly test whether a population of $z\approx5.2$ dusty galaxies is being missed.
  • Editorial inference: because $L_{\rm IR}$ is derived from an energy-balance fit with a single far-infrared photometric point, the quoted star-formation rate density scales roughly as $T_{\rm dust}^4$; if future high-frequency ALMA observations show these galaxies are warmer than the assumed $\sim33$ K, the obscured star-formation rate density would rise by roughly 0.1–0.2 dex, strengthening the pa
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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

3 major / 5 minor

Summary. The paper presents a measurement of the dust-obscured cosmic star formation rate density (SFRD) at z=4-6 using the ASPIRE JWST Cycle-1 and ALMA Cycle-9 surveys along 25 quasar sightlines. Eight dusty star-forming galaxies (DSFGs) are identified at z=4-6 through NIRCam WFSS detections of Ha or [O III], and their infrared luminosities are derived from CIGALE energy-balance SED fitting using three NIRCam bands plus a single ALMA 1.2 mm continuum point. From these sources the authors construct a 1/Vmax infrared luminosity function (IRLF), fit it with a double power law, integrate to obtain log rho_SFR,IR = -1.52+0.14/-0.13 (M_sun/yr/Mpc^3), and conclude that 66 +/- 7% of cosmic star formation at z~5 is obscured by dust. The paper also reports a flattened faint-end IRLF slope alpha = 0.59+0.39/-0.45 and discusses systematic effects from dust temperature, the bright-end IRLF, and AGN contamination.

Significance. If correct, this is a valuable direct measurement: it provides spectroscopic redshifts for DSFGs at z=4-6, a redshift range where photometric redshifts are notoriously unreliable, and uses 25 independent sightlines to suppress cosmic variance. The resulting SFRD is higher than ASPECS-based estimates and close to ALPINE/ALCS values, supporting a picture in which a majority of star formation at z~5 is obscured. The paper is transparent about its main assumptions, includes detailed completeness simulations, a lensing correction, and a systematic exploration of dust temperature, and it places the result in the context of existing literature. However, the headline claims of a 'spectroscopically complete census' and the 66% obscured fraction rest on two assumptions that are acknowledged but not fully quantified in the quoted uncertainties: the coverage gap in NIRCam WFSS at z~5.0-5.3, and the energy-balance SED modeling from a single far-IR photometric point. These issues, if unaddressed, weaken the precision of the central result even though the measurement itself is credible.

major comments (3)
  1. [Section 4.2, Section 4.3, Eq. (2)] The title and abstract claim a 'spectroscopically complete' DSFG sample at z=4-6, but the text in Section 4.2 explicitly states that 'the full ASPIRE DSFG sample is not spectroscopically complete across all redshifts' because of the NIRCam F356W gap at z~5.0-5.3 where neither Ha nor [O III] falls in the grism bandpass. The correction for this 'redshift desert' is described only as an argument that the true number density 'should be close to our measurement, or just slightly higher' rather than a quantitative correction. The 1/Vmax method in Eq. (2) is not specified as to whether Vmax excludes the redshift gap; if Vmax includes the gap, the LF is underestimated, and if it excludes it, the SFRD integral over z=4-6 assumes the LF is identical in the gap. The authors should state precisely how Vmax is computed over the gap and provide a quantitative upper/lower bound on the IRLF and SFRD from this assumption. Without this, the word 'complete' in the title overstates the actual selection.
  2. [Section 3.2, Section 5.1, Section 4.4] The central SFRD value and the 66% obscured fraction depend on L_IR values derived from CIGALE energy-balance fitting with only one ALMA 1.2 mm point constraining the rest-frame far-IR SED. Section 5.1 tests dust temperature variations and finds a 0.12 dex shift for Tdust=50 K, which is comparable to the quoted +0.14/-0.13 dex error, and it also tests beta and lambda_thick variations. However, this does not bound the energy-balance assumption itself, which the paper identifies as questionable: J0244m5008.C03 is poorly fit in F115W, and HDF850.1 is cited as a case where UV photon leakage breaks the energy-balance picture. The quoted error bars in Section 4.4 appear to be purely statistical/MCMC and do not include a systematic term for energy-balance violations. The authors should either estimate the magnitude of this systematic (e.g., by fitting L_IR directly from the ALMA flux with a range of dust temperatures and beta values and comparing SFRD, or by explicitly modeling a UV-leakage component for J0244m5008.C03) or clearly state that the headline rho_SFR,IR value is conditional on the energy-balance assumption. This is load-bearing because a ~0.2 dex shift in L_IR would move the obscured fraction toward 50% and change the paper's main qualitative conclusion.
  3. [Section 5.2, Table 3] The SFRD integral is not determined purely by the ASPIRE sample: the bright end of the IRLF (L > L* ~ 10^12.6 L_sun) is constrained by literature measurements that are not spectroscopically complete, with their uncertainties artificially increased by sqrt(2) to account for photometric redshifts. The contribution from ULIRGs (10^12-10^13 L_sun) to the obscured SFRD is 44 +/- 14%, so the quoted SFRD accuracy depends partly on the assumed literature LF. The authors should state explicitly what fraction of the quoted uncertainty on log rho_SFR,IR comes from the literature-anchored bright end versus the ASPIRE data alone, and report an ASPIRE-only SFRD if possible. This would make the contribution of the new data to the headline result more transparent.
minor comments (5)
  1. [Abstract and Section 4.2] The abstract's 'spectroscopically complete DSFG sample at z=4-6' should be qualified, e.g., 'complete for DSFGs with Ha or [O III] in the F356W bandpass' or 'complete over the redshift intervals z=4.0-5.0 and z=5.3-6.0', to match the statement in Section 4.2.
  2. [Section 3.2, Table 2] For J0244m5008.C03, the poorly fitted F115W point is noted in Section 5.1 but not flagged in Table 2 or Figure 5; adding a note or a different symbol for this source would help the reader see the energy-balance issue directly.
  3. [Section 4.2] The sentence describing the correction for the redshift desert gives no formula or magnitude; a short description of the assumed correction (e.g., 'we divide by the fractional redshift coverage' or 'we add a 10% systematic') would clarify the method.
  4. [Section 3.1, Figure 4] The two single-line redshifts (conf=2) are well argued, but the caption of Figure 4 does not mention the confidence level; adding 'conf=2' in the caption would avoid confusion with the six secure redshifts.
  5. [Section 5.1] The authors quote the Tdust=50 K shift as 0.12 dex but do not quote the resulting SFRD for Tdust=30 K; reporting both bounds in Figure 11 or in the text would make the direction and magnitude of the SED systematic clearer.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: the IRLF and obscured SFRD are standard model-based estimates from original JWST/ALMA data, not re-used inputs.

full rationale

I walked the derivation chain. The DSFG sample is selected by independent ALMA 1.2-mm detections plus JWST/NIRCam grism redshifts; L_IR values are obtained with CIGALE energy-balance SED fitting from four photometric bands (Section 3.2), a standard inference whose one-band far-IR constraint is acknowledged and tested in Section 5.1. The IRLF is built with the 1/V_max estimator (Eq. 2) from the sample's L_IR; the double-power-law fit (Eq. 3) uses these points plus literature measurements with inflated errors, and the SFRD (Section 4.4) is the integral of that fitted LF. This is a standard model-based estimate, not a hidden re-use of the target: the fitted LF parameters (alpha = 0.59, log L* = 12.58) are not defined in terms of rho_SFR,IR, and rho_SFR,IR is not an input to the fit. The 66% obscured fraction combines this integral with an independent UV SFRD fit to literature photometry. Self-citations occur for ASPIRE survey design and data reduction (Wang et al. 2023; Yang et al. 2023) and for the HDF850.1 comparison (Sun et al. 2024), but they are not load-bearing for the central SFRD result: the cited papers do not supply the IRLF, the SFRD value, or any uniqueness theorem. The paper itself flags the main systematic caveat (L_IR rests on energy balance with a single far-IR point and a Tdust = 30-50 K prior), but a stated assumption is not circular equivalence. No equation reduces to its input by construction, and no fitted parameter is renamed as a prediction. Verdict: no significant circularity.

