Bibliography
Consolidated reference list for all papers cited in hod_mod.
Entries are grouped by topic and ordered chronologically within each group
to show the progression of the field.
Cosmology and Power Spectra
Foundation papers for the cosmological framework, linear power spectrum
computation, and non-linear emulators used in hod_mod.
Eisenstein D.J. & Hu W. 1998, ApJ 496, 605. Fitting formulae for the linear matter power spectrum without CDM (transfer function). arXiv:astro-ph/9709066
Lewis A., Challinor A. & Lasenby A. 2000, ApJ 538, 473.
CAMB: Code for Anisotropies in the Microwave Background; hod_mod uses CAMB for
linear \(P(k)\) via LinearPowerSpectrum.
arXiv:astro-ph/9911177
Planck Collaboration 2018, A&A 641, A6.
Planck 2018 cosmological parameters (default cosmology in hod_mod).
arXiv:1807.06209
Aletheia Collaboration 2025.
Non-linear matter power spectrum emulator used via NonLinearPowerSpectrum.
arXiv:2511.13826
Halo Model Framework
The halo model provides the theoretical basis for connecting dark matter halos
to observed galaxy statistics. These foundational works established the framework
implemented in hod_mod.
Marika Asgari, Alexander J. Mead, Catherine Heymans OJAp 6E 39A 2023. The halo model for cosmology: a pedagogical review. arXiv:2303.08752
Seljak U. & Warren M.S. 2004, MNRAS 355, 129. First complete halo model predictions for galaxy clustering including scale-dependent bias; established the 1-halo + 2-halo decomposition. arXiv:astro-ph/0403698
Cooray A. & Sheth R. 2002, Phys. Rep. 372, 1.
Definitive review of halo models of large-scale structure; reference for the
1-halo / 2-halo power spectrum decomposition used throughout hod_mod.
arXiv:astro-ph/0206508
Halo Mass Function and Bias
Calibrations of the halo mass function and halo bias, from early analytic approximations through simulation-calibrated fits to modern emulators.
Press W.H. & Schechter P. 1974, ApJ 187, 425. Original analytic derivation of the dark matter halo abundance; historical foundation for all subsequent HMF work.
Sheth R.K. & Tormen G. 1999, MNRAS 308, 119. Ellipsoidal collapse HMF; improved agreement with N-body simulations over Press-Schechter. arXiv:astro-ph/9901122
Jenkins A. et al. 2001, MNRAS 321, 372. First large N-body calibration of the HMF across multiple cosmologies. arXiv:astro-ph/0005260
Tinker J.L. et al. 2008, ApJ 688, 709.
Precision calibration of the HMF from N-body simulations across 11 orders
of magnitude in mass; default HMF in hod_mod (tinker08).
arXiv:0803.2706
Tinker J.L. et al. 2010, ApJ 724, 878.
Calibration of the large-scale linear halo bias corresponding to the
Tinker+2008 HMF; used in the 2-halo term of hod_mod.
arXiv:1001.3162
Chen Z. et al. 2025, Science China: Physics, Mechanics & Astronomy 68, 9513.
CEmulator v2.0: Gaussian-Process emulator of halo statistics (HMF, matter power
spectrum, halo-matter cross-correlation) for CSST cosmologies spanning
\(w_0w_a\nu`CDM; ``make_hmf("csst")`\) in hod_mod.
ADS
Shen X. et al. 2025, JCAP 2025 (03), 056.
Aemulus-ν: Gaussian-Process HMF emulator for massive-neutrino wCDM cosmologies,
calibrated on 150 high-resolution N-body simulations for
\(M \geq 10^{13}\,M_\odot/h\), \(z \leq 2\); make_hmf("aemulusnu")
in hod_mod.
arXiv:2410.00913
Nishimichi T. et al. 2019, ApJ 884, 29. Dark Emulator: Gaussian Process emulation of halo clustering statistics; enables rapid HOD predictions for arbitrary ΛCDM cosmologies. arXiv:1811.09504
Halo Profiles and Concentration
From the original NFW profile through concentration calibrations to the projected surface-mass-density formulas used in lensing predictions.
Navarro J.F., Frenk C.S. & White S.D.M. 1997, ApJ 490, 493.
Universal NFW density profile from hierarchical clustering simulations;
the default halo profile in hod_mod.
arXiv:astro-ph/9611107
Einasto J. 1965, Trudy Astrofizicheskogo Instituta Alma-Ata 5, 87.
Einasto profile; alternative to NFW available in hod_mod.
Wright C.O. & Brainerd T.G. 2000, ApJ 534, 34.
Analytical formulas for weak-lensing shear and convergence of NFW halos;
basis for \(\Delta\Sigma(R)\) computations in hod_mod.
arXiv:astro-ph/9908213
Bryan G.L. & Norman M.L. 1998, ApJ 495, 80. Virial overdensity \(\Delta_{\rm vir}(z)\) fitting formula used in halo mass–concentration conversions. arXiv:astro-ph/9710107
Diemer B. & Joyce M. 2019, ApJ 871, 168.
Accurate physical model for halo concentrations; default concentration–mass
relation in hod_mod via colossus (diemer19).
arXiv:1809.07326
HOD Models
The halo occupation distribution (HOD) connects galaxies to dark matter halos.
These references cover the foundational formalism through the models implemented
in hod_mod/connection/hod/.
Berlind A.A. & Weinberg D.H. 2002, ApJ 575, 587. Foundational HOD formalism paper; introduced the conditional probability of finding \(N\) galaxies in a halo of mass \(M\) as the core statistic. arXiv:astro-ph/0109001
Zheng Z. et al. 2005, ApJ 633, 791. HOD models with explicit separation of central and satellite galaxies; introduced the \(\langle N_{\rm cen}\rangle + \langle N_{\rm sat}\rangle\) decomposition that underlies all modern HOD codes. arXiv:astro-ph/0408564
Zheng Z. et al. 2007, ApJ 667, 760.
HOD fits to DEEP2 and SDSS galaxy samples across redshifts; the parametrization
HODModel in hod_mod follows Zheng+2007.
arXiv:astro-ph/0703457
More S. et al. 2015, ApJ 806, 2.
HOD analysis of BOSS CMASS using \(w_p + \Delta\Sigma\); introduced the
incompleteness correction and \(\kappa\) satellite cut.
