Pith. sign in

REVIEW 2 major objections 3 minor 23 cited by

Stellar Population Inference with Prospector

T0 review · 2 major / 3 minor · reviewed 2026-05-10 · grok-4.3

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

Pith's one-line read Prospector infers stellar population parameters from UV-to-IR photometry and spectroscopy by forward modeling and Monte Carlo sampling of the posterior.

desk verdict Prospector is a practical software package for Bayesian SED fitting that handles complex models via MCMC, but it is an implementation of established techniques rather than a new theoretical advance. read the letter →

arxiv 2012.01426 v1 pith:XV5H257F submitted 2020-12-02 astro-ph.GA astro-ph.IM

Benjamin D. Johnson , Joel Leja , Charlie Conroy , Joshua S. Speagle This is my paper · ORCID
classification astro-ph.GAastro-ph.IM
keywords stellarpopulationsgalaxyevolutionphotometryspectroscopyMonteCarlosamplingstarformationhistorydustattenuationspectralenergydistribution
checked against Cost.FunctionalEquationFoundation.HierarchyEmergence
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

The paper presents Prospector as a software tool that builds detailed physical models of stellar populations and compares them directly to observed data across a wide range of wavelengths. Instead of relying on fixed grids of precomputed models, it uses statistical sampling to explore the full range of possible parameter values, even when those parameters are correlated. This approach supports more realistic descriptions of star formation histories, dust, and nebular emission than earlier methods allowed. A sympathetic reader would care because improved parameter recovery from existing and future datasets can sharpen our picture of how galaxies assemble and evolve.

What carries the argument

The Prospector code, which forward-models galaxy spectral energy distributions and uses Monte Carlo sampling to draw from the posterior distribution over parameters such as star-formation history, dust attenuation, and metallicity.

What would settle it

A test case in which Prospector yields parameter posteriors that systematically disagree with independent constraints from other methods, or in which the sampler fails to converge for typical datasets within feasible compute time.

Watch

Extended reading notes

Core claim

We present prospector, a flexible code for inferring stellar population parameters from photometry and spectroscopy spanning UV through IR wavelengths. This code is based on forward modeling the data and Monte Carlo sampling the posterior parameter distribution, enabling complex models and exploration of moderate dimensional parameter spaces.

Load-bearing premise

That the underlying stellar population synthesis models are realistic enough to match real galaxies and that Monte Carlo sampling can explore the correlated parameter space without prohibitive cost or convergence failure.

Editorial extensions

If this is right

  • Stellar population models with many correlated parameters can be fit directly to multi-wavelength data without grid interpolation.
  • Complex star-formation histories, dust properties, and nebular emission can be included in the inference.
  • Both photometric and spectroscopic observations from UV through IR can be modeled in a single framework.
  • Moderate-dimensional parameter spaces become accessible for routine analysis of individual galaxies.

Reading between the lines

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

  • If sampling remains efficient, the code could be applied to large surveys to map average star-formation histories across galaxy populations.
  • Future extensions might incorporate additional components such as active galactic nuclei or variable initial mass functions and test their impact on recovered parameters.
  • The same forward-modeling plus sampling strategy could be adapted to other wavelength regimes or to joint fits with dynamical or chemical data.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

2 major / 3 minor

Summary. The manuscript presents Prospector, a flexible Python code for inferring stellar population parameters (e.g., star-formation histories, dust attenuation, nebular emission) from UV-to-IR photometry and spectroscopy. The approach relies on forward modeling of the observed spectral energy distributions followed by Monte Carlo sampling of the posterior parameter distribution, enabling exploration of moderate-dimensional, correlated parameter spaces that cannot be handled by grid-based methods. The paper outlines the code's key ingredients and design philosophy, then demonstrates its use on both mock and real datasets.