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

The central measurement rests on standard astrophysical assumptions about SED modeling, luminosity function shape, and survey representativeness. The most important ones are the energy-balance assumption, the adopted far-IR SED, and the claim of spectroscopic completeness across a redshift range that includes a known observational gap. No new physical entities are introduced.

free parameters (5)
  • Dust temperature Tdust in Casey (2012) SED = 30-50 K flat prior, effective benchmark ~33 K
    CIGALE SED fits use a flat prior of 30-50 K; the benchmark LIR corresponds to Tdust ~33 K at LIR=1e12 Lsun. Adopting Tdust=50 K changes the obscured SFRD by 0.12 dex.
  • Dust emissivity beta_em = 1.8 (fixed)
    Fixed in the Casey (2012) modified blackbody model; varying by +/-0.3 changes the SFRD by 0.02 dex.
  • Mid-IR power-law slope alpha_MIR = 2.0 (fixed)
    Fixed in the adopted dust SED; used to extrapolate the mid-IR luminosity that contributes to LIR.
  • Wavelength where dust optical depth is unity, lambda_thick = 100 um (fixed)
    Fixed in the SED model; varying by a factor of 2 changes the SFRD by 0.07 dex.
  • IRLF double-power-law parameters (log Phi*, log L*, alpha, beta) = -4.52, 12.58, 0.59, 3.24
    Fitted to the ASPIRE luminosity bins plus literature points; the integrated SFRD depends on the assumed functional form, though the paper argues the integral is less sensitive to the parameter degeneracies.
assumptions (7)
  • domain assumption Energy balance: absorbed UV-optical stellar light is re-emitted in the infrared, so CIGALE can infer LIR from a simultaneous fit to stellar and dust emission.
    Section 3.2: 'we have to rely on the energy balance assumption with CIGALE to infer the IR luminosity.' If patchy dust lets UV photons escape without heating dust, LIR and the obscured SFRD could be underestimated.
  • domain assumption The far-IR SEDs of z=4-6 DSFGs follow the Casey (2012) modified blackbody with Tdust=30-50 K, beta_em=1.8, and alpha_MIR=2.0.
    Section 3.2; this model converts one ALMA 1.2 mm flux point into LIR. Section 5.1 tests Tdust variations but not all possible SED shapes.
  • domain assumption The z=4-6 IRLF follows a double power law, and literature points above LIR~1e12.6 Lsun can be combined with ASPIRE points, with literature uncertainties inflated by sqrt(2).
    Section 4.3; ASPIRE alone cannot constrain the bright end, and the best-fit Lstar, alpha, and beta are strongly degenerate.
  • domain assumption The 25 quasar sightlines provide a representative average of the field at z=4-6, with negligible cosmic variance and no residual bias from quasar environments.
    Section 4.2 uses the Moster et al. (2011) cosmic variance prescription with galaxy bias ~8 at z~5 and argues that z=4-6 lies below the quasar redshifts, but the sightlines are not random field pointings.
  • ad hoc to paper The sample is spectroscopically complete at z=4-6 because Halpha or [OIII] luminosities are above the NIRCam WFSS detection limit, except for the z=5.0-5.3 redshift desert where the number density is assumed to be close to the measured value.
    Section 4.2 acknowledges the redshift desert and states that the true number density should be 'close to our measurement, or just slightly higher' without a direct constraint in that interval.
  • domain assumption ALMA continuum peaks with S/N>=5 in native images or S/N>=4 in tapered images, after rejecting positive peaks without JWST counterparts, form a complete DSFG sample.
    Section 2.2; the negative-peak counts are used to estimate spurious fractions, but the requirement of a JWST counterpart within 0.7 arcsec could miss very red or blank-field DSFGs.
  • domain assumption The Chabrier IMF and the Kennicutt (1998) SFR-to-LIR conversion apply at z~5.
    Section 3.2 uses SFR_IR = 1.1e-10 LIR/Lsun and the same IMF assumed throughout the paper; the IMF is not independently constrained at this epoch.

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Cite this review

Pith. "Pith review of A SPectroscopic survey of biased halos In the Reionization Era (ASPIRE): Spectroscopically Complete Census of Obscured Cosmic Star Formation Rate Density at $z=4-6$." pith.science (2026). https://pith.science/paper/SYA5E3W6

@misc{pith2026241206894,
  author       = {Pith},
  title        = {Pith review of: A SPectroscopic survey of biased halos In the Reionization Era (ASPIRE): Spectroscopically Complete Census of Obscured Cosmic Star Formation Rate Density at $z=4-6$},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/SYA5E3W6}},
  note         = {Machine review of arXiv:2412.06894}
}
abstract

We present a stringent measurement of the dust-obscured star-formation rate density (SFRD) at $z=4-6$ from the ASPIRE JWST Cycle-1 medium and ALMA Cycle-9 large program. We obtained JWST/NIRCam grism spectroscopy and ALMA 1.2-mm continuum map along 25 independent quasar sightlines, covering a total survey area of $\sim$35 arcmin$^2$ where we search for dusty star-forming galaxies (DSFGs) at $z = 0 - 7$. We identify eight DSFGs in seven fields at $z=4-6$ through the detection of H$\alpha$ or [O III] $\lambda$5008 lines, including fainter lines such as H$\beta$, [O III] $\lambda$4960, [N II] $\lambda$6585, [S II] $\lambda\lambda$6718,6733 for six sources. With this spectroscopically complete DSFG sample at $z=4-6$ and negligible impact from cosmic variance (shot noise), we measure the infrared luminosity function (IRLF) down to $L_\mathrm{IR} \sim 2\times10^{11}$ $L_\odot$. We find flattening of IRLF at $z=4-6$ towards the faint end (power-law slope $\alpha = 0.59_{-0.45}^{+0.39}$). We determine the dust-obscured cosmic SFRD at this epoch as $\log[\rho_\mathrm{SFR,IR} / (\mathrm{M}_\odot\,\mathrm{yr}^{-1}\,\mathrm{Mpc}^{-3})] = -1.52_{-0.13}^{+0.14}$. This is significantly higher than previous determination using ALMA data in the Hubble Ultra Deep Field, which is void of DSFGs at $z=4-6$ because of strong cosmic variance (shot noise). We conclude that the majority ($66\pm7$%) of cosmic star formation at $z \sim 5$ is still obscured by dust. We also discuss the uncertainty of SFRD propagated from far-IR spectral energy distribution and IRLF at the bright end, which will need to be resolved with future ALMA and JWST observations.

Figures

Figures reproduced from arXiv: 2412.06894 by the authors.