Reference model for MoreHODModel in hod_mod.
arXiv:1407.1856
van Uitert E. et al. 2016, MNRAS 459, 3251.
HOD fits using a Gaussian conditional stellar mass function;
reference for VanUitert16CSMFModel in hod_mod.
arXiv:1601.06791
Zu Y. & Mandelbaum R. 2015, MNRAS 454, 1161.
iHOD model: inverse SHMR approach to galaxy–halo connection via SDSS
clustering and galaxy–galaxy lensing; reference for
ZuMandelbaum15HODModel in hod_mod.
arXiv:1505.02781
Zu Y. & Mandelbaum R. 2016, MNRAS 457, 4360.
iHOD quenching model: Weibull CDF red fractions for centrals and satellites;
reference for ZuMandelbaum16QuenchingModel in hod_mod.
arXiv:1509.06758
Guo H. et al. 2018, ApJ 858, 30.
Incompleteness-corrected SHMR (ICSMF) with broken power-law for SDSS main;
reference for Guo18ICSMFModel in hod_mod.
arXiv:1804.01993
Guo H. et al. 2019, ApJ 871, 147.
15-parameter ICSMF for eBOSS ELGs including quenched fraction;
reference for Guo19ICSMFModel in hod_mod.
arXiv:1810.05318
Zacharegkas G. et al. 2025.
Kravtsov SHMR with threshold scatter; reference for
Zacharegkas25HODModel in hod_mod.
arXiv:2506.22367
Stellar-to-Halo Mass Relations and SHAM
Empirical and simulation-based constraints on how stellar mass maps to halo mass,
used in SHAM models (hod_mod/connection/sham.py).
Moster B.P., Naab T. & White S.D.M. 2013, MNRAS 428, 3121.
Empirical SMHM relation via abundance matching across redshifts;
reference for smhm_moster13 in hod_mod.
arXiv:1205.5807
Behroozi P.S., Wechsler R.H. & Conroy C. 2013, ApJ 770, 57.
SMHM relation from average star formation histories; reference for
smhm_behroozi13 in hod_mod.
arXiv:1207.6105
Girelli G. et al. 2020, A&A 634, A135.
Stellar-to-halo mass relation over the past 12 Gyr;
reference for smhm_girelli20 in hod_mod.
arXiv:2001.02230
Galaxy Clustering and Projected Correlation Function
Theoretical and observational works on the projected correlation function \(w_p(r_p)\) and the power-law approximations used for model validation.
Davis M. & Peebles P.J.E. 1983, ApJ 267, 465. Introduced the projected correlation function \(w_p(r_p)\) via line-of-sight integration to \(\pi_{\rm max}\); fundamental observable in HOD fitting.
Hamilton A.J.S. 1992, ApJ 385, L5. Linear redshift-space distortions; basis for RSD corrections in projected correlation functions.
Galaxy-Galaxy Lensing and Excess Surface Mass Density
From the first GGL detections through modern combined HOD+lensing analyses
covering the full range of scales accessible to hod_mod.
Bartelmann M. & Schneider P. 2001, Phys. Rep. 340, 291. Comprehensive review of weak gravitational lensing theory; reference for \(\Delta\Sigma(R)\) and convergence formulas. arXiv:astro-ph/9912508
Mandelbaum R. et al. 2005, MNRAS 361, 1287. First SDSS galaxy-galaxy lensing analysis measuring halo masses and satellite fractions across galaxy samples. arXiv:astro-ph/0501048
Mandelbaum R. et al. 2006, MNRAS 372, 758. SDSS GGL: density profiles of galaxy groups and clusters from weak lensing; demonstrated NFW profile consistency at group scales. arXiv:astro-ph/0605476
Leauthaud A. et al. 2017, MNRAS 467, 3024. “Lensing is Low”: BOSS CMASS lensing amplitude 20–40% below predictions from clustering; established the lensing–clustering discrepancy as a key diagnostic. arXiv:1611.08606
Miyatake H. et al. 2022, Phys. Rev. D 106, 083520. Emulator-based HOD analysis of HSC-Y1 × SDSS: joint \(w_p + \Delta\Sigma\) at 3–30 \(h^{-1}`Mpc; :math:`S_8 = 0.795^{+0.049}_{-0.042}\). Used for pipeline consistency validation. arXiv:2111.02419
Lange J.U. et al. 2023, MNRAS 520, 5373. Full-scale \(w_p + \Delta\Sigma\) (0.4–63 \(h^{-1}`Mpc) in BOSS × KiDS+DES; :math:`S_8 = 0.792 \pm 0.022\); includes small-scale HOD constraints. arXiv:2301.08692
Heydenreich S. et al. 2025. “Lensing Without Borders”: \(\Delta\Sigma\) and \(w_p\) from DESI-DR1 cross-correlated with DES, KiDS, and HSC; data release for KP7 cosmological analyses. arXiv:2506.21677
Lange J.U. et al. 2025. Cosmological constraints from full-scale clustering + lensing with DESI-DR1: \(S_8 = 0.794 \pm 0.023\), \(\Omega_m = 0.295 \pm 0.012\). arXiv:2512.15962
Intrinsic Alignments
Progression from the first tidal alignment models through the non-linear alignment (NLA) model and its extensions, to modern observational constraints.
Catelan P., Kamionkowski M. & Blandford R.D. 2001, MNRAS 320, L7. First tidal shear model for intrinsic alignments of elliptical galaxies; foundation of the linear alignment (LA) model. arXiv:astro-ph/0012040
Hirata C.M. & Seljak U. 2004, Phys. Rev. D 70, 063526. Derived the gravitational torquing model and showed LA/NLA is the dominant systematic in weak lensing; NLA uses \(P_{\rm lin}(k)\) — not \(P_{\rm nl}\). arXiv:astro-ph/0406275
Brown M.L. et al. 2002, MNRAS 333, 501. Observational measurement of intrinsic alignments; defines \(C_1 \rho_{\rm crit,0} = 0.0134\) used in the NLA amplitude. arXiv:astro-ph/0208084
Bridle S. & King L. 2007, New J. Phys. 9, 444.
