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A Comprehensive Photometric Selection of `Little Red Dots' in MIRI Fields: An IR-Bright LRD at $z=3.1386$ with Warm Dust Emission

T0 review · 4 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read A new photometric selection using a diagonal NIRCam color cut recovers 93% of known Little Red Dots and adds 40% more, raising their number density by a factor of 1.7, while MIRI data reveal warm dust in a z=3.1386 LRD.

desk verdict Useful new LRD selection that recovers almost all previous samples and adds 40% more candidates, but the ×1.7 census gain rests on unverified purity and needs a contamination test; the warm-dust source is a careful, if degenerate, case study. read the letter →

arxiv 2412.01887 v1 pith:RYYD3NEY submitted 2024-12-02 astro-ph.GA

Guillermo Barro , Pablo G. Perez-Gonzalez , Dale D. Kocevski , Elizabeth J. McGrath , Gene C. K. Leung , Fergus Cullen , James S. Dunlop , Richard S. Ellis
show 8 more authors
Steven L. Finkelstein Norman A. Grogin Garth Illingworth Jeyhan S. Kartaltepe Anton M. Koekemoer Ray A. Lucas Ross J. McLure Guang Yang
This is my paper · ORCID
classification astro-ph.GA
keywords LittleRedDotsphotometricselectionJWSTMIRInumberdensitywarmdustAGNhigh-redshiftgalaxiesemission
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 introduces a photometric selection designed to capture the full range of colors and redshifts of 'Little Red Dots' (LRDs), compact red sources at z≈3–9 that are likely dusty starbursts or reddened AGN. Applied to 263 arcmin² of JWST NIRCam+MIRI imaging, the method finds 248 LRDs to F444W<27 mag, a factor of 1.7 higher number density than previous samples, implying earlier censuses underestimated the population. Using MIRI to extend the SED into the rest-frame near- and mid-infrared, the paper shows most LRDs are detected at 7.7 µm and that their rest-frame [1–3 µm] colors indicate diverse dust emission, with about half showing substantial dust fractions. It also reports a spectroscopically confirmed LRD at z=3.1386 whose SED rises steeply beyond 6 µm and peaks near 40 µm, interpreted as the first direct evidence of warm (50–100 K) dust emission in an LRD. A sympathetic reader would care because the census underpins estimates of early black hole growth and dust production.

What carries the argument

The selection is carried by a single NIRCam color-color diagram with a diagonal threshold: F200W−F444W > F115W−F200W + 0.25, along with F200W−F444W>1, F115W−F200W>−0.5, F444W<27. The pivot F200W traces rest-frame UV at z>4.5 and avoids the [O III]/Hα spikes that contaminate F277W; the diagonal cut runs parallel to the locus of massive dusty galaxies, separating LRDs from red extended galaxies while keeping the blue UV emission that defines them. A compactness criterion (F444W flux ratio 0.5"/0.2" <1.5) removes resolved interlopers. The color-redshift tracks of five LRD models spanning [0.25–1 µm]~2–5 mag define the selection region and are the device that produces the claimed completeness gain.

What would settle it

A MIRI-selected, spectroscopically confirmed sample of LRDs at z≈3–5 with F200W−F444W<1 mag, or a demonstration that the five model tracks misplace the observed LRD locus in Figure 1, would falsify the completeness claim. Alternatively, a complete F770W-selected census in the same fields yielding a surface density consistent with earlier samples (~0.5 arcmin⁻²) would show the ×1.7 gain is a selection artifact.

Watch

Extended reading notes

Core claim

The central claim is that a color-color selection using F200W−F444W>1, (F200W−F444W)>(F115W−F200W)+0.25, F115W−F200W>−0.5, F444W<27, plus a compactness cut, recovers 93% of previously known LRDs and adds ~40% new ones, raising the surface density to 0.93 arcmin⁻² and the number density by ×1.7 relative to KOV24 and KOI24. The new objects are preferentially fainter (median F444W=26.2) and bluer (F277W−F444W~0.8), many at z<5.5 where previous cuts were confused by [O III] emission. The paper's second claim is that MIRI data trace dust: F1800W-detected LRDs have median [1–3 µm]=1.5 mag, most have dust fractions up to f$_{3\mu m}$~0.7–0.8, and one object, JADES-191056 at z=3.1386, shows a steep IR rise and peak at ~40 µm consistent with warm dust at T=50–100 K, the first direct evidence of such emission in an LRD.

Load-bearing premise

The selection box is drawn from color-redshift tracks of five LRD models; if real LRDs at 3<z<9 have colors outside that box, the ×1.7 number-density gain is a calibration artifact, not a true census increase.

Editorial extensions

If this is right

  • If the selection is complete, previous LRD volume densities of ~10⁻⁵ Mpc⁻³ are underestimated by a factor ~1.7, strengthening the case that these objects are a major channel for early black hole growth.
  • The ~40% of new LRDs at z<5.5 with blue F277W−F444W colors imply that emission-line-boosted photometry had been hiding a substantial low-redshift LRD population.
  • The median [1–3 µm]=1.5 mag and dust fractions up to ~0.8 imply that dust emission, not a flat stellar continuum, dominates the 3 µm rest-frame light in most F1800W-detected LRDs.
  • The z=3.1386 LRD's warm-dust peak at ~40 µm makes low-redshift LRDs the best targets for future MIRI spectroscopy to identify PAH or silicate features and settle the ISM-versus-torus origin of the dust.