Figure 1
Figure 1. 1.2-mm continuum images of 25 quasar fields obtained by ASPIRE ALMA Cycle-9 large program. In the top-left panel, we highlight the design of JWST/NIRCam and ALMA observations. The whole ALMA 1.2-mm continuum imaging mosaics (uv-tapered with FWHM = 1′′) are within the full spectral (λ = 3.1–4.0 µm with F356W filter) coverage region of NIRCam module A as indicated by the blue shaded region. The quasar J0109–3047 (z = … view at source ↗
Figure 2
Figure 2. JWST NIRCam (red: F356W; green: F200W, blue: F115W) and ALMA 1.2-mm continuum images of DSFGs at z = 4 − 6 discovered with the ASPIRE survey. Image sizes are 4′′×4 ′′ (north up, east left). Source ID, spectroscopic redshifts and ALMA beam sizes are indicated in the plots. Most sources appear red in JWST RGB images, indicating that they are highly dust-obscured galaxies at high redshifts. Note that J0109m3047.C02 is … view at source ↗
Figure 3
Figure 3. JWST NIRCam image, 2D and 1D spectra of six DSFGs with secure spectroscopic redshifts at 4 − 6 (conf=1). Note that the continuum emission (primarily from bright contaminating galaxies) is subtracted in 1D spectra, but not in 2D spectra. NIRCam images are aligned along the dispersion direction (from left to right). Also note that the 2D spectra are compressed in the dispersion direction for display purpose. Primary e… view at source ↗
Figures from the paper (9 more)
Figure 4
Figure 4. Figure 4: Same as [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]
Figure 5
Figure 5. Figure 5: JWST and ALMA SEDs of eight DSFGs in ASPIRE sample. Photometric measurements are shown as red squares. Best-fit SED models obtained with CIGALE are shown as solid black curves. Source ID, redshifts and derived stellar masses, SFRs are indicated in the plots. tion of SF…
Figure 6
Figure 6. Figure 6: The dust obscuration and escape of Hα photons from DSFGs. In the top-left panel, we show the escape fraction of Hα photons versus IR luminosity. ASPIRE DSFGs are shown in red circles and two z > 5 DSFGs in the FRESCO GOODS-N field (HDF850.1, GN10; measurements from Sun…
Figure 7
Figure 7. Figure 7: Differential 1.2-mm number count of DSFGs at z = 4 − 6 measured with the ASPIRE sample (solid red circles). For comparison we show the 1.2-mm number count of DSFGs at all redshifts (Fujimoto et al. 2023; solid black line), and the number count of DSFGs at z = 4 − 6 est…
Figure 8
Figure 8. Figure 8: Spectroscopically complete infrared luminosity function at z = 4 − 6 measured with the ASPIRE sample (solid red circles). The best-fit IRLF and its uncertainties (16–84th percentile) are indicated with the solid red line and shaded region, respectively. For comparison …
Figure 9
Figure 9. Figure 9: MCMC corner plot of the double-power-law parameters for the IRLF fitting ( [PITH_FULL_IMAGE:figures/full_fig_p016_9.png]
Figure 10
Figure 10. Figure 10: Obscured cosmic star formation rate density measured with the ASPIRE sample at z = 4 − 6 (red diamond). For comparison we plot literature measurements of obscured SFRD as open salmon-pink symbols, including Rowan-Robinson et al. (2016), Dunlop et al. (2017), Liu et al…
Figure 11
Figure 11. Figure 11: 16-50-84% credible-interval constraints of obscured SFRD at z ∼ 4.5. Results from ASPIRE under various far-IR SED and dust temperature assumptions are shown on the top as solid red diamonds. Literature results through direct measurement and integral of IRLF are shown …
Figure 12
Figure 12. Figure 12: The contribution to obscured cosmic SFRD at z = 4−6 from sub-LIRGs, LIRGs, ULIRGs and HyLIRGs at LIR from 1010−1014 L⊙. LIRGs and ULIRGs at LIR = 1011−1013 L⊙ con￾tribute to the majority (81+11 −21%) of obscured SFRD at this epoch. et al. 2020; Mitsuhashi et al. 2023;…

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Works this paper leans on

163 extracted references · 12 canonical work pages

  1. [1]

    Abbott, T. M. C., Adam´ow, M., Aguena, M., et al. 2021, ApJS, 255, 20, doi: 10.3847/1538-4365/ac00b3

    doi:10.3847/1538-4365/ac00b3 2021

  2. [2]

    2024, A&A, 685, A1, doi: 10.1051/0004-6361/202348407

    Adscheid, S., Magnelli, B., Liu, D., et al. 2024, A&A, 685, A1, doi: 10.1051/0004-6361/202348407

    doi:10.1051/0004-6361/202348407 2024

  3. [3]

    2016, ApJ, 832, 56, doi: 10.3847/0004-637X/832/1/56 Alcalde Pampliega, B., P´erez-Gonz´alez, P

    Alavi, A., Siana, B., Richard, J., et al. 2016, ApJ, 832, 56, doi: 10.3847/0004-637X/832/1/56 Alcalde Pampliega, B., P´erez-Gonz´alez, P. G., Barro, G., et al. 2019, ApJ, 876, 135, doi: 10.3847/1538-4357/ab14f2

    doi:10.3847/0004-637x/832/1/56 2016

  4. [4]

    Algera, H. S. B., Inami, H., Oesch, P. A., et al. 2023, MNRAS, 518, 6142, doi: 10.1093/mnras/stac3195

    doi:10.1093/mnras/stac3195 2023

  5. [5]

    2020, arXiv e-prints, arXiv:2006.04284

    Aravena, M., Boogaard, L., G´onzalez-L´opez, J., et al. 2020, arXiv e-prints, arXiv:2006.04284. https://arxiv.org/abs/2006.04284

    arXiv 2020

  6. [6]

    M., Evans, A

    Armus, L., Mazzarella, J. M., Evans, A. S., et al. 2009, PASP, 121, 559, doi: 10.1086/600092

    doi:10.1086/600092 2009

  7. [7]

    2011, LePHARE: Photometric Analysis for Redshift Estimate, Astrophysics Source Code Library, record ascl:1108.009 Astropy Collaboration, Robitaille, T

    Arnouts, S., & Ilbert, O. 2011, LePHARE: Photometric Analysis for Redshift Estimate, Astrophysics Source Code Library, record ascl:1108.009 Astropy Collaboration, Robitaille, T. P., Tollerud, E. J., et al. 2013, A&A, 558, A33, doi: 10.1051/0004-6361/201322068 Astropy Collaboration, Price-Whelan, A. M., Sip˝ocz, B. M., et al. 2018, AJ, 156, 123, doi: 10.38...

    doi:10.1051/0004-6361/201322068 2011

  8. [8]

    2024, arXiv e-prints, arXiv:2404.08052, doi: 10.48550/arXiv.2404.08052

    Barrufet, L., Oesch, P., Marques-Chaves, R., et al. 2024, arXiv e-prints, arXiv:2404.08052, doi: 10.48550/arXiv.2404.08052

    arXiv doi:10.48550/arxiv.2404.08052 2024

Show all 163 references
  1. [9]

    E., Alexander, D

    Bauer, F. E., Alexander, D. M., Brandt, W. N., et al. 2004, AJ, 128, 2048, doi: 10.1086/424859 B´ethermin, M., Wu, H.-Y ., Lagache, G., et al. 2017, A&A, 607, A89, doi: 10.1051/0004-6361/201730866 B´ethermin, M., Fudamoto, Y ., Ginolfi, M., et al. 2020, A&A, 643, A2, doi: 10.1...

    2004 doi

  2. [10]

    2024, A&A, 686, A3, doi: 10.1051/0004-6361/202348845

    Bik, A., ´Alvarez-M´arquez, J., Colina, L., et al. 2024, A&A, 686, A3, doi: 10.1051/0004-6361/202348845

    2024 doi

  3. [11]

    A., Gillman, S., Melinder, J., et al

    Boogaard, L. A., Gillman, S., Melinder, J., et al. 2024, ApJ, 969, 27, doi: 10.3847/1538-4357/ad43e5

    2024 doi

  4. [12]

    2019, A&A, 622, A103, doi: 10.1051/0004-6361/201834156

    Boquien, M., Burgarella, D., Roehlly, Y ., et al. 2019, A&A, 622, A103, doi: 10.1051/0004-6361/201834156

    2019 doi

  5. [13]

    2020, ApJ, 902, 112, doi: 10.3847/1538-4357/abb830

    Bouwens, R., Gonz´alez-L´opez, J., Aravena, M., et al. 2020, ApJ, 902, 112, doi: 10.3847/1538-4357/abb830

    2020 doi

  6. [14]

    J., Illingworth, G

    Bouwens, R. J., Illingworth, G. D., Oesch, P. A., et al. 2015, ApJ, 811, 140, doi: 10.1088/0004-637X/811/2/140

    2015 doi

  7. [15]

    2024, astropy/photutils: 1.12.0, 1.12.0, Zenodo, doi: 10.5281/zenodo.10967176

    Bradley, L., Sip˝ocz, B., Robitaille, T., et al. 2024, astropy/photutils: 1.12.0, 1.12.0, Zenodo, doi: 10.5281/zenodo.10967176