NLA model applied to dark energy forecasts; showed IA can bias \(w\)
by ~50% if ignored; reference for \(A_{\rm IA}\) parametrisation in
hod_mod.
arXiv:0705.0166
Blazek J. et al. 2019, Phys. Rev. D 100, 103506. “Beyond linear galaxy alignments”: perturbative framework including quadratic tidal terms; order-unity corrections at small scales; FAST-PT implementation. arXiv:1708.09247
DESI Collaboration 2025. DESI KP6: intrinsic alignment of BGS-like lenses; \(A_{\rm IA} \sim 0.3{-}1.5\) for stellar-mass-selected samples. arXiv:2509.04552
Baryon Effects on the Matter Power Spectrum
Baryonic feedback suppresses the matter power spectrum at small scales.
These works calibrate and model the suppression, motivating
the baryon fraction and gas concentration models in hod_mod.
van Daalen M.P. et al. 2011, MNRAS 415, 3649. OWLS simulations: AGN feedback suppresses \(P(k)\) by up to 30% at \(k \gtrsim 1~h/{\rm Mpc}\); first large systematic study. arXiv:1104.1174
Schneider A. & Teyssier R. 2015, JCAP 12, 049. Baryon correction model (BCM): analytic prescription for baryonic redistribution based on gas fraction and stellar feedback. arXiv:1510.06034
Mead A.J. et al. 2015, MNRAS 454, 1958. HMcode: accurate halo model for non-linear \(P(k)\) including baryonic feedback; models gas as an NFW profile with reduced concentration \(c_{\rm gas} = \eta\,c_{\rm DM}\). arXiv:1505.07098
McCarthy I.G. et al. 2017, MNRAS 465, 2936. BAHAMAS: calibrated hydro simulations for large-scale structure cosmology; provides gas fractions and profiles at group–cluster scales. arXiv:1612.06090
Contreras S. et al. 2024.
IllustrisTNG / MillenniumTNG: baryonic effects on halo concentration;
broken power-law fit \(c_{\rm hydro}/c_{\rm DMO}\) (Table 2) used in
hod_mod for gas concentration ratio \(\eta(M)\).
arXiv:2409.01758
Schaller M. et al. 2025, MNRAS 539, 1337. FLAMINGO: Gaussian process emulator for baryon suppression of \(P(k)\); covers diverse feedback models to sub-percent accuracy. arXiv:2410.17109
Schaller M. & Schaye J. 2025, MNRAS (accepted).
Analytic redshift-independent sigmoid parametrisation of baryonic effects on
\(P(k)\) from FLAMINGO; motivates the BaryonFractionSigmoid model.
arXiv:2504.15633
FLAMINGO Collaboration 2025.
FLAMINGO gas fraction measurements at group scales;
\(f_b(M) < f_{b,\rm cosmic}\) as implemented in hod_mod.
arXiv:2510.25419 (verify: same ID as [Lange2025phz])
FLAMINGO Collaboration 2025. FLAMINGO hot gas profiles: \(c_{\rm gas} < c_{\rm DM}\) at group–cluster scales; motivates the gas concentration ratio \(\eta(M)\). arXiv:2509.10230
Siegel J. et al. 2025, MNRAS (submitted). X-ray gas fractions + kSZ profiles + GGL: \(10 \pm 2\%\) matter power suppression at \(k = 1~h/{\rm Mpc}\); validates baryon fraction model. arXiv:2512.02954
Veenema M. et al. 2026. Closure-radius model for the baryon fraction in halos. arXiv:2603.13095
Pfeifer S. et al. 2025. Machine-learning gas profiles: halo mass as primary driver beyond \(M_{\rm BH}\). arXiv:2512.09021
Gas Profiles and Cross-Correlations
Papers providing the gas profile parametrisations and the observational benchmarks for galaxy × tSZ and galaxy × soft X-ray cross-correlations.
Arnaud M., Pratt G.W., Piffaretti R. et al. 2010, A&A 517, A92.
Universal pressure profile of galaxy clusters from the REXCESS sample
(generalised NFW; Table 1: P₀=8.403, c₅₀₀=1.177, γ=0.3081, α=1.0510,
β=5.4905, α_p=0.12). Implemented as
PressureProfileA10.
arXiv:0910.1234
Oppenheimer B.D. et al. 2025.
DPMhalo: parametric electron density profiles for the diffuse gas around
galaxies; 3 calibrated model variants with mass- and redshift-dependent
normalisations. Implemented as
GasDensityDPM.
arXiv:2505.14782
Comparat J. et al. 2025, A&A 697, A173.
Galaxy × eROSITA eRASS:5 soft X-ray (0.5–2 keV) angular cross-correlation
for 7 stellar-mass-selected LS DR10 samples (M*>10¹⁰–10¹¹·⁵ M☉);
HOD + DPM gas model (Tables 3–4). Data in
hod_mod/data/benchmarks/xray/.
arXiv:2503.19796
Amodeo S. et al. 2021, Phys. Rev. D 103, 063514.
ACT DR4 × BOSS: stacked tSZ and kSZ profiles around BOSS CMASS and LOWZ
galaxies; 4.5σ measurement of the baryonic mass density in the warm-hot
intergalactic medium. Model comparison target for
validate_amodeo2021.py.
arXiv:2009.05557
Pandey S. et al. 2025.
DES Year 3 × ACT DR6: 21σ detection of the lensing × tSZ cross-correlation
C_ℓ^{γ,y}; constraints on baryonic feedback at group–cluster scales.
Model comparison target for validate_pandey2025.py.
arXiv:2506.07432
Surveys and Data
Key spectroscopic and imaging surveys providing the galaxy samples and
weak-lensing source catalogues used in hod_mod analyses.
Blanton M.R. et al. 2003, ApJ 592, 819. SDSS photometric survey and galaxy samples used in many HOD analyses. arXiv:astro-ph/0209479
Anderson L. et al. 2014, MNRAS 441, 24.
SDSS-III BOSS Data Releases 10 and 11; the CMASS sample is the
reference HOD target in more2015_boss_cmass.py.
arXiv:1312.4877
Aihara H. et al. 2018, PASJ 70, S4. Hyper Suprime-Cam Subaru Strategic Program: overview of the survey. arXiv:1704.05858
Mandelbaum R. et al. 2018, PASJ 70, S25.