Significance. If the implementation and validation hold, this is a useful contribution to galaxy evolution studies. It supplies an open, extensible tool that supports complex, physically motivated models and full posterior inference, addressing the growing need for flexible fitting of high-quality multi-wavelength data. The emphasis on forward modeling and reproducibility via shared code is a clear strength for the community.

major comments (2)
  1. [Demonstrations] Demonstrations section: the recovery of input parameters from mock data is shown but lacks quantitative metrics (e.g., bias, scatter, or coverage of credible intervals) and explicit convergence diagnostics for the Monte Carlo chains; without these, it is difficult to evaluate whether the claimed efficient exploration of parameter space is achieved in practice.
  2. [Code ingredients] Code description: the treatment of nebular emission and dust models is summarized at a high level, but the paper does not specify how degeneracies between these components and star-formation history parameters are mitigated or propagated in the posterior; this is load-bearing for the central claim of reliable inference in complex models.
minor comments (3)
  1. A table summarizing the free parameters, their priors, and default ranges would improve clarity and allow readers to reproduce the demonstrated fits.
  2. Figure captions should explicitly state the wavelength coverage, number of data points, and any data exclusion rules applied to the real datasets.
  3. The design philosophy discussion would benefit from a simple workflow diagram showing the forward-modeling and sampling steps.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their positive assessment of the manuscript and for the constructive comments, which have helped us improve the clarity of the demonstrations and the description of the inference framework. We address each major comment below.

read point-by-point responses

  1. Referee: Demonstrations section: the recovery of input parameters from mock data is shown but lacks quantitative metrics (e.g., bias, scatter, or coverage of credible intervals) and explicit convergence diagnostics for the Monte Carlo chains; without these, it is difficult to evaluate whether the claimed efficient exploration of parameter space is achieved in practice.

    Authors: We agree that the addition of quantitative metrics and convergence diagnostics will strengthen the demonstrations. In the revised manuscript we have added bias, scatter, and credible-interval coverage statistics for the recovered parameters from the mock data. We have also included explicit MCMC convergence diagnostics (Gelman-Rubin statistics and autocorrelation times) to document the sampling efficiency. revision: yes

  2. Referee: Code description: the treatment of nebular emission and dust models is summarized at a high level, but the paper does not specify how degeneracies between these components and star-formation history parameters are mitigated or propagated in the posterior; this is load-bearing for the central claim of reliable inference in complex models.

    Authors: The Bayesian posterior sampling framework does not attempt to mitigate degeneracies but instead propagates them by exploring the full joint posterior. We have expanded the code-ingredients section to state this explicitly: the forward-modeling approach combined with MCMC sampling naturally marginalizes over correlations among star-formation history, dust, and nebular parameters, yielding properly calibrated uncertainties and covariances in the posterior. revision: yes

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity; paper describes software tool

full rationale

The manuscript presents Prospector as a forward-modeling code using Monte Carlo sampling for stellar population inference from multi-wavelength data. No derivation chain exists that reduces a claimed prediction or first-principles result to its own inputs by construction, self-definition, or self-citation load-bearing. The contribution is the code architecture, parameter exploration philosophy, and demonstrations on mock/real data; these are self-contained tool descriptions without equations or results that loop back to fitted quantities. Central claims rest on the software design itself rather than any circular inference step.

Assumptions & free parameters 0 free parameters · 2 assumptions · 0 invented entities

This is a software-description paper; the central claim rests on the correctness of standard Bayesian inference and existing stellar population synthesis libraries rather than new axioms or entities introduced here.

assumptions (2)
  • domain assumption Stellar population synthesis models accurately predict observed spectral energy distributions when supplied with parameters for age, metallicity, star-formation history, dust, and nebular emission.
    Invoked throughout the abstract as the basis for forward modeling.
  • domain assumption Monte Carlo sampling can efficiently explore the posterior in moderate-dimensional parameter spaces for these models.
    Stated as the method enabling complex models.

how reviews work

0 comments
Cite this review

Pith. "Pith review of Stellar Population Inference with Prospector." pith.science (2026). https://pith.science/paper/XV5H257F

@misc{pith2026201201426,
  author       = {Pith},
  title        = {Pith review of: Stellar Population Inference with Prospector},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/XV5H257F}},
  note         = {Machine review of arXiv:2012.01426}
}
read the original abstract

Inference of the physical properties of stellar populations from observed photometry and spectroscopy is a key goal in the study of galaxy evolution. In recent years the quality and quantity of the available data has increased, and there have been corresponding efforts to increase the realism of the stellar population models used to interpret these observations. Describing the observed galaxy spectral energy distributions in detail now requires physical models with a large number of highly correlated parameters. These models do not fit easily on grids and necessitate a full exploration of the available parameter space. We present prospector, a flexible code for inferring stellar population parameters from photometry and spectroscopy spanning UV through IR wavelengths. This code is based on forward modeling the data and Monte Carlo sampling the posterior parameter distribution, enabling complex models and exploration of moderate dimensional parameter spaces. We describe the key ingredients of the code and discuss the general philosophy driving the design of these ingredients. We demonstrate some capabilities of the code on several datasets, including mock and real data.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 23 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 639 citations worldwide. Full citation record

  1. An emerging baryon cycle in a galaxy 500 million years after the Big Bang

    astro-ph.GA 2026-08 conditional novelty 7.0 of 10

    Fine-structure absorption lines in a z=9.3 galaxy yield the first direct electron-density measurement of a high-redshift outflow, giving a mass-loading factor among the highest ever measured.