Reading between the lines

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

  • The correlation between bluer UV-to-NIR and redder [1–3 µm] colors suggests a sequence where the bluest LRDs are transitional to unobscured type-1 QSOs; the paper hints at this but does not claim it as a unified evolutionary track.
  • If warm dust at 50–100 K is common, the ALMA non-detections of LRDs would imply the dust is compact and warm rather than extended and cold, which would favor AGN-torus or nuclear starburst geometries over galaxy-wide cold dust.
  • Deeper F1800W imaging by ~1 mag, as the paper notes, would turn most current upper limits into detections or tight bounds, directly testing whether the majority of LRDs have dust fractions as high as 0.7–0.8.
  • The same color-color method could be applied to wider JWST fields without MIRI, since the selection itself uses only NIRCam; MIRI is needed only for the dust-emission analysis, so the completeness gain may extend to the full NIRCam survey area.
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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 / 5 minor

Summary. The paper presents a new photometric selection for Little Red Dots (LRDs) based on NIRCam colors (F200W−F444W>1, F200W−F444W > F115W−F200W+0.25, F115W−F200W>−0.5, F444W<27) plus a compactness criterion, applied to 263 arcmin^2 of JADES, PRIMER-COSMOS, and PRIMER-UDS fields with MIRI coverage. The selection yields 248 LRDs, about 1.7 times more than previous samples in the same fields, with the new objects preferentially fainter and bluer. The paper uses MIRI photometry to derive rest-frame UV-to-NIR and near-to-mid IR colors, concluding that most LRDs show some dust emission at 3 μm but much less than obscured QSOs. A detailed case study of JADES-191056 at z=3.1386 combines MIRI, Spitzer/MIPS24, and Herschel/PACS data to claim the first direct evidence of warm dust emission (T~50–100 K) in an LRD. The SED is fit with two independent codes (Prospector-AGN and Synthesizer-AGN), but galaxy- and AGN-dominated scenarios remain degenerate.

Significance. If the selection is pure and complete as claimed, the paper's central result—that previous LRD censuses underestimated the population by a factor of ~1.7—is an important step toward a complete census of these objects at 3<z<9. The paper also provides a valuable MIRI-based color framework for distinguishing dust-dominated from dust-poor LRDs, and the MIPS24/PSF decomposition for JADES-191056 is a careful treatment of a blended source. The methodological strengths include the use of two independent SED-fitting codes, explicit comparison with previous samples (KOV24, KOI24), and public MIRI data. The main caveat is that the selection thresholds are calibrated on a small set of model tracks, and the redshift distribution is derived from photometric redshifts without an explicit purity test, so the magnitude of the density increase is not yet fully secured.

major comments (4)
  1. [§3.1, Figure 1] The selection thresholds (i)–(iv) are explicitly chosen to enclose the color-redshift tracks of five LRD models and to exclude a star-forming and a type-1 QSO track. This is a calibration loop: the selected sample will trivially contain objects with colors similar to the models. The claim that the sample is more complete (×1.7) than previous selections requires that those five models span the full LRD locus, but the paper does not provide a quantitative test of how the completeness fraction varies across the color-magnitude diagram, nor does it quantify the fraction of the 248 objects that lie in regions of the diagram not sampled by the model tracks. I recommend adding a completeness estimate computed from simulated sources drawn from a broader distribution of SEDs (including low-z interlopers) and evaluating the selection's purity against spectroscopic redshift samples in these fields.
  2. [§3.3, §4.1, §6] The number-density comparison (0.93 arcmin^-2, ×1.7 relative to KOV24/KOI24) rests on the assumption that the 248 selected objects are predominantly genuine LRDs at 3<z<9. However, the selection applies no redshift lower bound, and the only non-color cut is the compactness ratio (FF444W(0.5")/FF444W(0.2")<1.5). At z≲3, F115W−F200W probes rest-frame optical rather than UV, and compact quiescent or dusty star-forming galaxies can plausibly satisfy the diagonal color cut. The paper's redshift distribution is based on EAZY with an LRD template and Prospector-AGN fits; a low-z compact red galaxy can be fit at higher redshift, so the plotted 3<z<9 histogram does not demonstrate purity. The 99 objects not in previous samples are preferentially faint (median F444W≈26.2) and blue (median F277W−F444W≈0.8), making them especially vulnerable to contamination. I request a quantitative contamination assessment: e.g., how many selected objects have photometric redshifts <3 from an independent code, how many have spectroscopic redshifts (including MUSE in GOODS-S), and what fraction of the new faint/blue subset is expected to be low-z interlopers based on the non-LRD models shown in Figure 1.
  3. [§4.1, Figure 6 (bottom)] The redshift distribution shows a median z=5.5 with a peak at z=5 and secondary peak at z=7, but the text does not state whether any redshift cut is applied to the sample before computing number densities. If objects with z<3 are included in the 248 count, the ×1.7 density increase is inflated by non-LRDs. The paper should either apply a hard photometric-redshift lower bound (and show how many objects are removed) or explicitly justify why no lower bound is needed despite the selection's reduced ability to distinguish LRDs from red galaxies at low z. This is load-bearing for the central census claim.
  4. [§5, Figure 11, Appendix A] For JADES-191056, the MIPS24 flux is derived via PSF fitting with position priors from MIRI, yielding fν=35.7±3.8 μJy for the LRD and 12.3±1.7 μJy for the companion. However, the PACS100 and PACS160 fluxes are measured in circular apertures without deconvolution, and the text states the companion contributes ~30% based on the MIPS24 analysis. The warm-dust claim (steep rise at λ_rest>6 μm, peak ~40 μm, T=50–100 K) depends on the PACS photometry. Please quantify how the conclusion changes if the companion contributes more than 30% in the PACS bands (e.g., if the companion has a redder mid-IR SED), or provide an upper-limit analysis that excludes PACS160 given its low SNR.
minor comments (5)
  1. [§5 (first paragraph)] There is a typo: 'stidues' should be 'studies'.
  2. [Figure 7 caption] The center panel caption begins 'Center: Center:' with a duplicated word.
  3. [§2.1] The text says PRIMER has 'the same WCF3 bands'—should be 'WFC3 bands'.
  4. [Figure 3 caption] The caption describes 'KOK24' in one place ('KOK24 (purple)') while the text and legend use KOV24; please standardize the abbreviation.
  5. [§4.1] The statement that F1800W detections are concentrated at z≲5 with median z=4.7 is based on only 17 objects; the text should note the small-number statistics when interpreting this subset.

Circularity Check

1 steps flagged · score 3.0 of 10

Selection thresholds are calibrated to LRD model tracks, so the x1.7 completeness gain is partly a consequence of the box definition; the MIRI and warm-dust results are independent.

  1. fitted input called prediction [Section 3.1, selection criteria (thresholds i-iv) and Figure 1 right panel; census claim in Sections 3.3 and 6]
    "The F200W−F444W>1 mag threshold was chosen to cover all LRD color-redshift tracks, including the bluest model while excluding the SFG and type I QSO tracks. This choice balances LRD coverage with avoiding denser regions of the diagram, where contamination from strong emission lines could increase."