HSC-Y1 weak-lensing shape catalog; source of HSC ESD data in hod_mod.
arXiv:1705.06745
Heymans C. et al. 2021, A&A 646, A140. KiDS-1000 multi-probe 3×2pt analysis: \(S_8 = 0.766^{+0.020}_{-0.014}\), 2–3σ below Planck; source of KiDS ESD data used in BGS analyses. arXiv:2007.15632
Abbott T.M.C. et al. 2022, Phys. Rev. D 105, 023520. DES Year 3 cosmic shear: \(S_8 = 0.759^{+0.025}_{-0.023}\), 2.3σ below Planck; source of DES ESD data used in BGS analyses. arXiv:2105.13544
DESI Collaboration 2023. DESI Early Data Release: survey overview, instrument, targeting. arXiv:2306.06308
Hahn C. et al. 2023, AJ 165, 253. DESI Bright Galaxy Survey: target selection, completeness, and validation. arXiv:2208.08512
Comparat J. et al. 2023, A&A 673, A122. eFEDS X-ray AGN HOD analysis: joint X-ray/optical galaxy–halo connection. ADS
Lange J.U. et al. 2024, MNRAS (accepted). Systematic effects in galaxy–galaxy lensing with DESI: fibre incompleteness, magnification, and intrinsic alignment for DES/HSC/KiDS sources. arXiv:2404.09397
Lange J.U. et al. 2025, ApJ (accepted). Unified photometric redshift calibration for DES, HSC, and KiDS weak-lensing surveys using DESI spectroscopy; reduces photo-z systematic uncertainty. arXiv:2510.25419 (verify: same ID as [FLAMINGO_fgas])
Recent Cosmological Constraints (S₈ Tension)
Joint analyses combining galaxy clustering with weak gravitational lensing to constrain \(S_8 = \sigma_8 (\Omega_m/0.3)^{0.5}\). All recent results find \(S_8 \approx 0.77{-}0.80\), consistently 1.5–2.5σ below Planck.
The data for the DESI-DR1 analyses are described in [Heydenreich2025]_ (“Lensing Without Borders”). Individual HOD-based and full-shape constraints:
[Miyatake2022]_ — HSC-Y1 × SDSS, \(S_8 = 0.795^{+0.049}_{-0.042}\)
[Lange2023]_ — BOSS × KiDS+DES, \(S_8 = 0.792 \pm 0.022\)
[Porredon2025]_ — DESI-DR1 3×2pt, \(S_8 = 0.786^{+0.022}_{-0.019}\)
[Semenaite2025]_ — DESI-DR1 full-shape, \(S_8 = 0.771{-}0.791\)
[Lange2025]_ — DESI-DR1 HOD-based, \(S_8 = 0.794 \pm 0.023\)
Porredon A. et al. 2025, Open J. Astrophys. 9. DESI-DR1 3×2pt analysis (BGS+LRG × KiDS-1000/DES-Y3/HSC-Y3): \(S_8 = 0.786^{+0.022}_{-0.019}\), 1.5–2σ below Planck. arXiv:2512.15960
Semenaite A. et al. 2025, Open J. Astrophys. DESI-DR1 full-shape clustering + lensing in configuration space (BGS+LRG × KiDS-1000/DES-Y3/HSC-Y3): \(S_8 = 0.771{-}0.791\), 1.9–2.9σ below Planck. arXiv:2512.15961
Inference Methods
Statistical inference tools used in hod_mod for MAP estimation and
posterior sampling.
Foreman-Mackey D. et al. 2013, PASP 125, 306.
emcee: the MCMC Hammer — affine-invariant ensemble sampler;
used in WpFitter.mcmc_fit().
arXiv:1202.3665
Phan D. et al. 2019.
NumPyro: composable effects for flexible and accelerated probabilistic
programming; used in hod_mod/inference.py for HMC/NUTS.
arXiv:1912.11554
Galaxy-Halo Connection with Non-Linear Power Spectrum
The standard halo model in hod_mod uses the linear matter power spectrum
\(P_{\rm lin}(k)\) for the 2-halo term (following More et al. 2015). A parallel
literature bypasses this approximation by either (a) substituting a non-linear fitting
formula / emulator for \(P(k)\) directly into the halo-model integrals, or (b)
emulating \(w_p(r_p)\) and \(\Delta\Sigma(R)\) end-to-end from N-body
simulations. The papers below are organised chronologically within four sub-topics.
Non-linear P(k) fitting formulae.
Peacock J.A. & Smith R.E. 2000, MNRAS 318, 1144. Derived the first analytic halo model for the non-linear matter power spectrum, decomposing \(P_{\rm nl}(k) = P^{\rm 1h}(k) + P^{\rm 2h}(k)\) from NFW profiles and a Press-Schechter HMF; foundation for all subsequent non-linear halo-model treatments of galaxy statistics. arXiv:astro-ph/0005010
Smith R.E. et al. 2003, MNRAS 341, 1311. HALOFIT: empirical fitting formula for \(P_{\rm nl}(k)\) calibrated on N-body simulations over \(0.001 \le k \le 10\,h\,{\rm Mpc}^{-1}\); the first widely used non-linear \(P(k)\) prescription in HOD pipelines. arXiv:astro-ph/0207664
Takahashi R. et al. 2012, ApJ 761, 152. Revised HALOFIT recalibrated on higher-resolution N-body simulations; corrects ~10% errors in the original Smith et al. (2003) formula at \(k \gtrsim 1\,h\,{\rm Mpc}^{-1}\); default non-linear \(P(k)\) in many HOD pipelines and in the CosmoCov / TreeCorr ecosystem. arXiv:1208.2701
Mead A.J. et al. 2021, MNRAS 502, 1401. HMcode-2020: extended halo model for non-linear \(P(k)\) with neutrino masses and baryonic feedback; sub-percent accuracy to \(k \le 10\,h\,{\rm Mpc}^{-1}\), \(z \le 2\); see also [Mead2015] for the original version. arXiv:2009.01858
Foundational halo-model treatments of galaxy statistics.
Seljak U. 2000, MNRAS 318, 1144. First analytic galaxy + dark matter clustering model using NFW profiles and a Poisson HOD; showed that non-linear galaxy power spectra can be predicted from halo properties alone, motivating the modern HOD+halo-model approach. arXiv:astro-ph/0001493
Scoccimarro R., Sheth R.K., Hui L. & Jain B. 2001, ApJ 546, 20. “How Many Galaxies Fit in a Halo?”: tested non-linear HOD predictions from N-body simulations; established that the 1-halo term dominates \(w_p(r_p)\) at \(r_p \lesssim 1\,h^{-1}\) Mpc and that departures from linearity must be modelled at those scales. arXiv:astro-ph/0006319
HOD / CLF implementations fitting w_p and ΔΣ with the full non-linear halo model.