  2. Misaligned or chaotic? A strong break of axial symmetry in the local LRD J1025 revealed with VLT/FORS2 spectropolarimetry

    astro-ph.GA 2026-07 accept novelty 7.0 of 10

    The local Little Red Dot J1025 shows a 48° polarisation-angle offset between continuum and broad H-alpha, requiring broken axial symmetry.

  3. Lyman-alpha haloes in the aftermath of reionisation

    astro-ph.GA 2026-07 accept novelty 7.0 of 10

    Lyα haloes around low-luminosity LAEs at z≥6 have exponential scale lengths ~3× smaller than at z~3 under matched intrinsic surface-brightness sensitivity, with stacks of non-detections showing no extended emission at high-z.

  4. FRB20250613A: a remarkable repeating FRB with apparent millisecond-timescale scattering variations

    astro-ph.HE 2026-07 unverdicted novelty 7.0 of 10

    FRB20250613A exhibits millisecond-timescale scattering variations, spectral depolarisation, and RM changes of ~300 rad m^{-2}, consistent with embedding in the dense stellar wind of a Be star binary companion in its l...

  5. EP260321a/SN 2026gzf: The Faintest Shock Breakout Associated with a Broad-Lined Supernova

    astro-ph.HE 2026-06 unverdicted novelty 7.0 of 10

    EP260321a is the faintest observed shock breakout tied to a broad-lined Type Ic supernova, interpreted as a choked weak outflow from a stripped star.

  6. AT 2024qfm: a luminous fast blue optical transient at a redshift of z = 0.2267 identified by Lasair-ZTF

    astro-ph.HE 2026-08 accept novelty 6.0 of 10

    A new luminous fast blue optical transient, AT 2024qfm, was discovered at z=0.2267 with a Lasair broker filter and matches AT 2018cow in luminosity and 13 day fade.

  7. FLAGS II: Constraining Galaxy Formation Models with Dimensionality Reduction of Direct Observables

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

    UMAP embeddings of 13-band JWST and HST photometry preserve enough information to statistically rank five galaxy formation models against observed GOODS-S galaxies, with JAGUAR performing best.

  8. CORN -- Chronometers of Relic Nature I: The first estimate of the expansion rate of the Universe using compact relic galaxies

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

    The first Hubble-parameter measurement using relic galaxies gives H(z=0.15)=85±53 km/s/Mpc and identifies alpha-element enhancement as a major systematic in the Dn4000 chronometer method.

  9. Breathing Fire: Hot Dust in the Big Three Dragons at z = 7.15

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

    The Big Three Dragons galaxy pair at z=7.15 has hot dust (T_d ≈ 78 K) and is almost fully obscured (f_obs ≈ 0.94), implying that UV-bright galaxies can host major hidden starbursts in the early universe.

  10. Outflows in the Early Universe: Neutral gas Absorption in Galaxies at z > 3 from low-resolution JWST Spectroscopy

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

    Excess Na I D absorption, interpreted as neutral-gas outflows, is detected in 20 of 811 galaxies at z>3 and rises steeply with stellar mass and quiescence.

  11. An (in)complete NIRSpec census of Balmer absorption in Type 1 AGN -- radiation-driven outflows in little red dots, quasars and variable stars

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

    About 44% of little red dots have hydrogen-alpha absorption from outflowing gas, implying radiatively driven outflows rather than static atmospheres.

  12. Compact Objects Merging with Stars as an Origin of Ultra-Long Gamma-Ray Bursts and Luminous Fast Blue Optical Transients

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

    SN 2011kl is broadly consistent with an LFBOT light-curve model and ULGRB hosts match LFBOT/LGRB environments, supporting a shared He-CO merger progenitor for a subset of both classes.