    The selection box is explicitly drawn as the envelope of five LRD model color tracks, and the paper then reports that the new selection finds 248 LRDs, roughly x1.7 more than previous samples, concluding that previous censuses were underestimated. Because the thresholds were chosen to include the bluer and fainter colors that prior redder cuts missed, the excess count is a direct consequence of the constructed box once the five models are assumed to span the true LRD locus. The completeness gain is therefore partly definitional rather than an independent validation that the new objects are genuinely LRDs. The MIRI color distributions and the warm-dust SED of JADES-191056 are separate, data-driven measurements that do not inherit this loop.

full rationale

The paper is mostly self-contained and uses external benchmarks: it recovers 93% of the LRDs in the independent KOV24 and KOI24 samples, which provides a genuine anchor for the selection. The main circularity is mild and confined to the census claim: thresholds (i)-(iv) in Section 3.1 were chosen to enclose the color-redshift tracks of five representative LRD models, so finding an additional population of bluer, fainter objects is largely a consequence of the box definition rather than an independent test of the true LRD locus. This is a calibration loop, not a full reduction of the central result to a fitted parameter. The MIRI detection statistics, rest-frame color distributions, and especially the warm-dust analysis of JADES-191056 rest on independent photometry (MIRI, Spitzer/MIPS24, Herschel/PACS100/160, and ALMA upper limits), so those claims are not circular. The paper also explicitly acknowledges the ISM-versus-torus degeneracy and the limited IR SED coverage, so it does not overclaim those conclusions. Self-citations to Perez-Gonzalez et al. (2024), Kocevski et al. (2024), and Leung et al. (2024) are present but are anchored to real data and external samples, not invoked as an unverified uniqueness theorem. The lack of spectroscopy for the 99 newly identified candidates is a contamination/purity risk rather than a circularity, and therefore does not raise the score further.

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

The central claims rest on hand-tuned selection thresholds, representative templates, and photometric deblending assumptions, rather than on new free parameters in a derivation. No new physical entities are introduced.

free parameters (5)
  • Selection threshold F200W-F444W > 1 mag = 1 mag
    Chosen to cover all five LRD color-redshift tracks while excluding SFG and type I QSO tracks (Section 3.1, Figure 1). It is a hand-tuned boundary, not derived from theory.
  • Diagonal selection offset +0.25 mag = +0.25 mag
    Offset in the condition (F200W-F444W) > (F115W-F200W) + 0.25, set so the cut runs parallel to the locus of massive dusty and evolved galaxies (Section 3.1).
  • UV slope threshold F115W-F200W > -0.5 mag = -0.5 mag
    Set to reject brown dwarf contaminants while retaining LRD tracks (Section 3.1).
  • Compactness flux ratio < 1.5 = FF444W(0.5")/FF444W(0.2") < 1.5
    Chosen following Greene et al. (2023) to exclude resolved galaxies (Section 3.2, Figure 2).
  • Warm dust temperature in MBB fit = 167 K (JADES-191056); 50-500 K allowed range
    Fitted to MIPS24 and PACS100 and PACS160 fluxes in Section 5.3; the single-MBB model does not reproduce PACS160.
assumptions (4)
  • domain assumption Flat LCDM cosmology with Omega_M=0.3, Omega_Lambda=0.7, H0=70 km/s/Mpc
    Standard cosmology used for all luminosities and densities (Section 1).
  • ad hoc to paper The five LRD SED models used to define the selection thresholds are representative of the full LRD population.
    The thresholds are chosen to enclose their color-redshift tracks (Section 3.1); if the true population lies outside, completeness claims fail.
  • domain assumption The MIRI photometry is point-source aperture corrected and the compactness criterion does not exclude genuine LRDs.
    Assumed in Sections 2.2 and 3.2; extended LRDs would be missed by the flux ratio cut.
  • ad hoc to paper PSF-fitting photometry correctly deblends JADES-191056 from its companion in MIPS24.
    Section 5.3 and Appendix A rely on this to attribute 70% of the MIPS24 flux to the LRD.

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Cite this review

Pith. "Pith review of A Comprehensive Photometric Selection of `Little Red Dots' in MIRI Fields: An IR-Bright LRD at $z=3.1386$ with Warm Dust Emission." pith.science (2026). https://pith.science/paper/RYYD3NEY

@misc{pith2026241201887,
  author       = {Pith},
  title        = {Pith review of: A Comprehensive Photometric Selection of `Little Red Dots' in MIRI Fields: An IR-Bright LRD at $z=3.1386$ with Warm Dust Emission},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/RYYD3NEY}},
  note         = {Machine review of arXiv:2412.01887}
}
abstract

JWST has revealed a population of compact `Little Red Dots' (LRDs) at $z\gtrsim4$, with red rest-frame optical and blue UV colors. These objects are likely compact dusty starbursts or heavily reddened AGNs, playing a pivotal role in early black hole growth, dust production, and stellar assembly. We introduce a new photometric selection to identify LRDs over a broad range in redshifts and rest-frame UV-to-NIR colors enabling a more complete census of the population. This method identifies 248 LRDs with F444W$<27$ mag over 263 arcmin$^2$ in the JADES, PRIMER-COSMOS, and UDS fields with MIRI coverage, increasing the number density by $\times$1.7 compared to previous samples, suggesting that previous census were underestimated. Most LRDs are detected in MIRI/F770W but only 7% (17) are detected in F1800W. We use MIRI-based rest-frame [1$-$3 $\mu$m] colors to trace dust emission. F1800W-detected LRDs have a median [1$-$3 $\mu$m]$=1.5$ mag, with a broad scatter indicative of diverse dust emission properties. About 20% exhibit [1$-$3 $\mu$m]$<1$ mag colors consistent with negligible dust emission, but the majority show significant dust emission at 3 $\mu$m (f$^{\rm dust}_{3\mu m}\lesssim0.8$) from the galaxy ISM or a hot-dust-deficient AGN torus. A correlation between bluer UV-to-NIR colors and stronger IR emission suggests that the bluest LRDs may resemble unobscured QSOs. We report a LRD at $z_{\rm spec}=3.1386$, detected in MIRI, Spitzer/MIPS, and Herschel/PACS. Its IR SED rises steeply at $\lambda_{\rm rest}>6~\mu$m and peaks near $\sim40~\mu$m, providing the first direct evidence of warm dust emission (T$=50-100$ K) in a LRD.

Figures

Figures reproduced from arXiv: 2412.01887 by the authors.