Cacciato M., van den Bosch F.C. & More S. 2009, MNRAS 394, 929. Conditional luminosity function (CLF) halo model jointly fitting galaxy clustering and galaxy-galaxy lensing; the 1-halo contribution to both \(w_p\) and \(\Delta\Sigma\) is computed from non-linear NFW profiles; pioneered the combined \(w_p + \Delta\Sigma\) constraint framework. arXiv:0807.4932
Leauthaud A. et al. 2012, ApJ 744, 159. COSMOS HOD: joint weak lensing + clustering across stellar-mass threshold bins at \(0.2 < z < 1.0\); full non-linear 1h+2h halo model including NFW profiles; derived galaxy–halo connection from \(\Delta\Sigma + n_{\rm gal}\). arXiv:1104.0928
Cacciato M., van Uitert E. & Hoekstra H. 2014, MNRAS 437, 377. CLF halo model for KiDS/SDSS weak lensing + clustering spanning 0.1–30 \(h^{-1}`Mpc in a single non-linear halo model; demonstrated consistent :math:`w_p + \Delta\Sigma\) constraints without switching between linear and non-linear prescriptions. arXiv:1303.5445
Zacharegkas G. et al. 2022, MNRAS 509, 3119. DES Year 3 galaxy-galaxy lensing: high-precision \(\Delta\Sigma(R)\) measurement combined with \(w_p(r_p)\) and HOD halo-model fitting at non-linear scales; one of the largest GGL samples used for galaxy-halo connection inference at the time. arXiv:2106.08438
N-body emulator approaches: w_p and ΔΣ predicted directly from simulations.
These methods replace both the linear power spectrum and the analytic halo model integrals with Gaussian-process or neural-network interpolation over a grid of N-body runs, making the predicted statistics fully non-linear by construction.
DeRose J. et al. 2019. The Aemulus Project I: suite of 75 high-resolution N-body simulations spanning a 7-dimensional wCDM parameter space; the simulation grid that underpins the Aemulus halo-statistics emulator (see [Wibking2019]). arXiv:1804.05865
Wibking B.D., Salcedo A.N. & Weinberg D.H. 2019, MNRAS 492, 2872. Methodology and Fisher-matrix forecasts for emulating galaxy clustering and galaxy-galaxy lensing into the deeply non-linear regime; Taylor-expansion emulator around a pivot HOD; showed that small scales (\(r_p \gtrsim 0.5\,h^{-1}\) Mpc) tighten cosmological constraints substantially. arXiv:1709.07099
Wibking B.D., Weinberg D.H. & Salcedo A.N. 2020, MNRAS 492, 2872. Applied the emulator method to BOSS LOWZ: cosmological constraints from \(w_p + \Delta\Sigma\) on non-perturbative scales (0.4–30 \(h^{-1}\) Mpc); demonstrated consistent results with traditional large-scale analyses while extracting additional information from the 1-halo regime. arXiv:1907.06293
Kobayashi Y. et al. 2020. Dark Quest emulator for the redshift-space power spectrum of dark matter halos; neural-network emulator trained on the Dark Quest N-body suite; achieves ~1% accuracy for galaxy power spectrum predictions used in HOD \(w_p\) / \(\Delta\Sigma\) forward models. arXiv:2005.06122
Miyatake H. et al. 2021, Phys. Rev. D 103, 123517. Dark Quest validation paper: cosmological inference pipeline from emulator-based HOD applied to HSC-Y1 and SDSS mock catalogues; established end-to-end accuracy of the emulator approach for joint \(w_p + \Delta\Sigma\) analysis before application to real data (see [Miyatake2022]). arXiv:2101.00113
Simulation Reference: FLAMINGO
The FLAMINGO suite of cosmological hydrodynamical simulations underpins
the baryon fraction and gas profile calibrations in hod_mod.
FLAMINGO Collaboration 2023. FLAMINGO: Large cosmo-hydro simulations for next-generation lensing surveys. https://flamingo.strw.leidenuniv.nl/
Beyond-ΛCDM Cosmology and Emulators
Dynamical dark energy, massive neutrinos and the emulator landscape — the upgrade path for the differentiable forecast cosmology.
Eisenstein D.J. & Hu W. 1999, ApJ 511, 5. Analytic transfer functions for CDM variants including massive neutrinos; the fitting-function route to a differentiable ν-suppression. arXiv:astro-ph/9710252
Kiakotou A., Elgarøy Ø. & Lahav O. 2008, Phys. Rev. D 77, 063005. Scale-dependent growth suppression from massive neutrinos — compact coefficients for the f_ν correction to D(z, k). arXiv:0709.0253
DESI Collaboration 2025, Phys. Rev. D 112, 083515. DESI DR2 BAO measurements: ~3σ preference for dynamical dark energy (w0waCDM) — the science driver for freeing w0/wa in the forecast. arXiv:2503.14738
Di Valentino E. et al. 2025, Phys. Dark Univ. 49, 101965. CosmoVerse white paper on observational tensions (H0, S8) — the context in which beyond-ΛCDM freedom must be marginalised, not assumed away. arXiv:2504.01669
Euclid Collaboration (Knabenhans M.) et al. 2021, MNRAS 505, 2840. EuclidEmulator2: non-linear P(k) boost emulation with massive neutrinos and dynamical dark energy — a distill-to-table candidate for the JAX pipeline. arXiv:2010.11288
Angulo R.E. et al. 2021, MNRAS 507, 5869. The BACCO simulation project: cosmology-rescaled non-linear P(k) emulator covering σ8, w0/wa and Σm_ν. arXiv:2004.06245
Moran K.R. et al. 2023, MNRAS 520, 3443. Mira-Titan IV: high-precision P(k) emulator over an 8-parameter w0waνCDM space. arXiv:2207.12345
Chen Z. et al. 2025, Sci. China Phys. Mech. Astron. 68, 289512.
CSST emulator I: matter P(k) to k = 10 h/Mpc at one percent in w0waνCDM;
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Precision Halo-Model Ingredients
Cosmology-dependent calibrations of the mass function, bias, concentration and the linear power spectrum (see What the model does not yet contain).