    2024 doi

  8. [16]

    2003, MNRAS, 344, 1000, doi: 10.1046/j.1365-8711.2003.06897.x

    Bruzual, G., & Charlot, S. 2003, MNRAS, 344, 1000, doi: 10.1046/j.1365-8711.2003.06897.x

    2003

  9. [17]

    2013, A&A, 554, A70, doi: 10.1051/0004-6361/201321651

    Burgarella, D., Buat, V ., Gruppioni, C., et al. 2013, A&A, 554, A70, doi: 10.1051/0004-6361/201321651

    2013 doi

  10. [18]

    2023, JWST Calibration Pipeline, 1.10.2, Zenodo, doi: 10.5281/zenodo.7829329

    Bushouse, H., Eisenhamer, J., Dencheva, N., et al. 2023, JWST Calibration Pipeline, 1.10.2, Zenodo, doi: 10.5281/zenodo.7829329

    2023 doi

  11. [19]

    2023, arXiv e-prints, arXiv:2302.10323, doi: 10.48550/arXiv.2302.10323

    Calvi, R., Castignani, G., & Dannerbauer, H. 2023, arXiv e-prints, arXiv:2302.10323, doi: 10.48550/arXiv.2302.10323

    2023 doi arXiv

  12. [20]

    2021, MNRAS, 502, 4558, doi: 10.1093/mnras/staa4037

    Calvi, R., Dannerbauer, H., Arrabal Haro, P., et al. 2021, MNRAS, 502, 4558, doi: 10.1093/mnras/staa4037

    2021 doi

  13. [21]

    1997, in American Institute of Physics Conference

    Calzetti, D. 1997, in American Institute of Physics Conference

    1997

  14. [22]

    408, The ultraviolet universe at low and High redshift, ed

    Series, V ol. 408, The ultraviolet universe at low and High redshift, ed. W. H. Waller (AIP), 403–412, doi: 10.1063/1.53764

    doi

  15. [23]

    C., et al

    Calzetti, D., Armus, L., Bohlin, R. C., et al. 2000, ApJ, 533, 682, doi: 10.1086/308692

    2000 doi

  16. [24]

    I., Dunlop, J

    Caputi, K. I., Dunlop, J. S., McLure, R. J., et al. 2012, ApJL, 750, L20, doi: 10.1088/2041-8205/750/1/L20 CASA Team, Bean, B., Bhatnagar, S., et al. 2022, PASP, 134, 114501, doi: 10.1088/1538-3873/ac9642

    2012 doi

  17. [25]

    Casey, C. M. 2012, MNRAS, 425, 3094, doi: 10.1111/j.1365-2966.2012.21455.x

    2012

  18. [26]

    M., Hodge, J., Zavala, J

    Casey, C. M., Hodge, J., Zavala, J. A., et al. 2018a, ApJ, 862, 78, doi: 10.3847/1538-4357/aacd11

    doi

  19. [27]

    M., Narayanan, D., & Cooray, A

    Casey, C. M., Narayanan, D., & Cooray, A. 2014a, PhR, 541, 45, doi: 10.1016/j.physrep.2014.02.009

    2014 doi

  20. [28]

    M., Chen, C.-C., Cowie, L

    Casey, C. M., Chen, C.-C., Cowie, L. L., et al. 2013, MNRAS, 436, 1919, doi: 10.1093/mnras/stt1673

    2013 doi

  21. [29]

    M., Scoville, N

    Casey, C. M., Scoville, N. Z., Sanders, D. B., et al. 2014b, ApJ, 796, 95, doi: 10.1088/0004-637X/796/2/95

    doi

  22. [30]

    M., Zavala, J

    Casey, C. M., Zavala, J. A., Spilker, J., et al. 2018b, ApJ, 862, 77, doi: 10.3847/1538-4357/aac82d

    doi

  23. [31]

    M., Zavala, J

    Casey, C. M., Zavala, J. A., Aravena, M., et al. 2019, ApJ, 887, 55, doi: 10.3847/1538-4357/ab52ff

    2019 doi

  24. [32]

    M., Zavala, J

    Casey, C. M., Zavala, J. A., Manning, S. M., et al. 2021, ApJ, 923, 215, doi: 10.3847/1538-4357/ac2eb4

    2021 doi

  25. [33]

    2003, PASP, 115, 763, doi: 10.1086/376392

    Chabrier, G. 2003, PASP, 115, 763, doi: 10.1086/376392

    2003 doi

  26. [34]

    B., Wang, F., Zhang, H., et al

    Champagne, J. B., Wang, F., Zhang, H., et al. 2024a, arXiv e-prints, arXiv:2410.03826, doi: 10.48550/arXiv.2410.03826

    doi arXiv

  27. [35]

    B., Wang, F., Yang, J., et al

    Champagne, J. B., Wang, F., Yang, J., et al. 2024b, arXiv e-prints, arXiv:2410.03827, doi: 10.48550/arXiv.2410.03827

    doi arXiv

  28. [36]

    2001, ApJ, 556, 562, doi: 10.1086/321609 24

    Chary, R., & Elbaz, D. 2001, ApJ, 556, 562, doi: 10.1086/321609 24

    2001 doi

  29. [37]

    2022, ApJ, 929, 159, doi: 10.3847/1538-4357/ac61df

    Chen, C.-C., Liao, C.-L., Smail, I., et al. 2022, ApJ, 929, 159, doi: 10.3847/1538-4357/ac61df

    2022 doi

  30. [38]

    J., Zwaan, M

    Chen, J., Ivison, R. J., Zwaan, M. A., et al. 2023, MNRAS, 518, 1378, doi: 10.1093/mnras/stac2989

    2023 doi

  31. [39]

    2023, A&A, 673, L6, doi: 10.1051/0004-6361/202346535

    Colina, L., Crespo G´omez, A., ´Alvarez-M´arquez, J., et al. 2023, A&A, 673, L6, doi: 10.1051/0004-6361/202346535

    2023 doi

  32. [40]

    R., Brammer, G., Heintz, K

    Cooper, O. R., Brammer, G., Heintz, K. E., et al. 2024, arXiv e-prints, arXiv:2410.08387, doi: 10.48550/arXiv.2410.08387

    2024 doi arXiv

  33. [41]

    L., Garmire, G

    Cowie, L. L., Garmire, G. P., Bautz, M. W., et al. 2002, ApJL, 566, L5, doi: 10.1086/339545

    2002 doi

  34. [42]

    2012, A&A, 539, A31, doi: 10.1051/0004-6361/201118010 da Cunha, E., Groves, B., Walter, F., et al

    Cucciati, O., Tresse, L., Ilbert, O., et al. 2012, A&A, 539, A31, doi: 10.1051/0004-6361/201118010 da Cunha, E., Groves, B., Walter, F., et al. 2013, ApJ, 766, 13, doi: 10.1088/0004-637X/766/1/13 da Cunha, E., Walter, F., Smail, I. R., et al. 2015, ApJ, 806, 110, doi: 10.1088/...