  13. Cold gas in a post-starburst pair at z ~ 1.4: major mergers as a pathway to quenching in the HeavyMetal survey

    astro-ph.GA 2025-06 conditional novelty 6.0 of 10

    ALMA detects about 10^9.7 solar masses of molecular gas in a merging post-starburst pair at z~1.4, while four similar galaxies are undetected, hinting that mergers may preserve gas while quenching star formation.

  14. ZTF SN Ia DR2 follow-up: early excess in Type Ia supernova light curves

    astro-ph.CO 2026-07 conditional novelty 5.0 of 10

    Up to about a quarter of Type Ia supernovae may have an early light-curve bump, but the candidate list depends strongly on the detection method and neither leading explosion model is clearly preferred.

  15. The DESI View of the Faint Radio Source Population in LoTSS DR2

    astro-ph.GA 2026-07 conditional novelty 5.0 of 10

    Probabilistic spectroscopic classification of 251k LoTSS radio sources yields the largest high-confidence sample and confirms LERGs accrete below ~1% Eddington while HERGs accrete above it.

  16. JWST spectroscopy of galaxies at $z>10$: Damped Ly$\alpha$ absorbers reveal efficient star formation and hidden redshift biases

    astro-ph.GA 2026-06 unverdicted novelty 5.0 of 10

    Analysis of JWST/NIRSpec Prism spectra for 25 z>=10 galaxies finds burstiness correlates with strong UV lines, short depletion times, and DLA-induced redshift biases of 0.39 and 0.14 with marginal impact on UV luminos...

  17. Late-time evolution of the interacting stripped-envelope supernova 2017dio

    astro-ph.HE 2026-06 unverdicted novelty 5.0 of 10

    Late-time data on SN 2017dio yield mass-loss rates of ~0.2 M_sun/yr peak and ~0.06 typical, with H-rich CSM from a companion and dust masses 0.001-0.02 M_sun, indicating sudden mass-loss increase.

  18. Star-formation variability on the star-forming main sequence during the Epoch of Reionization

    astro-ph.GA 2026-06 unverdicted novelty 5.0 of 10

    PSD modeling of SFR scatter at six timescales shows dominant variability on 10-30 Myr scales, stronger in lower-mass galaxies at z=3-8.

  19. Decadal pre-explosion activity and circumstellar interaction in a supernova

    astro-ph.HE 2026-06 unverdicted novelty 5.0 of 10

    SN 2026gzf exhibits early circumstellar interaction with 0.02 solar masses of material and decadal pre-explosion variability suggesting enhanced eruptive mass loss.

  20. JWST Advanced Deep Extragalactic Survey (JADES) Data Release 5: stellar population catalogue for galaxies in GOODS-N and GOODS-S

    astro-ph.GA 2026-05 unverdicted novelty 5.0 of 10

    JADES DR5 delivers a public catalog of Bayesian-inferred stellar masses, SFRs, SFHs, dust, metallicities, and AGN contributions for ~500k galaxies via Prospector with an evolving SFMS prior.

  21. Electromagnetic Follow-up of the Sub-Solar Mass Gravitational Wave Candidate S251112cm: Kilonova Constraints and a Coincident IIb Supernova

    astro-ph.HE 2026-05 unverdicted novelty 5.0 of 10

    No kilonova detected from sub-solar GW candidate S251112cm, but coincident IIb supernova SN 2025adtq yields suggestive evidence for the superkilonova channel, though inconclusive after accounting for chance coincidence.

  22. Bridging the gap between SLSNe and SE-SNe. Multi-wavelength analysis of the SLSN-Ib SN 2024jlc

    astro-ph.HE 2026-07 unverdicted novelty 4.0 of 10

    Multi-wavelength analysis of SN 2024jlc classifies it as an SLSN-Ib whose light curve fits circumstellar interaction or magnetar spin-down plus nickel decay, suggesting it bridges to SE-SNe via powering mechanism.

  23. Optical observations of candidate host galaxies of eight fast X-ray transients

    astro-ph.HE 2026-06 unverdicted novelty 4.0 of 10

    Optical imaging and BAGPIPES SED fitting of eight FXTs yields candidate hosts consistent with WD-IMBH TDEs or BNS mergers for most events, with one reclassified as a Galactic flare and evidence for diverse origins.