Lesgourgues J. 2011, arXiv:1104.2932. The CLASS Boltzmann solver — with CAMB [Lewis2002], the accuracy standard any differentiable P(k) surrogate must reproduce. arXiv:1104.2932
Spurio Mancini A. et al. 2022, MNRAS 511, 1771.
COSMOPOWER: neural-network emulation of CMB and matter power spectra; the
dense-MLP architecture is ~10 lines of jnp to evaluate — the template
for a differentiable CAMB-ratio emulator.
arXiv:2106.03846
Sánchez A.G. et al. 2022, MNRAS 514, 5673. Evolution mapping: degeneracy structure of matter clustering across cosmologies — compresses the emulation parameter space. arXiv:2108.12710
Diemer B. & Kravtsov A.V. 2015, ApJ 799, 108. Universal concentration–mass model in terms of peak height ν and local P(k) slope n_eff — a genuinely cosmology-dependent c(M) that is JAX-portable (both inputs live on the σ(M) grid). arXiv:1407.4730
Diemer B. 2018, ApJS 239, 35. COLOSSUS toolkit — the numerical reference implementation for validating c(M) and HMF ports. arXiv:1712.04512
Despali G. et al. 2016, MNRAS 456, 2486. Universality of the virial-overdensity HMF and models for the non-universality of other halo definitions. arXiv:1507.05627
Comparat J. et al. 2017, MNRAS 469, 4157.
Accurate halo mass and velocity functions from the MultiDark simulations;
one of the f(sigma) fits shipped in hod_mod.
arXiv:1702.01628
Euclid Collaboration (Castro T.) et al. 2023, A&A 671, A100. Percent-level HMF calibration in Λ(ν)CDM with explicit cosmology dependence of the fit parameters — the reference against which the tinker08 non-universality budget should be quoted. arXiv:2208.02174
AGN Multi-Wavelength Emission and the Fundamental Plane
Radio and infrared AGN emission channels and their halo statistics (see What the model does not yet contain).
Merloni A., Heinz S. & di Matteo T. 2003, MNRAS 345, 1057. The fundamental plane of black-hole activity: log L_R = 0.60 log L_X + 0.78 log M_BH + 7.33 — the relation that attaches a radio luminosity to the Powell chain’s (M_BH, L_X). arXiv:astro-ph/0305261
Falcke H., Körding E. & Markoff S. 2004, A&A 414, 895. Jet-dominated accretion unification of low-power black holes — the physical basis of the radio/X-ray correlation. arXiv:astro-ph/0305335
Gültekin K. et al. 2019, ApJ 871, 80. Updated fundamental-plane coefficients and scatter with dynamical M_BH — the natural external prior on (ξ_RX, ξ_RM, b_R, σ_R). arXiv:1901.02530
Hopkins P.F., Richards G.T. & Hernquist L. 2007, ApJ 654, 731. Observational bolometric quasar luminosity function; the L_bol-dependent bolometric corrections used to map L_bol to infrared bands. arXiv:astro-ph/0605678
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Shen Y. et al. 2009, ApJ 697, 1656. Quasar clustering from SDSS DR5 as a function of physical properties (including radio loudness). arXiv:0810.4144
Retana-Montenegro E. & Röttgering H.J.A. 2017, A&A 600, A97. Radio-loud vs radio-quiet quasar clustering (SDSS×FIRST): halo-mass difference between the two populations. arXiv:1611.08630
Hale C.L. et al. 2025, MNRAS 544, 1323. Clustering of radio AGN and star-forming galaxies in the LoTSS Deep Fields — the current benchmark for radio-AGN halo occupation. arXiv:2510.01029
Donoso E. et al. 2014, ApJ 789, 44. Angular clustering of WISE-selected AGN: different halos for obscured and unobscured populations. arXiv:1309.2277
Petter G.C. et al. 2023, ApJ 946, 27. Host halos of 1.4 million WISE obscured/unobscured quasars — the IR-side test of the obscuration parameter shared with the X-ray sector. arXiv:2302.00690
Comparat J. et al. 2019, MNRAS 487, 2005.
eROSITA AGN mock catalogue with empirical multi-wavelength SEDs — the
hod_mod heritage for AGN band modelling.
arXiv:1901.10866
Aird J. et al. 2015, MNRAS 451, 1892.
X-ray luminosity functions of unabsorbed and absorbed AGN to z~5; the
validation target of the Powell XLF in hod_mod.
arXiv:1503.01120
Galaxy Morphology, Quenching and Star Formation
The galaxy-population physics (morphology, sSFR, quenching) missing from the all-galaxy ZM15 connection (see What the model does not yet contain).
Wechsler R.H. & Tinker J.L. 2018, ARA&A 56, 435. Review of the galaxy–halo connection: assembly bias, conditional distributions, and where morphology/SFR enter the halo model. arXiv:1804.03097
Peng Y. et al. 2010, ApJ 721, 193. Mass and environment quenching separability — the empirical form behind halo-mass quenching parameterisations. arXiv:1003.4747
Ilbert O. et al. 2013, A&A 556, A55. Quiescent and star-forming stellar mass functions since z≈4 (UltraVISTA) — the f_Q(M*, z) data the split model must reproduce. arXiv:1301.3157
Muzzin A. et al. 2013, ApJ 777, 18. SF/quiescent stellar mass functions to z = 4 (COSMOS/UltraVISTA). arXiv:1303.4409
Speagle J.S. et al. 2014, ApJS 214, 15. Consistent star-forming main sequence over 0 < z < 6 — the μ_MS(M*, z) parameterisation for a continuous sSFR model. arXiv:1405.2041
Kennicutt R.C. 1998, ARA&A 36, 189.
Star formation in galaxies along the Hubble sequence — the SFR–line
calibrations behind the loii_norm parameter.
arXiv:astro-ph/9807187
Madau P. & Dickinson M. 2014, ARA&A 52, 415.
The cosmic star-formation history — the ρ_SFR(z) data the sfrd
observable is compared against.
arXiv:1403.0007
Murphy E.J. et al. 2011, ApJ 737, 67.
Extinction-free SFR diagnostics calibrated with 33 GHz free–free emission
— the L_ν(1.4 GHz)–SFR calibration behind l14_sfr.
arXiv:1105.4877
Kennicutt R.C. & Evans N.J. 2012, ARA&A 50, 531.