Figure 1
Figure 1. NIRCam color-color diagrams illustrating photometric selection methods for LRDs and their typical SEDs. Left: Best-fit SED models for 5 LRDs (solid lines) drawn from P´erez-Gonz´alez et al. (2024) and Wang et al. (2024a), covering a representative range of UV-to-NIR colors indicated in the text. The black and green dashed lines show typical SEDs for a young, low-extinction star-forming galaxy with extreme emission l… view at source ↗
Figure 2
Figure 2. The flux ratio within apertures of radius r = 0.5” and 0.2” in F444W for the full photometric sample in PRIMER and JADES is shown by black dots. The unresolved stellar locus appears as a straight line at FF444W(0.5”)/FF444W(0.2”) ∼ 1.2. The black line marks the compactness threshold used to identify LRDs (red circles). tion from strong emission lines could increase. In the following sections, we show that using the … view at source ↗
Figure 3
Figure 3. Left: New color-color selection diagram (as in the right panel of [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figures from the paper (10 more)
Figure 4
Figure 4. Figure 4: Same color-color and color-magnitude diagrams as in [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]
Figure 5
Figure 5. Figure 5: F1800W−F770W color vs. F444W magni￾tude for MIRI-detected LRDs. Orange circles represent F1800W detections, and blue triangles indicate upper limits for LRDs detected only in F770W. The blue lines show the running median and percentiles for the upper limits. His￾togram…
Figure 6
Figure 6. Figure 6: Left: Cumulative number density of LRDs as a function of F277W−F444W color (black; bottom axis) and F444W magnitude (grey; top axis). The density increases steeply with bluer colors, rising by up to ∼5× from F277W−F444W=1.5 to 0. Our selection agrees with previous samp…
Figure 7
Figure 7. Figure 7: Left: UV-to-NIR color vs. redshift for LRDs detected in F1800W (orange) and F770W (blue). Black and red markers indicate LRDs in common with previous works. LRDs span a broad range of ∼ 3 mag in UV-to-NIR colors, with medians of [0.25−1 µm] = 2.5 mag (F770W) and 3.0 (F…
Figure 8
Figure 8. Figure 8: Stellar and AGN-dominated SED models illus￾trating the range of near-to-mid IR colors for LRDs. The solid lines represent models where the continuum is domi￾nated by stars (light) or stars plus nebular emission (dark). The dashed lines correspond to models dominated by…
Figure 9
Figure 9. Figure 9: UV and NIR absolute magnitudes (M0.25µm and M1µm) vs. color and redshift for F1800W detections (orange) and non-detections (blue). Left: Running medians (grey lines) show a constant median M0.25µm = −19 mag with ∼2 mag scatter, consistent with previous works. Central: …
Figure 10
Figure 10. Figure 10: 2”×2” cutouts of the two F1800W-detected LRDs at z=3.1 in different NIRCam bands. Left: JADES-191056 shows resolved structure in the short-wavelength NIRCam bands with a low S´ersic index (n = 0.58) and re = 0.62 kpc in F090W. The source is slightly elongated, possibl…
Figure 11
Figure 11. Figure 11: 5”×5” cutouts and best-fit SEDs for the two LRDs at z ∼ 3.1, using galaxy- and AGN-dominated models. Top: JADES-191056. Left: The top rows show the blue rest-frame UV (ACS composite), red optical (NIRCam composite), and MIRI IR detections. A red square on the lower-re…
Figure 12
Figure 12. Figure 12: MIPS24 photometry of JADES-191056. Left: Panels a) and b) show 51”×51” cutouts of the MIRI/F2100W and Spitzer/MIPS24 images around JADES-191056. The high-resolution MIRI image shows the primary source and two nearby companions. The dashed line indicates the 25”×25” zo…
Figure 13
Figure 13. Figure 13: Top: 5”×5” NIRCam color (F150W+F277W+F444W) cutouts of the F1800W-detected LRDs. Each row shows 7, 5 and 4 sources in PRIMER-COSMOS, UDS and JADES, respectively. Middle: 5”×5” MIRI/F1800W cutouts of the same LRDs smoothed with a 3-pixel Gaussian kernel to increase the…

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Forward citations

Cited by 4 Pith papers

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

  1. What you see is what you get: empirically measured bolometric luminosities of Little Red Dots

    astro-ph.GA 2025-09 conditional novelty 7.0 of 10

    Directly integrating the observed spectra of two Little Red Dots shows the bolometric luminosity is dominated by rest-frame optical light, lowering implied black hole masses to about 10^5 to 10^7 solar masses.

  2. Where did all the Little Red Dots go? The abundance of LRD analogues among objects with broad lines at $z < 0.35$

    astro-ph.GA 2026-08 conditional novelty 6.0 of 10

    Only 0.08% of low-redshift broad-line objects have SEDs resembling high-redshift Little Red Dots, yielding nine candidates, one of which is the known analogue 'The Egg'.

  3. ATLAS. II. Extremely High Incidence of Balmer Line Absorption with Predominant Blueshifts in LRDs: Statistical Insights through Comparison with Type 1 AGNs

    astro-ph.GA 2026-07 conditional novelty 6.0 of 10

    Balmer-line absorption occurs in ~35% (14/40) of JWST little-red-dot AGNs, roughly 850x the rate in SDSS type-1 AGNs, with mostly slow blueshifted absorber velocities.

  4. Little Red Dots are Tidal Disruption Events in Runaway-Collapsing Clusters

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    Little Red Dots may be tidal disruption events in runaway-collapsing clusters that form intermediate-mass black hole seeds.

Reference graph

Works this paper leans on

112 extracted references · 7 canonical work pages · cited by 4 Pith papers

  1. [1]

    , " * write output.state after.block = add.period write newline

    ENTRY address archivePrefix author booktitle chapter doi edition editor eprint howpublished institution journal key month number organization pages publisher school series title misctitle type volume year version url label extra.label sort.label short.list INTEGERS output.state before.all mid.sentence after.sentence after.block FUNCTION init.state.consts ...

  2. [2]

    write newline

    " write newline "" before.all 'output.state := FUNCTION format.url url empty "" new.block "" url * "" * if FUNCTION format.eprint eprint empty "" archivePrefix empty "" archivePrefix "arXiv" = new.block " " eprint * " " * new.block " " eprint * " " * if if if FUNCTION format.doi doi empty "" " " doi * " " * if FUNCTION format.pid doi empty eprint empty ur...