    2012 doi

  35. [43]

    P., et al

    Decarli, R., Walter, F., Venemans, B. P., et al. 2017, Nature, 545, 457, doi: 10.1038/nature22358

    2017 doi

  36. [44]

    J., Lang, D., et al

    Dey, A., Schlegel, D. J., Lang, D., et al. 2019, AJ, 157, 168, doi: 10.3847/1538-3881/ab089d

    2019 doi

  37. [45]

    M., & Casey, C

    Drew, P. M., & Casey, C. M. 2022, arXiv e-prints, arXiv:2203.16655. https://arxiv.org/abs/2203.16655

    2022 arXiv

  38. [46]

    P., & Robotham, A

    Driver, S. P., & Robotham, A. S. G. 2010, MNRAS, 407, 2131, doi: 10.1111/j.1365-2966.2010.17028.x Dudzeviˇci¯ut˙e, U., Smail, I., Swinbank, A. M., et al. 2020, MNRAS, 494, 3828, doi: 10.1093/mnras/staa769

    2010

  39. [47]

    S., McLure, R

    Dunlop, J. S., McLure, R. J., Biggs, A. D., et al. 2017, MNRAS, 466, 861, doi: 10.1093/mnras/stw3088

    2017 doi

  40. [48]

    2022, ApJ, 927, 204, doi: 10.3847/1538-4357/ac51ca

    Enia, A., Talia, M., Pozzi, F., et al. 2022, ApJ, 927, 204, doi: 10.3847/1538-4357/ac51ca

    2022 doi

  41. [49]

    L., Fudamoto, Y ., Oesch, P

    Faisst, A. L., Fudamoto, Y ., Oesch, P. A., et al. 2020, MNRAS, 498, 4192, doi: 10.1093/mnras/staa2545

    2020 doi

  42. [50]

    L., Ryan, Russell E., J., Papovich, C., et al

    Finkelstein, S. L., Ryan, Russell E., J., Papovich, C., et al. 2015, ApJ, 810, 71, doi: 10.1088/0004-637X/810/1/71

    2015 doi

  43. [51]

    W., Lang, D., & Goodman, J

    Foreman-Mackey, D., Hogg, D. W., Lang, D., & Goodman, J. 2013, PASP, 125, 306, doi: 10.1086/670067

    2013 doi

  44. [52]

    2018, A&A, 620, A152, doi: 10.1051/0004-6361/201832928

    Franco, M., Elbaz, D., B´ethermin, M., et al. 2018, A&A, 620, A152, doi: 10.1051/0004-6361/201832928

    2018 doi

  45. [53]

    A., Faisst, A., et al

    Fudamoto, Y ., Oesch, P. A., Faisst, A., et al. 2020, A&A, 643, A4, doi: 10.1051/0004-6361/202038163

    2020 doi

  46. [54]

    A., Schouws, S., et al

    Fudamoto, Y ., Oesch, P. A., Schouws, S., et al. 2021, Nature, 597, 489, doi: 10.1038/s41586-021-03846-z

    2021 doi

  47. [55]

    2016, ApJS, 222, 1, doi: 10.3847/0067-0049/222/1/1

    Fujimoto, S., Ouchi, M., Ono, Y ., et al. 2016, ApJS, 222, 1, doi: 10.3847/0067-0049/222/1/1

    2016 doi

  48. [56]

    2023, arXiv e-prints, arXiv:2303.01658, doi: 10.48550/arXiv.2303.01658 Gaia Collaboration, Vallenari, A., Brown, A

    Fujimoto, S., Kohno, K., Ouchi, M., et al. 2023, arXiv e-prints, arXiv:2303.01658, doi: 10.48550/arXiv.2303.01658 Gaia Collaboration, Vallenari, A., Brown, A. G. A., et al. 2023, A&A, 674, A1, doi: 10.1051/0004-6361/202243940 Garc´ıa-Mar´ın, M., Colina, L., & Arribas, S. 2009,...

    2023 doi arXiv

  49. [57]

    P., Mather, J

    Gardner, J. P., Mather, J. C., Abbott, R., et al. 2023, PASP, 135, 068001, doi: 10.1088/1538-3873/acd1b5

    2023 doi

  50. [58]

    E., Dunlop, J

    Geach, J. E., Dunlop, J. S., Halpern, M., et al. 2017, MNRAS, 465, 1789, doi: 10.1093/mnras/stw2721

    2017 doi

  51. [59]

    1986, ApJ, 303, 336, doi: 10.1086/164079 Gim´enez-Arteaga, C., Brammer, G

    Gehrels, N. 1986, ApJ, 303, 336, doi: 10.1086/164079 Gim´enez-Arteaga, C., Brammer, G. B., Marchesini, D., et al. 2022, ApJS, 263, 17, doi: 10.3847/1538-4365/ac958c G´omez-Guijarro, C., Elbaz, D., Xiao, M., et al. 2022, A&A, 658, A43, doi: 10.1051/0004-6361/202141615 Gonz´alez...

    1986 arXiv

  52. [60]

    Wolff, M. J. 2003, ApJ, 594, 279, doi: 10.1086/376774

    2003 doi

  53. [61]

    A., et al

    Gottumukkala, R., Barrufet, L., Oesch, P. A., et al. 2024, MNRAS, 530, 966, doi: 10.1093/mnras/stae754

    2024 doi

  54. [62]

    2013, MNRAS, 432, 23, doi: 10.1093/mnras/stt308

    Gruppioni, C., Pozzi, F., Rodighiero, G., et al. 2013, MNRAS, 432, 23, doi: 10.1093/mnras/stt308

    2013 doi

  55. [63]

    2020, A&A, 643, A8, doi: 10.1051/0004-6361/202038487

    Gruppioni, C., B´ethermin, M., Loiacono, F., et al. 2020, A&A, 643, A8, doi: 10.1051/0004-6361/202038487

    2020 doi

  56. [64]

    2018, PASJ, 70, 105, doi: 10.1093/pasj/psy104

    Hatsukade, B., Kohno, K., Yamaguchi, Y ., et al. 2018, PASJ, 70, 105, doi: 10.1093/pasj/psy104

    2018 doi

  57. [65]

    J., Oesch, P

    Herard-Demanche, T., Bouwens, R. J., Oesch, P. A., et al. 2023, arXiv e-prints, arXiv:2309.04525, doi: 10.48550/arXiv.2309.04525

    2023 doi arXiv

  58. [66]

    A., & da Cunha, E

    Hodge, J. A., & da Cunha, E. 2020, Royal Society Open Science, 7, 200556, doi: 10.1098/rsos.200556

    2020 doi

  59. [67]

    1986, PASP, 98, 609, doi: 10.1086/131801

    Horne, K. 1986, PASP, 98, 609, doi: 10.1086/131801

    1986 doi

  60. [68]

    S., Zheng, X

    Huang, J. S., Zheng, X. Z., Rigopoulou, D., et al. 2011, ApJL, 742, L13, doi: 10.1088/2041-8205/742/1/L13

    2011 doi

  61. [69]

    H., Serjeant, S., Dunlop, J., et al

    Hughes, D. H., Serjeant, S., Dunlop, J., et al. 1998, Nature, 394, 241, doi: 10.1038/28328

    1998 doi

  62. [70]

    R., et al

    Hyun, M., Im, M., Smail, I. R., et al. 2023, ApJS, 264, 19, doi: 10.3847/1538-4365/ac9bf4

    2023 doi

  63. [71]

    2019a, ApJS, 244, 33, doi: 10.3847/1538-4365/ab3bfe

    Jin, J.-J., Zhu, Y .-N., Wu, H., et al. 2019a, ApJS, 244, 33, doi: 10.3847/1538-4365/ab3bfe

    doi

  64. [72]

    E., et al

    Jin, S., Daddi, E., Magdis, G. E., et al. 2019b, ApJ, 887, 144, doi: 10.3847/1538-4357/ab55d6

    doi

  65. [73]

    B., Hodge, J., et al

    Jin, S., Sillassen, N. B., Hodge, J., et al. 2024a, arXiv e-prints, arXiv:2407.07585, doi: 10.48550/arXiv.2407.07585

    doi arXiv

  66. [74]

    2024b, arXiv e-prints, arXiv:2410.01318, doi: 10.48550/arXiv.2410.01318

    Jin, X., Yang, J., Fan, X., et al. 2024b, arXiv e-prints, arXiv:2410.01318, doi: 10.48550/arXiv.2410.01318

    doi arXiv

  67. [75]

    P., Limousin, M., et al

    Jullo, E., Kneib, J. P., Limousin, M., et al. 2007, New Journal of Physics, 9, 447, doi: 10.1088/1367-2630/9/12/447

    2007 doi

  68. [76]