Star formation in the Milky Way and nearby galaxies — the total-IR and
Hα SFR calibrations behind lir_sfr and lha_norm.
arXiv:1204.3552
Runnoe J.C., Brotherton M.S. & Shang Z. 2012, MNRAS 422, 478.
Updated quasar bolometric corrections — the 1450 Å and 4400 Å values
behind agn_bc_uv / agn_bc_opt.
arXiv:1201.5155
Wright E.L. et al. 2010, AJ 140, 1868. The Wide-field Infrared Survey Explorer (WISE) — the 3.4/4.6/12 μm bands the tier-3 IR intensity maps emulate. arXiv:1008.0031
Doré O. et al. 2014, arXiv e-prints. Cosmology with the SPHEREx all-sky spectral survey — the near-IR intensity-mapping context of the tier-3 IR maps. arXiv:1412.4872
Bacon D.J. et al. (SKA Cosmology SWG) 2020, PASA 37, e007. Cosmology with Phase 1 of the Square Kilometre Array (Red Book 2018) — the radio continuum survey spec the tier-3 radio maps emulate. arXiv:1811.02743
Behroozi P. et al. 2019, MNRAS 488, 3143. UniverseMachine: empirical galaxy–halo connection with per-halo SFR histories — the reference empirical model for SFR-resolved occupations. arXiv:1806.07893
Yang X., Mo H.J. & van den Bosch F.C. 2007, ApJ 671, 153. SDSS DR4 galaxy group catalogue — quenched fractions vs group halo mass. arXiv:0707.4640
Tinker J.L. 2021, ApJ 923, 154. Self-calibrating halo-based group finder — the modern f_Q(M_h) data. arXiv:2010.02946
Zhang Y. et al. 2024, A&A 690, A268. eROSITA hot CGM II: L_X–mass scaling relations of central galaxies. arXiv:2401.17309
Zhang Y. et al. 2025, A&A 693, A197. eROSITA hot CGM III: star-forming vs quiescent galaxies — the measurement an SF/Q-split hot-gas sector must fit. arXiv:2411.19945
Truong N., Pillepich A. & Nelson D. 2021, MNRAS 508, 1563. TNG predictions linking CGM X-ray properties to galaxy sSFR. arXiv:2109.06884
Banerjee A. et al. 2025, PASA 42, 78. AGN vs star-forming galaxies at fixed stellar mass: colour, D4000, morphology and clustering differences. arXiv:2310.12943
Yang G. et al. 2019, MNRAS 485, 3721. Black-hole growth traces bulge growth — the coupling that puts B/T into the M_BH–M* step of the Powell chain. arXiv:1903.00003
Ni Q. et al. 2019, MNRAS 490, 1135. BH growth vs host compactness at fixed M* — morphology as a second parameter of AGN occupation. arXiv:1909.06382
Galaxy Luminosity Functions and SEDs
Multi-band luminosity functions and the stellar-population calibrations for a band-resolved conditional luminosity function (see What the model does not yet contain).
Kauffmann G. et al. 2003, MNRAS 341, 33. Stellar masses and star-formation histories for 10^5 SDSS galaxies — the M*/L calibration anchor for mass-to-light parameterisations. arXiv:astro-ph/0204055
Faber S.M. et al. 2007, ApJ 665, 265. B-band luminosity functions to z≈1 (DEEP2/COMBO-17) and the red-sequence build-up. arXiv:astro-ph/0506044
Comparat J. et al. 2015, A&A 575, A40. Evolution of the bright end of the [OII] luminosity function — the emission-line LF target for an SFR→line extension. arXiv:1408.1523
Shuntov M. et al. 2025, A&A 695, A20. COSMOS-Web stellar-mass assembly in relation to dark-matter halos over 0.2 < z < 12. arXiv:2410.08290
Xu K. et al. 2025, MNRAS 540, 1635. PAC in DESI: the galaxy stellar mass function into the 10^6 M_sun frontier. arXiv:2503.01948
Euclid Collaboration (Zalesky L.) et al. 2025. Cosmic Dawn Survey: galaxy stellar mass function over 0.2 < z < 6.5 on 10 deg². arXiv:2504.17867
Stellar Feedback and Galactic Winds
Supernova-driven winds and the simulation suites that calibrate them (see What the model does not yet contain; AGN feedback references live in the Baryonic-Effects group above).
Somerville R.S. & Davé R. 2015, ARA&A 53, 51. Review of physical models of galaxy formation — the canonical forms of energy- and momentum-driven wind mass loading. arXiv:1412.2712
Muratov A.L. et al. 2015, MNRAS 454, 2691. FIRE galactic winds: η_w ∝ V_c^{-1} (momentum) to V_c^{-2} (energy) mass-loading calibrations. arXiv:1501.03155
Chisholm J., Tremonti C.A., Leitherer C. & Chen Y. 2017, MNRAS 469, 4831. Measured mass and momentum outflow rates of photoionised galactic winds — external priors on the wind sector. arXiv:1702.07351
Pillepich A. et al. 2018, MNRAS 473, 4077. The IllustrisTNG galaxy-formation model — the SN-wind + AGN subgrid pairing modern hydro suites converge on. arXiv:1703.02970
Villaescusa-Navarro F. et al. 2023, ApJS 265, 54. CAMELS public data release: thousands of hydro simulations varying SN and AGN feedback — the validation grid for a freed feedback sector. arXiv:2201.01300
Eckert D., Gaspari M., Gastaldello F. et al. 2021, Universe 7, 142. Feedback in galaxy groups: the mass scale where SN and AGN feedback prescriptions diverge most between simulations. arXiv:2106.13259
Cold Gas and Neutral Hydrogen
The HI halo model, cold-gas scaling relations and 21 cm data (see What the model does not yet contain).