  3. [3]

    38 Y?Op| l _06jy = !bt 1 xx&EglgH2L| ߥxja t T ,lȓ _ŇB>>i40 QY l& lֽHSר vq Dun@ 2 4nv 7 !!rD )1v I tyf^Rf YfG

    thebibliography [1] 20pt to REFERENCES 6pt =0pt -12pt 10pt plus 3pt =0pt =0pt =1pt plus 1pt =0pt =0pt -12pt =13pt plus 1pt =20pt =13pt plus 1pt \@M =10000 =-1.0em =0pt =0pt 0pt =0pt =1.0em @enumiv\@empty 10000 10000 `\.\@m \@noitemerr \@latex@warning Empty `thebibliography' environment \@ifnextchar \@reference \@latexerr Missing key on reference command E...

    arXiv 2021

  4. [4]

    B., Casey , C

    Akins , H. B., Casey , C. M., Lambrides , E., et al. 2024, arXiv e-prints, arXiv:2406.10341, 10.48550/arXiv.2406.10341

    arXiv doi:10.48550/arxiv.2406.10341 2024

  5. [5]

    2024, arXiv e-prints, arXiv:2405.15972, 10.48550/arXiv.2405.15972

    Alberts , S., Lyu , J., Shivaei , I., et al. 2024, arXiv e-prints, arXiv:2405.15972, 10.48550/arXiv.2405.15972

    arXiv doi:10.48550/arxiv.2405.15972 2024

  6. [6]

    M., Lim , P

    Astropy Collaboration , Price-Whelan , A. M., Lim , P. L., et al. 2022, , 935, 167, 10.3847/1538-4357/ac7c74

    doi:10.3847/1538-4357/ac7c74 2022

  7. [7]

    Baggen , J. F. W., van Dokkum , P., Labbe , I., et al. 2023, arXiv e-prints, arXiv:2305.17162, 10.48550/arXiv.2305.17162

    arXiv doi:10.48550/arxiv.2305.17162 2023

  8. [8]

    2015, sep: v0.3.0 , v0.3.0, Zenodo, 10.5281/zenodo.15669

    Barbary , K., Boone , K., & Deil , C. 2015, sep: v0.3.0 , v0.3.0, Zenodo, 10.5281/zenodo.15669

    work page doi:10.5281/zenodo.15669 2015

Show all 112 references
  1. [9]

    G., Gallego , J., et al

    Barro , G., P \'e rez-Gonz \'a lez , P. G., Gallego , J., et al. 2011, , 193, 30, 10.1088/0067-0049/193/2/30

    2011 doi

  2. [10]

    M., P \'e rez-Gonz \'a lez , P

    Barro , G., Faber , S. M., P \'e rez-Gonz \'a lez , P. G., et al. 2013, , 765, 104, 10.1088/0004-637X/765/2/104

    2013 doi

  3. [11]

    G., et al

    Barro , G., Kriek , M., P \'e rez-Gonz \'a lez , P. G., et al. 2016, , 827, L32, 10.3847/2041-8205/827/2/L32

    2016 doi

  4. [12]

    M., Koo , D

    Barro , G., Faber , S. M., Koo , D. C., et al. 2017, , 840, 47, 10.3847/1538-4357/aa6b05

    2017 doi

  5. [13]

    G., Cava , A., et al

    Barro , G., P \'e rez-Gonz \'a lez , P. G., Cava , A., et al. 2019, , 243, 22, 10.3847/1538-4365/ab23f2

    2019 doi

  6. [14]

    G., Kocevski , D

    Barro , G., P \'e rez-Gonz \'a lez , P. G., Kocevski , D. D., et al. 2024, , 963, 128, 10.3847/1538-4357/ad167e

    2024 doi

  7. [15]

    2011, , 532, A49, 10.1051/0004-6361/201116844

    Berta , S., Magnelli , B., Nordon , R., et al. 2011, , 532, A49, 10.1051/0004-6361/201116844

    2011 doi

  8. [16]

    1996, , 117, 393

    Bertin , E., & Arnouts , S. 1996, , 117, 393

    1996

  9. [17]

    2024, , 965, 103, 10.3847/1538-4357/ad2a50

    Bianchin , M., U , V., Song , Y., et al. 2024, , 965, 103, 10.3847/1538-4357/ad2a50

    2024 doi

  10. [18]

    2022, arXiv e-prints, arXiv:2208.01611, 10.48550/arXiv.2208.01611

    Boylan-Kolchin , M. 2022, arXiv e-prints, arXiv:2208.01611, 10.48550/arXiv.2208.01611

    2022 doi arXiv

  11. [19]

    2019, Grizli: Grism redshift and line analysis software , Astrophysics Source Code Library, record ascl:1905.001

    Brammer , G. 2019, Grizli: Grism redshift and line analysis software , Astrophysics Source Code Library, record ascl:1905.001

    2019

  12. [20]

    B., van Dokkum , P

    Brammer , G. B., van Dokkum , P. G., & Coppi , P. 2008, , 686, 1503, 10.1086/591786

    2008 doi

  13. [21]

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

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

    2003

  14. [22]

    A., van der Werf , P

    Buiten , V. A., van der Werf , P. P., Viti , S., et al. 2024, , 966, 166, 10.3847/1538-4357/ad344b

    2024 doi

  15. [23]

    2017, , 465, 54, 10.1093/mnras/stw2749

    Calura , F., Pozzi , F., Cresci , G., et al. 2017, , 465, 54, 10.1093/mnras/stw2749

    2017 doi

  16. [24]

    C., et al

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

    2000 doi

  17. [25]

    M., Akins , H

    Casey , C. M., Akins , H. B., Kokorev , V., et al. 2024, arXiv e-prints, arXiv:2407.05094, 10.48550/arXiv.2407.05094

    2024 doi arXiv

  18. [26]

    2003, , 115, 763, 10.1086/376392

    Chabrier , G. 2003, , 115, 763, 10.1086/376392

    2003 doi

  19. [27]

    2001, , 556, 562, 10.1086/321609

    Chary , R., & Elbaz , D. 2001, , 556, 562, 10.1086/321609

    2001 doi

  20. [28]

    A., & Helou , G

    Dale , D. A., & Helou , G. 2002, , 576, 159, 10.1086/341632

    2002 doi

  21. [29]