Reference graph

Works this paper leans on

97 extracted references · 97 canonical work pages · cited by 23 Pith papers

  1. [1]

    2011, ApJ, 737, 47

    Acquaviva, V., Gawiser, E., & Guaita, L. 2011, ApJ, 737, 47

    work page 2011

  2. [2]

    2020, ApJS, 249, 3

    Ahumada, R., Allende Prieto, C., Almeida, A., et al. 2020, ApJS, 249, 3

    work page 2020

  3. [3]

    2020, ApJS, 249, 5 Astropy Collaboration, Robitaille, T

    Alsing, J., Peiris, H., Leja, J., et al. 2020, ApJS, 249, 5 Astropy Collaboration, Robitaille, T. P., Tollerud, E. J., et al. 2013, A&A, 558, A33 Astropy Collaboration, Price-Whelan, A. M., Sip˝ ocz, B. M., et al. 2018, AJ, 156, 123

    work page 2020

  4. [4]

    C., Johnson, J

    Becker, J. C., Johnson, J. A., Vand erburg, A., & Morton, T. D. 2015, ApJS, 217, 29

    work page 2015

  5. [5]

    F., & de Jong, R

    Bell, E. F., & de Jong, R. S. 2001, ApJ, 550, 212

    work page 2001

  6. [6]

    R., Bershady, M

    Blanton, M. R., Bershady, M. A., Abolfathi, B., et al. 2017, AJ, 154, 28

    work page 2017

  7. [7]

    S., & Schlegel, D

    Bolton, A. S., & Schlegel, D. J. 2010, PASP, 122, 248

    work page 2010

  8. [8]

    2012, MNRAS, 427, 127

    Bressan, A., Marigo, P., Girardi, L., et al. 2012, MNRAS, 427, 127

    work page 2012

Show all 97 references
  1. [9]

    2003, MNRAS, 344, 1000

    Bruzual, G., & Charlot, S. 2003, MNRAS, 344, 1000

    2003

  2. [10]

    2005, MNRAS, 360, 1413

    Burgarella, D., Buat, V., & Iglesias-P´ aramo, J. 2005, MNRAS, 360, 1413

    2005

  3. [11]

    J., Conroy, C., & Johnson, B

    Byler, N., Dalcanton, J. J., Conroy, C., & Johnson, B. D. 2017, ApJ, 840, 44

    2017

  4. [12]

    C., et al

    Calzetti, D., Armus, L., Bohlin, R. C., et al. 2000, ApJ, 533, 682

    2000

  5. [13]

    2017, MNRAS, 466, 798

    Cappellari, M. 2017, MNRAS, 466, 798

    2017

  6. [14]

    2004, PASP, 116, 138

    Cappellari, M., & Emsellem, E. 2004, PASP, 116, 138

    2004

  7. [15]

    A., Clayton, G

    Cardelli, J. A., Clayton, G. C., & Mathis, J. S. 1989, ApJ, 345, 245

    1989

  8. [16]

    C., McLure, R

    Carnall, A. C., McLure, R. J., Dunlop, J. S., & Dav´ e, R. 2018, MNRAS, 480, 4379

    2018

  9. [17]

    G., et al

    Carretta, E., Bragaglia, A., Gratton, R. G., et al. 2009, A&A, 505, 117

    2009

  10. [18]

    Charlot, S., & Fall, S. M. 2000, ApJ, 539, 718

    2000

  11. [19]

    2016, MNRAS, 462, 1415

    Chevallard, J., & Charlot, S. 2016, MNRAS, 462, 1415

    2016

  12. [20]

    2016, ApJ, 823, 102 Cid Fernandes, R., Mateus, A., Sodr´ e, L., Stasi´ nska, G., &

    Choi, J., Dotter, A., Conroy, C., et al. 2016, ApJ, 823, 102 Cid Fernandes, R., Mateus, A., Sodr´ e, L., Stasi´ nska, G., &

    2016

  13. [21]

    Gomes, J. M. 2005, MNRAS, 358, 363

    2005

  14. [22]

    2013, ARA&A, 51, 393

    Conroy, C. 2013, ARA&A, 51, 393

    2013

  15. [23]

    Conroy, C., & Gunn, J. E. 2010, ApJ, 712, 833

    2010

  16. [24]

    E., & White, M

    Conroy, C., Gunn, J. E., & White, M. 2009, ApJ, 699, 486

    2009

  17. [25]

    G., & Lind, K

    Conroy, C., Villaume, A., van Dokkum, P. G., & Lind, K. 2018, ApJ, 854, 139

    2018

  18. [26]

    Conroy, C., White, M., & Gunn, J. E. 2010, ApJ, 708, 58

    2010

  19. [27]