    1998, ARA&A, 36, 189, doi: 10.1146/annurev.astro.36.1.189

    Kennicutt, Robert C., J. 1998, ARA&A, 36, 189, doi: 10.1146/annurev.astro.36.1.189

    1998 doi

  69. [77]

    A., Tsutsumi, T., Brogan, C

    Kepley, A. A., Tsutsumi, T., Brogan, C. L., et al. 2020, PASP, 132, 024505, doi: 10.1088/1538-3873/ab5e14 25

    2020 doi

  70. [78]

    2021, A&A, 649, A152, doi: 10.1051/0004-6361/202038944

    Khusanova, Y ., Bethermin, M., Le F`evre, O., et al. 2021, A&A, 649, A152, doi: 10.1051/0004-6361/202038944

    2021 doi

  71. [79]

    2023, arXiv e-prints, arXiv:2305.15126, doi: 10.48550/arXiv.2305.15126

    Kohno, K., Fujimoto, S., Tsujita, A., et al. 2023, arXiv e-prints, arXiv:2305.15126, doi: 10.48550/arXiv.2305.15126

    2023 doi arXiv

  72. [80]

    2022, ApJS, 263, 38, doi: 10.3847/1538-4365/ac9909

    Kokorev, V ., Brammer, G., Fujimoto, S., et al. 2022, ApJS, 263, 38, doi: 10.3847/1538-4365/ac9909

    2022 doi

  73. [81]

    P., Dunlop, J

    Koprowski, M. P., Dunlop, J. S., Michałowski, M. J., et al. 2017, MNRAS, 471, 4155, doi: 10.1093/mnras/stx1843

    2017 doi

  74. [82]

    Lagos, C. d. P., da Cunha, E., Robotham, A. S. G., et al. 2020, MNRAS, 499, 1948, doi: 10.1093/mnras/staa2861 Le F`evre, O., B´ethermin, M., Faisst, A., et al. 2020, A&A, 643, A1, doi: 10.1051/0004-6361/201936965

    2020 doi

  75. [83]

    2020, ApJ, 889, 80, doi: 10.3847/1538-4357/ab607f

    Lim, C.-F., Wang, W.-H., Smail, I., et al. 2020, ApJ, 889, 80, doi: 10.3847/1538-4357/ab607f

    2020 doi

  76. [84]

    2024, arXiv e-prints, arXiv:2402.16649, doi: 10.48550/arXiv.2402.16649

    Ling, C., Sun, B., Cheng, C., et al. 2024, arXiv e-prints, arXiv:2402.16649, doi: 10.48550/arXiv.2402.16649

    2024 doi arXiv

  77. [85]

    2018, ApJ, 853, 172, doi: 10.3847/1538-4357/aaa600

    Liu, D., Daddi, E., Dickinson, M., et al. 2018, ApJ, 853, 172, doi: 10.3847/1538-4357/aaa600

    2018 doi

  78. [86]

    Lyu, J., & Rieke, G. H. 2018, ApJ, 866, 92, doi: 10.3847/1538-4357/aae075

    2018 doi

  79. [87]

    H., & Shi, Y

    Lyu, J., Rieke, G. H., & Shi, Y . 2017, ApJ, 835, 257, doi: 10.3847/1538-4357/835/2/257

    2017 doi

  80. [88]

    2014, ARA&A, 52, 415, doi: 10.1146/annurev-astro-081811-125615

    Madau, P., & Dickinson, M. 2014, ARA&A, 52, 415, doi: 10.1146/annurev-astro-081811-125615

    2014 doi

  81. [89]

    2014, A&A, 561, A86, doi: 10.1051/0004-6361/201322217

    Magnelli, B., Lutz, D., Saintonge, A., et al. 2014, A&A, 561, A86, doi: 10.1051/0004-6361/201322217

    2014 doi

  82. [90]

    2024, arXiv e-prints, arXiv:2405.18086, doi: 10.48550/arXiv.2405.18086

    Magnelli, B., Adscheid, S., Wang, T.-M., et al. 2024, arXiv e-prints, arXiv:2405.18086, doi: 10.48550/arXiv.2405.18086

    2024 doi arXiv

  83. [91]

    M., Casey, C

    Manning, S. M., Casey, C. M., Zavala, J. A., et al. 2022, ApJ, 925, 23, doi: 10.3847/1538-4357/ac366a

    2022 doi

  84. [92]

    P., et al

    Mazzucchelli, C., Decarli, R., Farina, E. P., et al. 2019, ApJ, 881, 163, doi: 10.3847/1538-4357/ab2f75

    2019 doi

  85. [93]

    C., Rosenthal, L

    McKinney, J., Hayward, C. C., Rosenthal, L. J., et al. 2021, ApJ, 921, 55, doi: 10.3847/1538-4357/ac185f

    2021 doi

  86. [94]

    M., Cooper, O

    McKinney, J., Manning, S. M., Cooper, O. R., et al. 2023a, ApJ, 956, 72, doi: 10.3847/1538-4357/acf614

    doi

  87. [95]

    M., et al

    McKinney, J., Finnerty, L., Casey, C. M., et al. 2023b, ApJL, 946, L39, doi: 10.3847/2041-8213/acc322

    doi

  88. [96]

    M., Long, A

    McKinney, J., Casey, C. M., Long, A. S., et al. 2024, arXiv e-prints, arXiv:2408.08346, doi: 10.48550/arXiv.2408.08346

    2024 doi arXiv

  89. [97]

    P., Waters, B., Schiebel, D., Young, W., & Golap, K

    McMullin, J. P., Waters, B., Schiebel, D., Young, W., & Golap, K. 2007, in Astronomical Society of the Pacific Conference Series, V ol. 376, Astronomical Data Analysis Software and Systems XVI, ed. R. A. Shaw, F. Hill, & D. J. Bell, 127

    2007

  90. [98]

    2017, ApJ, 838, 29, doi: 10.3847/1538-4357/aa6259

    Mehta, V ., Scarlata, C., Rafelski, M., et al. 2017, ApJ, 838, 29, doi: 10.3847/1538-4357/aa6259

    2017 doi

  91. [99]

    R., Heckman, T

    Meurer, G. R., Heckman, T. M., & Calzetti, D. 1999, ApJ, 521, 64, doi: 10.1086/307523

    1999 doi

  92. [100]

    E., et al

    Mitsuhashi, I., Harikane, Y ., Bauer, F. E., et al. 2023, arXiv e-prints, arXiv:2311.16857, doi: 10.48550/arXiv.2311.16857

    2023 doi arXiv

  93. [101]

    2003, ApJ, 588, 696, doi: 10.1086/374335

    Moretti, A., Campana, S., Lazzati, D., & Tagliaferri, G. 2003, ApJ, 588, 696, doi: 10.1086/374335

    2003 doi

  94. [102]

    P., Somerville, R

    Moster, B. P., Somerville, R. S., Newman, J. A., & Rix, H.-W. 2011, ApJ, 731, 113, doi: 10.1088/0004-637X/731/2/113

    2011 doi

  95. [103]

    2020, MNRAS, 494, 1894, doi: 10.1093/mnras/staa706

    Moutard, T., Sawicki, M., Arnouts, S., et al. 2020, MNRAS, 494, 1894, doi: 10.1093/mnras/staa706

    2020 doi

  96. [104]

    T., Schulz, B., Levenson, L., et al

    Nguyen, H. T., Schulz, B., Levenson, L., et al. 2010, A&A, 518, L5, doi: 10.1051/0004-6361/201014680

    2010 doi

  97. [105]

    2009, A&A, 507, 1793, doi: 10.1051/0004-6361/200912497

    Noll, S., Burgarella, D., Giovannoli, E., et al. 2009, A&A, 507, 1793, doi: 10.1051/0004-6361/200912497

    2009 doi

  98. [106]

    A., Stiavelli, M., Carollo, C

    Oesch, P. A., Stiavelli, M., Carollo, C. M., et al. 2007, ApJ, 671, 1212, doi: 10.1086/522423