    2024, arXiv e-prints, arXiv:2409.05948, 10.48550/arXiv.2409.05948

    de Graaff , A., Brammer , G., Weibel , A., et al. 2024, arXiv e-prints, arXiv:2409.05948, 10.48550/arXiv.2409.05948

    2024 doi arXiv

  22. [30]

    2014, ArXiv e-prints

    Dekel , A., & Burkert , A. 2014, ArXiv e-prints. 1310.1074

    2014 arXiv

  23. [31]

    C., Birnboim , Y., Mandelker , N., & Li , Z

    Dekel , A., Sarkar , K. C., Birnboim , Y., Mandelker , N., & Li , Z. 2023, , 523, 3201, 10.1093/mnras/stad1557

    2023 doi

  24. [32]

    2003, in The Mass of Galaxies at Low and High Redshift, ed

    Dickinson , M., Giavalisco , M., & GOODS Team . 2003, in The Mass of Galaxies at Low and High Redshift, ed. R. Bender & A. Renzini , 324, 10.1007/10899892_78

    2003 doi

  25. [33]

    2020, , 644, A144, 10.1051/0004-6361/202038405

    Donevski , D., Lapi , A., Ma ek , K., et al. 2020, , 644, A144, 10.1051/0004-6361/202038405

    2020 doi

  26. [34]

    L., Koekemoer , A

    Donley , J. L., Koekemoer , A. M., Brusa , M., et al. 2012, , 748, 142, 10.1088/0004-637X/748/2/142

    2012 doi

  27. [35]

    L., Kartaltepe , J., Kocevski , D., et al

    Donley , J. L., Kartaltepe , J., Kocevski , D., et al. 2018, , 853, 63, 10.3847/1538-4357/aa9ffa

    2018 doi

  28. [36]

    T., & Li , A

    Draine , B. T., & Li , A. 2007, , 657, 810, 10.1086/511055

    2007 doi

  29. [37]

    S., Abraham , R

    Dunlop , J. S., Abraham , R. G., Ashby , M. L. N., et al. 2021, PRIMER: Public Release IMaging for Extragalactic Research , JWST Proposal. Cycle 1, ID. \#1837

    2021

  30. [38]

    J., Willott , C., Alberts , S., et al

    Eisenstein , D. J., Willott , C., Alberts , S., et al. 2023 a , arXiv e-prints, arXiv:2306.02465, 10.48550/arXiv.2306.02465

    2023 doi arXiv

  31. [39]

    J., Johnson , B

    Eisenstein , D. J., Johnson , B. D., Robertson , B., et al. 2023 b , arXiv e-prints, arXiv:2310.12340, 10.48550/arXiv.2310.12340

    2023 doi

  32. [40]

    S., et al

    Elbaz , D., Dickinson , M., Hwang , H. S., et al. 2011, , 533, A119, 10.1051/0004-6361/201117239

    2011 doi

  33. [41]

    P., Lyu , J., et al

    Endsley , R., Stark , D. P., Lyu , J., et al. 2023, , 520, 4609, 10.1093/mnras/stad266

    2023 doi

  34. [42]

    2018, , 620, A152, 10.1051/0004-6361/201832928

    Franco , M., Elbaz , D., B \'e thermin , M., et al. 2018, , 620, A152, 10.1051/0004-6361/201832928

    2018 doi

  35. [43]

    J., Zitrin , A., Plat , A., et al

    Furtak , L. J., Zitrin , A., Plat , A., et al. 2022, arXiv e-prints, arXiv:2212.10531, 10.48550/arXiv.2212.10531

    2022 doi arXiv

  36. [44]

    C., Koekemoer , A

    Giavalisco , M., Ferguson , H. C., Koekemoer , A. M., et al. 2004, , 600, L93, 10.1086/379232

    2004 doi

  37. [45]

    2022, , 658, A43, 10.1051/0004-6361/202141615

    G \'o mez-Guijarro , C., Elbaz , D., Xiao , M., et al. 2022, , 658, A43, 10.1051/0004-6361/202141615

    2022 doi

  38. [46]

    E., Labbe , I., Goulding , A

    Greene , J. E., Labbe , I., Goulding , A. D., et al. 2023, arXiv e-prints, arXiv:2309.05714, 10.48550/arXiv.2309.05714

    2023 doi arXiv

  39. [47]

    A., Kocevski , D

    Grogin , N. A., Kocevski , D. D., Faber , S. M., et al. 2011, , 197, 35, 10.1088/0067-0049/197/2/35

    2011 doi

  40. [48]

    M., van Hoof , P

    Gunasekera , C. M., van Hoof , P. A. M., Chatzikos , M., & Ferland , G. J. 2023, Research Notes of the American Astronomical Society, 7, 246, 10.3847/2515-5172/ad0e75

    2023 doi

  41. [49]

    N., Helton , J

    Hainline , K. N., Helton , J. M., Johnson , B. D., et al. 2023, arXiv e-prints, arXiv:2309.03250, 10.48550/arXiv.2309.03250

    2023 doi arXiv

  42. [50]

    N., Maiolino , R., Juodzbalis , I., et al

    Hainline , K. N., Maiolino , R., Juodzbalis , I., et al. 2024, arXiv e-prints, arXiv:2410.00100, 10.48550/arXiv.2410.00100

    2024 doi arXiv

  43. [51]

    2023, arXiv e-prints, arXiv:2303.11946, 10.48550/arXiv.2303.11946

    Harikane , Y., Zhang , Y., Nakajima , K., et al. 2023, arXiv e-prints, arXiv:2303.11946, 10.48550/arXiv.2303.11946

    2023 doi arXiv

  44. [52]

    2016, , 463, 2064, 10.1093/mnras/stw2107

    Hern \'a n-Caballero , A., Hatziminaoglou , E., Alonso-Herrero , A., & Mateos , S. 2016, , 463, 2064, 10.1093/mnras/stw2107

    2016 doi

  45. [53]

    Hunter , J. D. 2007, Computing in Science and Engineering, 9, 90, 10.1109/MCSE.2007.55

    2007 doi

  46. [54]

    2024, arXiv e-prints, arXiv:2409.07805, 10.48550/arXiv.2409.07805

    Inayoshi , K., & Maiolino , R. 2024, arXiv e-prints, arXiv:2409.07805, 10.48550/arXiv.2409.07805

    2024 doi arXiv

  47. [55]