    A., Conroy, C., van Dokkum, P., & Speagle, J

    Cook, B. A., Conroy, C., van Dokkum, P., & Speagle, J. S. 2019, ApJ, 876, 78

    2019

  20. [28]

    2007, AJ, 133, 468 da Cunha, E., Charlot, S., & Elbaz, D

    Cordier, D., Pietrinferni, A., Cassisi, S., & Salaris, M. 2007, AJ, 133, 468 da Cunha, E., Charlot, S., & Elbaz, D. 2008, MNRAS, 388, 1595

    2007

  21. [29]

    E., & Torrey, P

    Diemer, B., Sparre, M., Abramson, L. E., & Torrey, P. 2017, ApJ, 839, 26

    2017

  22. [30]

    1997, NewA, 2, 397

    Dolphin, A. 1997, NewA, 2, 397

    1997

  23. [31]

    T., & Li, A

    Draine, B. T., & Li, A. 2007, ApJ, 657, 810

    2007

  24. [32]

    2018, ApJL, 852, L7

    Ebeling, H., Stockmann, M., Richard, J., et al. 2018, ApJL, 852, L7

    2018

  25. [33]

    J., Stanway, E

    Eldridge, J. J., Stanway, E. R., Xiao, L., et al. 2017, PASA, 34, e058

    2017

  26. [34]

    Faber, S. M. 1977, in Evolution of Galaxies and Stellar Populations, ed. B. M. Tinsley & D. C. Larson, Richard B. Gehret, 157

    1977

  27. [35]

    J., Porter, R

    Ferland, G. J., Porter, R. L., van Hoof, P. A. M., et al. 2013, RMxAA, 49, 137

    2013

  28. [36]

    P., & Bridges, M

    Feroz, F., Hobson, M. P., & Bridges, M. 2009, MNRAS, 398, 1601

    2009

  29. [37]

    1997, A&A, 500, 507

    Fioc, M., & Rocca-Volmerange, B. 1997, A&A, 500, 507

    1997

  30. [38]

    2014, Zenodo, doi:10.5281/zenodo.12157

    Foreman-Mackey, D., Sick, J., & Johnson, B. 2014, Zenodo, doi:10.5281/zenodo.12157

    2014 doi

  31. [39]

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

    Foreman-Mackey, D., Hogg, D. W., Lang, D., & Goodman, J. 2013, PASP, 125, 306

    2013

  32. [40]

    D., Yang, Y., Zabludoff, A., et al

    French, K. D., Yang, Y., Zabludoff, A., et al. 2015, ApJ, 801, 1

    2015

  33. [41]

    S., I., Hunter, D

    Gallagher, J. S., I., Hunter, D. A., & Tutukov, A. V. 1984, ApJ, 284, 544

    1984

  34. [42]

    D., Oemler, A., Dressler, A., et al

    Gladders, M. D., Oemler, A., Dressler, A., et al. 2013, ApJ, 770, 64

    2013

  35. [43]

    2010, Communications in Applied Mathematics and Computational Science, 5, 65

    Goodman, J., & Weare, J. 2010, Communications in Applied Mathematics and Computational Science, 5, 65

    2010

  36. [44]

    D., Roman-Duval, J., Bot, C., et al

    Gordon, K. D., Roman-Duval, J., Bot, C., et al. 2014, ApJ, 797, 85

    2014

  37. [45]

    2007, MNRAS, 381, 187

    Goto, T. 2007, MNRAS, 381, 187

    2007

  38. [46]

    P., Goulding, A

    Greco, J. P., Goulding, A. D., Greene, J. E., et al. 2018, ApJ, 866, 112

    2018

  39. [47]

    E., Siegmund, W

    Gunn, J. E., Siegmund, W. A., Mannery, E. J., et al. 2006, AJ, 131, 2332 26 Johnson et al

    2006

  40. [48]

    R., Millman, K

    Harris, C. R., Millman, K. J., van der Walt, S. J., et al. 2020, Nature, 585, 357–362

    2020

  41. [49]

    2001, ApJS, 136, 25

    Harris, J., & Zaritsky, D. 2001, ApJS, 136, 25

    2001

  42. [50]

    Harris, W. E. 1996, AJ, 112, 1487

    1996

  43. [51]

    F., Jimenez, R., & Lahav, O

    Heavens, A. F., Jimenez, R., & Lahav, O. 2000, MNRAS, 317, 965

    2000

  44. [52]