    2007 doi

  99. [107]

    A., Brammer, G., Naidu, R

    Oesch, P. A., Brammer, G., Naidu, R. P., et al. 2023, arXiv e-prints, arXiv:2304.02026, doi: 10.48550/arXiv.2304.02026

    2023 doi arXiv

  100. [108]

    B., & Gunn, J

    Oke, J. B., & Gunn, J. E. 1983, ApJ, 266, 713, doi: 10.1086/160817

    1983 doi

  101. [109]

    2013, A&A, 554, L3, doi: 10.1051/0004-6361/201321478

    Oteo, I., Cepa, J., Bongiovanni, ´A., et al. 2013, A&A, 554, L3, doi: 10.1051/0004-6361/201321478

    2013 doi

  102. [110]

    Y ., Ho, L

    Peng, C. Y ., Ho, L. C., Impey, C. D., & Rix, H.-W. 2010, AJ, 139, 2097, doi: 10.1088/0004-6256/139/6/2097

    2010 doi

  103. [111]

    2012, ApJ, 759, 28, doi: 10.1088/0004-637X/759/1/28

    Penner, K., Dickinson, M., Pope, A., et al. 2012, ApJ, 759, 28, doi: 10.1088/0004-637X/759/1/28

    2012 doi

  104. [112]

    2015, A&A, 577, A78, doi: 10.1051/0004-6361/201425359

    Pereira-Santaella, M., Alonso-Herrero, A., Colina, L., et al. 2015, A&A, 577, A78, doi: 10.1051/0004-6361/201425359

    2015 doi

  105. [113]

    D., Sivaramakrishnan, A., Lajoie, C.-P., et al

    Perrin, M. D., Sivaramakrishnan, A., Lajoie, C.-P., et al. 2014, in Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, V ol. 9143, Space Telescopes and Instrumentation 2014: Optical, Infrared, and Millimeter Wave, ed. J. Oschmann, Jacobus M., M. Clamp...

    2014 doi

  106. [114]

    2020, ApJ, 891, 135, doi: 10.3847/1538-4357/ab76c0

    Popping, G., Walter, F., Behroozi, P., et al. 2020, ApJ, 891, 135, doi: 10.3847/1538-4357/ab76c0

    2020 doi

  107. [115]

    2020, The Journal of Open Source Software, 5, 2308, doi: 10.21105/joss.02308

    Prochaska, J., Hennawi, J., Westfall, K., et al. 2020, The Journal of Open Source Software, 5, 2308, doi: 10.21105/joss.02308

    2020 doi

  108. [116]

    D., Egami, E., Bussmann, R

    Rawle, T. D., Egami, E., Bussmann, R. S., et al. 2014, ApJ, 783, 59, doi: 10.1088/0004-637X/783/1/59

    2014 doi

  109. [117]

    A., & Steidel, C

    Reddy, N. A., & Steidel, C. C. 2009, ApJ, 692, 778, doi: 10.1088/0004-637X/692/1/778

    2009 doi

  110. [118]

    D., Spilker, J

    Reuter, C., Vieira, J. D., Spilker, J. S., et al. 2020, ApJ, 902, 78, doi: 10.3847/1538-4357/abb599

    2020 doi

  111. [119]

    A., Bradford, C

    Riechers, D. A., Bradford, C. M., Clements, D. L., et al. 2013, Nature, 496, 329, doi: 10.1038/nature12050

    2013 doi

  112. [120]

    A., Hodge, J

    Riechers, D. A., Hodge, J. A., Pavesi, R., et al. 2020, ApJ, 895, 81, doi: 10.3847/1538-4357/ab8c48

    2020 doi

  113. [121]

    H., Alonso-Herrero, A., Weiner, B

    Rieke, G. H., Alonso-Herrero, A., Weiner, B. J., et al. 2009, ApJ, 692, 556, doi: 10.1088/0004-637X/692/1/556

    2009 doi

  114. [122]

    J., Kelly, D

    Rieke, M. J., Kelly, D. M., Misselt, K., et al. 2023, PASP, 135, 028001, doi: 10.1088/1538-3873/acac53

    2023 doi

  115. [123]

    2016, MNRAS, 461, 1100, doi: 10.1093/mnras/stw1169

    Rowan-Robinson, M., Oliver, S., Wang, L., et al. 2016, MNRAS, 461, 1100, doi: 10.1093/mnras/stw1169

    2016 doi

  116. [124]

    1968, ApJ, 151, 393, doi: 10.1086/149446 26

    Schmidt, M. 1968, ApJ, 151, 393, doi: 10.1086/149446 26

    1968 doi

  117. [125]

    2018, A&A, 609, A30, doi: 10.1051/0004-6361/201731506

    Schreiber, C., Elbaz, D., Pannella, M., et al. 2018, A&A, 609, A30, doi: 10.1051/0004-6361/201731506

    2018 doi

  118. [126]

    2024, arXiv e-prints, arXiv:2408.12713, doi: 10.48550/arXiv.2408.12713

    Shen, Y ., Zhuang, M.-Y ., Li, J., et al. 2024, arXiv e-prints, arXiv:2408.12713, doi: 10.48550/arXiv.2408.12713

    2024 doi arXiv

  119. [127]

    2020, MNRAS, 498, 5065, doi: 10.1093/mnras/staa2621

    Shim, H., Kim, Y ., Lee, D., et al. 2020, MNRAS, 498, 5065, doi: 10.1093/mnras/staa2621

    2020 doi

  120. [128]

    M., Smail, I., Swinbank, A

    Simpson, J. M., Smail, I., Swinbank, A. M., et al. 2015, ApJ, 799, 81, doi: 10.1088/0004-637X/799/1/81 —. 2019, ApJ, 880, 43, doi: 10.3847/1538-4357/ab23ff

    2015 doi

  121. [129]

    L., Weiss, A., Vieira, J

    Strandet, M. L., Weiss, A., Vieira, J. D., et al. 2016, ApJ, 822, 80, doi: 10.3847/0004-637X/822/2/80

    2016 doi

  122. [130]

    G., et al

    Sun, F., Egami, E., P´erez-Gonz´alez, P. G., et al. 2021, ApJ, 922, 114, doi: 10.3847/1538-4357/ac2578

    2021 doi

  123. [131]

    2022, ApJ, 932, 77, doi: 10.3847/1538-4357/ac6e3f

    Sun, F., Egami, E., Fujimoto, S., et al. 2022, ApJ, 932, 77, doi: 10.3847/1538-4357/ac6e3f

    2022 doi

  124. [132]

    2023a, ApJ, 953, 53, doi: 10.3847/1538-4357/acd53c

    Sun, F., Egami, E., Pirzkal, N., et al. 2023a, ApJ, 953, 53, doi: 10.3847/1538-4357/acd53c

    doi

  125. [133]

    M., Egami, E., et al

    Sun, F., Helton, J. M., Egami, E., et al. 2024, ApJ, 961, 69, doi: 10.3847/1538-4357/ad07e3

    2024 doi

  126. [134]

    I., et al

    Sun, L., Wang, X., Teplitz, H. I., et al. 2023b, arXiv e-prints, arXiv:2311.15664, doi: 10.48550/arXiv.2311.15664

    doi arXiv

  127. [135]

    M., Page, M

    Symeonidis, M., Giblin, B. M., Page, M. J., et al. 2016, MNRAS, 459, 257, doi: 10.1093/mnras/stw667

    2016 doi

  128. [136]

    J., Hainline, K., et al

    Tacchella, S., Eisenstein, D. J., Hainline, K., et al. 2023, arXiv e-prints, arXiv:2302.07234, doi: 10.48550/arXiv.2302.07234

    2023 doi arXiv

  129. [137]

    2021, ApJ, 909, 23, doi: 10.3847/1538-4357/abd6e3

    Talia, M., Cimatti, A., Giulietti, M., et al. 2021, ApJ, 909, 23, doi: 10.3847/1538-4357/abd6e3