    D., Leja , J., Conroy , C., & Speagle , J

    Johnson , B. D., Leja , J., Conroy , C., & Speagle , J. S. 2021, , 254, 22, 10.3847/1538-4365/abef67

    2021 doi

  48. [56]

    2022, , 659, A183, 10.1051/0004-6361/202141900

    Kerutt , J., Wisotzki , L., Verhamme , A., et al. 2022, , 659, A183, 10.1051/0004-6361/202141900

    2022 doi

  49. [57]

    2023, arXiv e-prints, arXiv:2312.03065, 10.48550/arXiv.2312.03065

    Killi , M., Watson , D., Brammer , G., et al. 2023, arXiv e-prints, arXiv:2312.03065, 10.48550/arXiv.2312.03065

    2023 doi

  50. [58]

    D., Barro , G., Faber , S

    Kocevski , D. D., Barro , G., Faber , S. M., et al. 2017, , 846, 112, 10.3847/1538-4357/aa8566

    2017 doi

  51. [59]

    D., Onoue , M., Inayoshi , K., et al

    Kocevski , D. D., Onoue , M., Inayoshi , K., et al. 2023, arXiv e-prints, arXiv:2302.00012, 10.48550/arXiv.2302.00012

    2023 doi arXiv

  52. [60]

    D., Finkelstein , S

    Kocevski , D. D., Finkelstein , S. L., Barro , G., et al. 2024, arXiv e-prints, arXiv:2404.03576, 10.48550/arXiv.2404.03576

    2024 doi arXiv

  53. [61]

    M., Faber , S

    Koekemoer , A. M., Faber , S. M., Ferguson , H. C., et al. 2011, , 197, 36, 10.1088/0067-0049/197/2/36

    2011 doi

  54. [62]

    I., Greene , J

    Kokorev , V., Caputi , K. I., Greene , J. E., et al. 2024 a , arXiv e-prints, arXiv:2401.09981, 10.48550/arXiv.2401.09981

    2024 doi arXiv

  55. [63]

    2024 b , arXiv e-prints, arXiv:2407.20320, 10.48550/arXiv.2407.20320

    Kokorev , V., Chisholm , J., Endsley , R., et al. 2024 b , arXiv e-prints, arXiv:2407.20320, 10.48550/arXiv.2407.20320

    2024 doi arXiv

  56. [64]

    Kron , R. G. 1980, , 43, 305, 10.1086/190669

    1980 doi

  57. [65]

    2023, , 616, 266, 10.1038/s41586-023-05786-2

    Labb \'e , I., van Dokkum , P., Nelson , E., et al. 2023, , 616, 266, 10.1038/s41586-023-05786-2

    2023 doi

  58. [66]

    E., Bezanson , R., et al

    Labbe , I., Greene , J. E., Bezanson , R., et al. 2023, arXiv e-prints, arXiv:2306.07320, 10.48550/arXiv.2306.07320

    2023 doi arXiv

  59. [67]

    L., Finkelstein , S

    Larson , R. L., Finkelstein , S. L., Kocevski , D. D., et al. 2023, arXiv e-prints, arXiv:2303.08918, 10.48550/arXiv.2303.08918

    2023 doi arXiv

  60. [68]

    C., Johnson , B

    Leja , J., Carnall , A. C., Johnson , B. D., Conroy , C., & Speagle , J. S. 2019, , 876, 3, 10.3847/1538-4357/ab133c

    2019 doi

  61. [69]

    Leung , G. C. K., Finkelstein , S. L., P \'e rez-Gonz \'a lez , P. G., et al. 2024, arXiv e-prints, arXiv:2411.12005. 2411.12005

    2024 arXiv

  62. [70]

    Li , Z., Inayoshi , K., Chen , K., Ichikawa , K., & Ho , L. C. 2024, arXiv e-prints, arXiv:2407.10760, 10.48550/arXiv.2407.10760

    2024 doi arXiv

  63. [71]

    2011, , 532, A90, 10.1051/0004-6361/201117107

    Lutz , D., Poglitsch , A., Altieri , B., et al. 2011, , 532, A90, 10.1051/0004-6361/201117107

    2011 doi

  64. [72]

    H., & Shi , Y

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

    2017 doi

  65. [73]

    H., et al

    Lyu , J., Alberts , S., Rieke , G. H., et al. 2024, , 966, 229, 10.3847/1538-4357/ad3643

    2024 doi

  66. [74]

    2013, , 553, A132, 10.1051/0004-6361/201321371

    Magnelli , B., Popesso , P., Berta , S., et al. 2013, , 553, A132, 10.1051/0004-6361/201321371

    2013 doi

  67. [75]

    2023, arXiv e-prints, arXiv:2308.01230, 10.48550/arXiv.2308.01230

    Maiolino , R., Scholtz , J., Curtis-Lake , E., et al. 2023, arXiv e-prints, arXiv:2308.01230, 10.48550/arXiv.2308.01230

    2023 doi arXiv

  68. [76]

    P., Brammer , G., et al

    Matthee , J., Naidu , R. P., Brammer , G., et al. 2024, , 963, 129, 10.3847/1538-4357/ad2345

    2024 doi

  69. [77]

    2019, , 622, A169, 10.1051/0004-6361/201833991

    Merlin , E., Pilo , S., Fontana , A., et al. 2019, , 622, A169, 10.1051/0004-6361/201833991

    2019 doi

  70. [78]

    2014, , 513, 394, 10.1038/nature13616

    Nelson , E., van Dokkum , P., Franx , M., et al. 2014, , 513, 394, 10.1038/nature13616

    2014 doi

  71. [79]

    M., Nikutta , R., Ivezi \'c , Z ., & Elitzur , M

    Nenkova , M., Sirocky , M. M., Nikutta , R., Ivezi \'c , Z ., & Elitzur , M. 2008, , 685, 160, 10.1086/590483

    2008 doi

  72. [80]

    A., Brammer , G., Naidu , R

    Oesch , P. A., Brammer , G., Naidu , R. P., et al. 2023, , 525, 2864, 10.1093/mnras/stad2411

    2023 doi

  73. [81]

    B., & Gunn , J

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

    1983 doi

  74. [82]

    Pasha , I., & Miller , T. B. 2023, The Journal of Open Source Software, 8, 5703, 10.21105/joss.05703

    2023 doi

  75. [83]