    2019, Statistics and Computing, 29, 891

    Higson, E., Handley, W., Hobson, M., & Lasenby, A. 2019, Statistics and Computing, 29, 891

    2019

  45. [53]

    W., Bovy, J., & Lang, D

    Hogg, D. W., Bovy, J., & Lang, D. 2010, arXiv e-prints, arXiv:1008.4686

    2010

  46. [54]

    2007, Computing in Science & Engineering, 9, 90

    Hunter, J. 2007, Computing in Science & Engineering, 9, 90

    2007

  47. [55]

    2017, ApJ, 838, 127

    Iyer, K., & Gawiser, E. 2017, ApJ, 838, 127

    2017

  48. [56]

    G., Gawiser, E., Faber, S

    Iyer, K. G., Gawiser, E., Faber, S. M., et al. 2019, ApJ, 879, 116

    2019

  49. [57]

    Johnson, B. D. 2019, SEDPY: Modules for storing and operating on astronomical source spectral energy distribution, , , ascl:1905.026 Kauffmann, G., Heckman, T. M., White, S. D. M., et al. 2003, MNRAS, 341, 33

    2019

  50. [58]

    D., Benson, A

    Kelson, D. D., Benson, A. J., & Abramson, L. E. 2016, arXiv e-prints, arXiv:1610.06566

    2016

  51. [59]

    J., Groves, B., Kauffmann, G., & Heckman, T

    Kewley, L. J., Groves, B., Kauffmann, G., & Heckman, T. 2006, MNRAS, 372, 961

    2006

  52. [60]

    2009, A&A, 501, 1269

    Koleva, M., Prugniel, P., Bouchard, A., & Wu, Y. 2009, A&A, 501, 1269

    2009

  53. [61]

    2008, MNRAS, 385, 1998

    Soubiran, C. 2008, MNRAS, 385, 1998

    2008

  54. [62]

    2013, ApJL, 775, L16 Lan¸ con, A., & Wood, P

    Kriek, M., & Conroy, C. 2013, ApJL, 775, L16 Lan¸ con, A., & Wood, P. R. 2000, A&AS, 146, 217 Le Borgne, D., Rocca-Volmerange, B., Prugniel, P., et al. 2004, A&A, 425, 881

    2013

  55. [63]

    L., van Dyk, D

    Lee, H., Kashyap, V. L., van Dyk, D. A., et al. 2011, ApJ, 731, 126

    2011

  56. [64]

    C., et al

    Lee, S.-K., Idzi, R., Ferguson, H. C., et al. 2009, ApJS, 184, 100

    2009

  57. [65]

    Speagle, J. S. 2019, ApJ, 876, 3

    2019

  58. [66]

    D., Conroy, C., & van Dokkum, P

    Leja, J., Johnson, B. D., Conroy, C., & van Dokkum, P. 2018, ApJ, 854, 62

    2018

  59. [67]

    2020, arXiv e-prints, arXiv:2006.03599

    Lower, S., Narayanan, D., Leja, J., et al. 2020, arXiv e-prints, arXiv:2006.03599

    2020

  60. [68]

    2005, MNRAS, 362, 799

    Maraston, C. 2005, MNRAS, 362, 799

    2005

  61. [69]

    2008, A&A, 482, 883

    Marigo, P., Girardi, L., Bressan, A., et al. 2008, A&A, 482, 883

    2008

  62. [70]

    1994, A&AS, 103, 97

    Charbonnel, C. 1994, A&AS, 103, 97

    1994

  63. [71]

    2017, ApJL, 845, L8

    Nicholl, M., Berger, E., Margutti, R., et al. 2017, ApJL, 845, L8

    2017

  64. [72]

    2009, A&A, 507, 1793

    Noll, S., Burgarella, D., Giovannoli, E., et al. 2009, A&A, 507, 1793

    2009

  65. [73]

    2006, MNRAS, 365, 46

    Ocvirk, P., Pichon, C., Lan¸ con, A., & Thi´ ebaut, E. 2006, MNRAS, 365, 46

    2006

  66. [74]

    A., Brammer, G., van Dokkum, P

    Oesch, P. A., Brammer, G., van Dokkum, P. G., et al. 2016, ApJ, 819, 129 Oyarz´ un, G. A., Bundy, K., Westfall, K. B., et al. 2019, ApJ, 880, 111 Pacifici, C., Kassin, S. A., Weiner, B., Charlot, S., &