    2021 doi

  130. [138]

    2024, A&A, 681, A118, doi: 10.1051/0004-6361/202347048

    Traina, A., Gruppioni, C., Delvecchio, I., et al. 2024, A&A, 681, A118, doi: 10.1051/0004-6361/202347048

    2024 doi

  131. [139]

    2020, MNRAS, 491, 3937, doi: 10.1093/mnras/stz3234

    Obreschkow, D. 2020, MNRAS, 491, 3937, doi: 10.1093/mnras/stz3234

    2020 doi

  132. [140]

    2008, ApJ, 676, 767, doi: 10.1086/528674

    Trenti, M., & Stiavelli, M. 2008, ApJ, 676, 767, doi: 10.1086/528674

    2008 doi

  133. [141]

    2024, arXiv e-prints, arXiv:2406.09890, doi: 10.48550/arXiv.2406.09890 ¨Ubler, H., F¨orster Schreiber, N

    Tsujita, A., Kohno, K., Huang, S., et al. 2024, arXiv e-prints, arXiv:2406.09890, doi: 10.48550/arXiv.2406.09890 ¨Ubler, H., F¨orster Schreiber, N. M., Genzel, R., et al. 2017, ApJ, 842, 121, doi: 10.3847/1538-4357/aa7558 ¨Ubler, H., D’Eugenio, F., Perna, M., et al. 2024, arXi...

    2024 doi arXiv

  134. [142]

    2024, A&A, 685, A138, doi: 10.1051/0004-6361/202348128

    Valentino, F., Fujimoto, S., Gim´enez-Arteaga, C., et al. 2024, A&A, 685, A138, doi: 10.1051/0004-6361/202348128

    2024 doi

  135. [143]

    P., Neeleman, M., Walter, F., et al

    Venemans, B. P., Neeleman, M., Walter, F., et al. 2019, ApJL, 874, L30, doi: 10.3847/2041-8213/ab11cc

    2019 doi

  136. [144]

    P., Wilkins, S

    Vijayan, A. P., Wilkins, S. M., Lovell, C. C., et al. 2022, MNRAS, 511, 4999, doi: 10.1093/mnras/stac338

    2022 doi

  137. [145]

    2024, arXiv e-prints, arXiv:2407.09681

    Villanueva, V ., Herrera-Camus, R., Gonzalez-Lopez, J., et al. 2024, arXiv e-prints, arXiv:2407.09681. https://arxiv.org/abs/2407.09681

    2024 arXiv

  138. [146]

    2012, Nature, 486, 233, doi: 10.1038/nature11073

    Walter, F., Decarli, R., Carilli, C., et al. 2012, Nature, 486, 233, doi: 10.1038/nature11073

    2012 doi

  139. [147]

    2016, ApJ, 833, 67, doi: 10.3847/1538-4357/833/1/67

    Walter, F., Decarli, R., Aravena, M., et al. 2016, ApJ, 833, 67, doi: 10.3847/1538-4357/833/1/67

    2016 doi

  140. [148]

    F., et al

    Wang, F., Yang, J., Hennawi, J. F., et al. 2023, ApJL, 951, L4, doi: 10.3847/2041-8213/accd6f

    2023 doi

  141. [149]

    J., Cowley, W., et al

    Wang, L., Pearson, W. J., Cowley, W., et al. 2019a, A&A, 624, A98, doi: 10.1051/0004-6361/201834093

    doi

  142. [150]

    2016, ApJ, 816, 84, doi: 10.3847/0004-637X/816/2/84

    Wang, T., Elbaz, D., Schreiber, C., et al. 2016, ApJ, 816, 84, doi: 10.3847/0004-637X/816/2/84

    2016 doi

  143. [151]

    2019b, Nature, 572, 211, doi: 10.1038/s41586-019-1452-4

    Wang, T., Schreiber, C., Elbaz, D., et al. 2019b, Nature, 572, 211, doi: 10.1038/s41586-019-1452-4

    doi

  144. [152]

    2017, ApJ, 850, 37, doi: 10.3847/1538-4357/aa911b Weiß, A., De Breuck, C., Marrone, D

    Wang, W.-H., Lin, W.-C., Lim, C.-F., et al. 2017, ApJ, 850, 37, doi: 10.3847/1538-4357/aa911b Weiß, A., De Breuck, C., Marrone, D. P., et al. 2013, ApJ, 767, 88, doi: 10.1088/0004-637X/767/1/88

    2017 doi

  145. [153]

    E., Pope, A., Cybulski, R., et al

    Whitaker, K. E., Pope, A., Cybulski, R., et al. 2017, ApJ, 850, 208, doi: 10.3847/1538-4357/aa94ce

    2017 doi

  146. [154]

    C., Labbe, I., Spilker, J., et al

    Williams, C. C., Labbe, I., Spilker, J., et al. 2019, ApJ, 884, 154, doi: 10.3847/1538-4357/ab44aa

    2019 doi

  147. [155]

    C., Alberts, S., Ji, Z., et al

    Williams, C. C., Alberts, S., Ji, Z., et al. 2024, ApJ, 968, 34, doi: 10.3847/1538-4357/ad3f17

    2024 doi

  148. [156]

    2023, ApJL, 956, L40, doi: 10.3847/2041-8213/acfee3

    Wu, Y ., Wang, F., Cai, Z., et al. 2023, ApJL, 956, L40, doi: 10.3847/2041-8213/acfee3

    2023 doi

  149. [157]

    2023a, arXiv e-prints, arXiv:2309.02492, doi: 10.48550/arXiv.2309.02492

    Xiao, M., Oesch, P., Elbaz, D., et al. 2023a, arXiv e-prints, arXiv:2309.02492, doi: 10.48550/arXiv.2309.02492

    doi arXiv

  150. [158]

    Y ., Elbaz, D., G´omez-Guijarro, C., et al

    Xiao, M. Y ., Elbaz, D., G´omez-Guijarro, C., et al. 2023b, A&A, 672, A18, doi: 10.1051/0004-6361/202245100

    doi

  151. [159]

    2019, ApJ, 878, 73, doi: 10.3847/1538-4357/ab0d22

    Yamaguchi, Y ., Kohno, K., Hatsukade, B., et al. 2019, ApJ, 878, 73, doi: 10.3847/1538-4357/ab0d22

    2019 doi

  152. [160]

    2021, ApJ, 923, 262, doi: 10.3847/1538-4357/ac2b32 —

    Yang, J., Wang, F., Fan, X., et al. 2021, ApJ, 923, 262, doi: 10.3847/1538-4357/ac2b32 —. 2023, ApJL, 951, L5, doi: 10.3847/2041-8213/acc9c8

    2021 doi

  153. [161]

    A., Casey, C

    Zavala, J. A., Casey, C. M., Manning, S. M., et al. 2021, ApJ, 909, 165, doi: 10.3847/1538-4357/abdb27

    2021 doi

  154. [162]

    T., Narayanan, D., Whitaker, K

    Zimmerman, D. T., Narayanan, D., Whitaker, K. E., & Dav´e, R. 2024, ApJ, 973, 146, doi: 10.3847/1538-4357/ad6844

    2024 doi

  155. [163]

    2024, ApJL, 963, L28, doi: 10.3847/2041-8213/ad23e7 All Authors and Affiliations FENGWU SUN ,1 FEIGE WANG ,2, 3 JINYI YANG ,2, 3 JACLYN B

    Zou, S., Cai, Z., Wang, F., et al. 2024, ApJL, 963, L28, doi: 10.3847/2041-8213/ad23e7 All Authors and Affiliations FENGWU SUN ,1 FEIGE WANG ,2, 3 JINYI YANG ,2, 3 JACLYN B. C HAMPAGNE ,3 ROBERTO DECARLI ,4 XIAOHUI FAN ,3 EDUARDO BA ˜NADOS ,5 ZHENG CAI ,6 LUIS COLINA ,7 EIICHI...

    2024 doi

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