    Y., Ho , L

    Peng , C. Y., Ho , L. C., Impey , C. D., & Rix , H.-W. 2002, , 124, 266, 10.1086/340952

    2002 doi

  76. [84]

    G., Trujillo , I., Barro , G., et al

    P \'e rez-Gonz \'a lez , P. G., Trujillo , I., Barro , G., et al. 2008 a , , 687, 50, 10.1086/591843

    2008 doi

  77. [85]

    G., Rieke , G

    P \'e rez-Gonz \'a lez , P. G., Rieke , G. H., Egami , E., et al. 2005, , 630, 82, 10.1086/431894

    2005 doi

  78. [86]

    G., Rieke , G

    P \'e rez-Gonz \'a lez , P. G., Rieke , G. H., Villar , V., et al. 2008 b , , 675, 234, 10.1086/523690

    2008 doi

  79. [87]

    G., Egami , E., Rex , M., et al

    P \'e rez-Gonz \'a lez , P. G., Egami , E., Rex , M., et al. 2010, , 518, L15+, 10.1051/0004-6361/201014593

    2010 doi

  80. [88]

    G., Barro , G., Annunziatella , M., et al

    P \'e rez-Gonz \'a lez , P. G., Barro , G., Annunziatella , M., et al. 2023, , 946, L16, 10.3847/2041-8213/acb3a5

    2023 doi

  81. [89]

    G., Barro , G., Rieke , G

    P \'e rez-Gonz \'a lez , P. G., Barro , G., Rieke , G. H., et al. 2024, arXiv e-prints, arXiv:2401.08782, 10.48550/arXiv.2401.08782

    2024 doi arXiv

  82. [90]

    S., & Galametz , M

    Popping , G., Somerville , R. S., & Galametz , M. 2017, , 471, 3152, 10.1093/mnras/stx1545

    2017 doi

  83. [91]

    2017, Nature Astronomy, 1, 679, 10.1038/s41550-017-0232-z

    Ramos Almeida , C., & Ricci , C. 2017, Nature Astronomy, 1, 679, 10.1038/s41550-017-0232-z

    2017 doi

  84. [92]

    2024, arXiv e-prints, arXiv:2406.03518, 10.48550/arXiv.2406.03518

    Rieke , G., Alberts , S., Shivaei , I., et al. 2024, arXiv e-prints, arXiv:2406.03518, 10.48550/arXiv.2406.03518

    2024 doi arXiv

  85. [93]

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

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

    2009 doi

  86. [94]

    2014, , 562, A30, 10.1051/0004-6361/201322835

    Santini , P., Maiolino , R., Magnelli , B., et al. 2014, , 562, A30, 10.1051/0004-6361/201322835

    2014 doi

  87. [95]

    S \'e rsic , J. L. 1963, Boletin de la Asociacion Argentina de Astronomia La Plata Argentina, 6, 41

    1963

  88. [96]

    2015, , 219, 15, 10.1088/0067-0049/219/2/15

    Shibuya , T., Ouchi , M., & Harikane , Y. 2015, , 219, 15, 10.1088/0067-0049/219/2/15

    2015 doi

  89. [97]

    2007, , 461, 445, 10.1051/0004-6361:20065700

    Siebenmorgen , R., & Kr \"u gel , E. 2007, , 461, 445, 10.1051/0004-6361:20065700

    2007 doi

  90. [98]

    Stalevski , M., Fritz , J., Baes , M., Nakos , T., & Popovi \'c , L. C . 2012, , 420, 2756, 10.1111/j.1365-2966.2011.19775.x

    2012

  91. [99]

    M., et al

    Tacchella , S., Dekel , A., Carollo , C. M., et al. 2016, , 458, 242, 10.1093/mnras/stw303

    2016 doi

  92. [101]

    2017 b , , 834, 135, 10.3847/1538-4357/834/2/135

    ---. 2017 b , , 834, 135, 10.3847/1538-4357/834/2/135

    2017 doi

  93. [102]

    J., Finkelstein , S

    Taylor , A. J., Finkelstein , S. L., Kocevski , D. D., et al. 2024, arXiv e-prints, arXiv:2409.06772, 10.48550/arXiv.2409.06772

    2024 doi arXiv

  94. [103]

    J., Hewett , P

    Temple , M. J., Hewett , P. C., & Banerji , M. 2021, , 508, 737, 10.1093/mnras/stab2586

    2021 doi

  95. [104]

    Valentino , F., Brammer , G., Gould , K. M. L., et al. 2023, , 947, 20, 10.3847/1538-4357/acbefa

    2023 doi

  96. [105]

    C., & Varoquaux , G

    van der Walt , S., Colbert , S. C., & Varoquaux , G. 2011, Computing in Science and Engineering, 13, 22, 10.1109/MCSE.2011.37

    2011 doi

  97. [106]

    E., Richards , G

    Vanden Berk , D. E., Richards , G. T., Bauer , A., et al. 2001, , 122, 549, 10.1086/321167

    2001 doi

  98. [107]

    L., et al

    Wang , B., de Graaff , A., Davies , R. L., et al. 2024 a , arXiv e-prints, arXiv:2403.02304, 10.48550/arXiv.2403.02304

    2024 doi arXiv

  99. [108]

    2024 b , , 969, L13, 10.3847/2041-8213/ad55f7

    Wang , B., Leja , J., de Graaff , A., et al. 2024 b , , 969, L13, 10.3847/2041-8213/ad55f7

    2024 doi

  100. [109]

    2015, , 449, 361, 10.1093/mnras/stv303

    Wellons , S., Torrey , P., Ma , C.-P., et al. 2015, , 449, 361, 10.1093/mnras/stv303

    2015 doi

  101. [110]

    C., Tacchella , S., Maseda , M

    Williams , C. C., Tacchella , S., Maseda , M. V., et al. 2023, , 268, 64, 10.3847/1538-4365/acf130

    2023 doi

  102. [111]

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

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

    2024 doi

  103. [112]

    A., Buat , V., Casey , C

    Zavala , J. A., Buat , V., Casey , C. M., et al. 2023, , 943, L9, 10.3847/2041-8213/acacfe

    2023 doi

  104. [113]

    2015, , 450, 2327, 10.1093/mnras/stv740

    Zolotov , A., Dekel , A., Mandelker , N., et al. 2015, , 450, 2327, 10.1093/mnras/stv740

    2015 doi

Pith tools

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