    2016

  67. [75]

    Gardner, J. P. 2013, ApJL, 762, L15 Pacifici, C., da Cunha, E., Charlot, S., et al. 2015, MNRAS, 447, 786

    2013

  68. [76]

    J., Laine, S., et al

    Pandya, V., Romanowsky, A. J., Laine, S., et al. 2018, ApJ, 858, 29 Patr´ ıcio, V., Richard, J., Carton, D., et al. 2018, MNRAS, 477, 18

    2018

  69. [77]

    Perez, F., & Granger, B. E. 2007, Computing in Science & Engineering, 9, 21

    2007

  70. [78]

    2009, International Journal of Computational Science and Engineering, 4, 296

    Peterson, P. 2009, International Journal of Computational Science and Engineering, 4, 296

    2009

  71. [79]

    2012, MNRAS, 422, 3285

    Pforr, J., Maraston, C., & Tonini, C. 2012, MNRAS, 422, 3285

    2012

  72. [80]

    M., Charlot, S., et al

    Salim, S., Rich, R. M., Charlot, S., et al. 2007, ApJS, 173, 267 S´ anchez-Bl´ azquez, P., Peletier, R. F., Jim´ enez-Vicente, J., et al. 2006, MNRAS, 371, 703

    2007

  73. [81]

    P., Rose, J

    Schiavon, R. P., Rose, J. A., Courteau, S., & MacArthur, L. A. 2005, ApJS, 160, 163

    2005

  74. [82]

    H., Conroy, C., et al

    Simha, V., Weinberg, D. H., Conroy, C., et al. 2014, arXiv e-prints, arXiv:1404.0402

    2014

  75. [83]

    2004, in American Institute of Physics Conference Series, Vol

    Skilling, J. 2004, in American Institute of Physics Conference Series, Vol. 735, American Institute of Physics Conference Series, ed. R. Fischer, R. Preuss, & U. V. Toussaint, 395–405

    2004

  76. [84]

    A., Gunn, J

    Smee, S. A., Gunn, J. E., Uomoto, A., et al. 2013, AJ, 146, 32

    2013

  77. [85]

    Speagle, J. S. 2020, MNRAS, 493, 3132

    2020

  78. [86]

    C., & Caplar, N

    Tacchella, S., Forbes, J. C., & Caplar, N. 2020, MNRAS, 497, 698

    2020

  79. [87]

    Tinsley, B. M. 1980, FCPh, 5, 287

    1980

  80. [88]

    F., Jimenez, R., & Panter, B

    Tojeiro, R., Heavens, A. F., Jimenez, R., & Panter, B. 2007, MNRAS, 381, 1252 Prospector 27

    2007

  81. [89]

    Villaume, A., Conroy, C., & Johnson, B. D. 2015, ApJ, 806, 82

    2015

  82. [90]

    E., et al

    Virtanen, P., Gommers, R., Oliphant, T. E., et al. 2020, Nature Methods, 17, 261

    2020

  83. [91]

    2014, MNRAS, 444, 1518

    Vogelsberger, M., Genel, S., Springel, V., et al. 2014, MNRAS, 444, 1518

    2014

  84. [92]

    2011, Ap&SS, 331, 1

    Walcher, J., Groves, B., Budav´ ari, T., & Dale, D. 2011, Ap&SS, 331, 1

    2011

  85. [93]

    R., Dalcanton, J

    Weisz, D. R., Dalcanton, J. J., Williams, B. F., et al. 2011, ApJ, 739, 5

    2011

  86. [94]

    M., Gonzalez-Perez, V., Lacey, C

    Wilkins, S. M., Gonzalez-Perez, V., Lacey, C. G., & Baugh, C. M. 2012, MNRAS, 427, 1490

    2012

  87. [95]

    N., & Gordon, K

    Witt, A. N., & Gordon, K. D. 2000, ApJ, 528, 799

    2000

  88. [96]

    A., Faber, S

    Yan, R., Newman, J. A., Faber, S. M., et al. 2006, ApJ, 648, 281

    2006

  89. [97]

    A., Zabludoff, A

    Yang, Y., Tremonti, C. A., Zabludoff, A. I., & Zaritsky, D. 2006, ApJL, 646, L33

    2006

Pith tools

Reviewed May 10, 2026 · model on record in the stance chip above.