Jones M.G. et al. 2018, MNRAS 477, 2. The ALFALFA HI mass function — the z≈0 abundance anchor for the HI sector. arXiv:1802.00053
Catinella B. et al. 2018, MNRAS 476, 875. xGASS: cold-gas scaling relations and atomic-to-molecular ratios of local galaxies — the M_HI(M*, sSFR) conditional data. arXiv:1802.02373
Obuljen A. et al. 2019, MNRAS 486, 5124. The HI content of dark-matter halos at z≈0 from ALFALFA — the empirical HI HOD. arXiv:1805.00934
Guo H. et al. 2023, ApJ 955, 57. NeutralUniverseMachine: empirical HI/H2 evolution on the UniverseMachine galaxy–halo connection (HDR reference for the cold-gas outlook). arXiv:2307.07078
Nishigaki M. et al. 2025, ApJ 984, 135. ChemicalUniverseMachine: metals in the galaxy–ISM–CGM ecosystem. arXiv:2503.10999
CHIME Collaboration 2023, ApJ 947, 16. Detection of cosmological 21 cm emission in cross-correlation with eBOSS tracers — the proof of principle for C_ell^{HI×g}. arXiv:2202.01242
Ponomareva A.A. et al. 2023, MNRAS 522, 5308. MIGHTEE-HI: the first MeerKAT HI mass function from an untargeted interferometric survey. arXiv:2304.13051
Braun R. et al. 2019, arXiv:1912.12699. Anticipated performance of SKA1 — the sensitivity reference for HI intensity-mapping forecasts. arXiv:1912.12699
HI4PI Collaboration 2016, A&A 594, A116.
Full-sky Galactic HI survey — used in hod_mod only as the Galactic
absorption (N_H) template, not as extragalactic cold gas.
arXiv:1610.06175
Sensitivity-Benchmark Data Anchors
The published measurements compiled on Sensitivity benchmark: the existing observables the model must reproduce — the “already existing observables” the forward model must reproduce, and the current state of the art per probe.
Zehavi I. et al. 2011, ApJ 736, 59. SDSS DR7 projected correlation functions w_p(r_p) per luminosity/colour sample — the reference low-z clustering benchmark. arXiv:1005.2413
Wright A.H. et al. 2025, A&A. KiDS-Legacy cosmic shear (1347 deg², nine bands): S8 = 0.815 (+0.016/−0.021) — the current cosmic-shear state of the art. arXiv:2503.19441
Qu F.J. et al. 2024, ApJ 962, 112. ACT DR6 CMB-lensing power spectrum: 43σ (2.3 % amplitude), matching Planck PR4 — the C_ell^{κκ_CMB} benchmark. arXiv:2304.05202
Kim J. et al. 2024, JCAP 12, 022. DESI LRG × ACT DR6 CMB lensing in four tomographic bins (0.4 ≤ z ≤ 1): 38σ (50σ with Planck PR4), S8 to 2.7 % — the C_ell^{gκ_CMB} benchmark. arXiv:2407.04606
Ghirardini V. et al. 2024, A&A.
eRASS1 cluster-abundance cosmology (5259 clusters, 12 791 deg²):
σ8 = 0.88 ± 0.02, S8 = 0.86 ± 0.01, Σm_ν < 0.43 eV — the live X-ray
cluster-counts (ncl) benchmark.
arXiv:2402.08458
DESI Collaboration 2025, JCAP 07, 028 (DESI 2024 VII). DR1 full-shape clustering: σ8 = 0.842 ± 0.034 alone, σ8 to 0.65 % with CMB — the RSD/full-shape benchmark. arXiv:2411.12022
DESI Collaboration 2025, Phys. Rev. D 112, 083515. DR2 BAO from >14 million tracers; with CMB a 3.1σ preference for w0waCDM over ΛCDM — the geometric benchmark for the freed (w0, wa). arXiv:2503.14738
Schaan E. et al. (ACT) 2021, Phys. Rev. D 103, 063513. kSZ + tSZ profiles of BOSS CMASS galaxies with ACT — the measured gas-density/pressure profiles a kSZ observable would be fit against. arXiv:2009.05557
CHIME Collaboration 2025 (preprint). First detection of the cosmological 21 cm auto-power spectrum at z ≈ 1 with CHIME — the C_ell^{HI×HI} proof of principle. arXiv:2511.19620
Bonato M. et al. 2021, A&A 656, A48.
LoTSS Deep Fields: 150 MHz luminosity of star-forming galaxies as an SFR
tracer — the measured SF radio LF behind l14_sfr/rlf.
arXiv:2109.06735
Kondapally R. et al. 2022, MNRAS 513, 3742. LoTSS Deep Fields: cosmic evolution of low-excitation radio galaxies to z ≈ 2.5 — the radio-AGN (jet-mode) LF benchmark. arXiv:2204.07588
Weaver J.R. et al. 2023, A&A 677, A184. COSMOS2020 galaxy stellar-mass function 0.2 < z < 5.5, total and quiescent — the SMF + quenched-fraction benchmark for the (z, M*) grid. arXiv:2212.02512
Driver S.P. et al. 2022, MNRAS 513, 439. GAMA DR4: the low-z galaxy stellar-mass function over 250 deg² — the local SMF anchor. arXiv:2203.08539
Popesso P. et al. 2023, MNRAS 519, 1526.
The main sequence of star-forming galaxies over 0 < z < 6 — the
sSFR(M*, z) benchmark behind ssfr_ms_*.
arXiv:2203.10487
Kulkarni G., Worseck G. & Hennawi J.F. 2019, MNRAS 488, 1035.
Homogenised type-1 quasar luminosity functions 0 < z < 7.5 — the
qlf_uv/qlf_opt benchmark.
arXiv:1807.09774
Kormendy J. & Ho L.C. 2013, ARA&A 51, 511.
The coevolution of supermassive black holes and their host galaxies —
the M_BH–M_bulge census that pins the agn_mu_bh sector externally.
arXiv:1304.7762
Greene J.E., Strader J. & Ho L.C. 2020, ARA&A 58, 257. The demographics of intermediate-mass and low-mass-galaxy black holes — extends the M_BH census to the tier-3 M* < 10¹⁰ regime. arXiv:1911.09678
Sobral D. et al. 2013, MNRAS 428, 1128.
HiZELS: Hα luminosity functions at z = 0.4–2.2 — the half
observable benchmark.
arXiv:1202.3436
Wyder T.K. et al. 2005, ApJ 619, L15.
GALEX local UV luminosity functions — the low-z uvlf anchor.
arXiv:astro-ph/0411364
Moustakas J. et al. 2013, ApJ 767, 50. PRIMUS: stellar-mass functions and quenching 0 < z < 1 — a core constraint of the UniverseMachine compilation. arXiv:1301.1688
Song M. et al. 2016, ApJ 825, 5. UV–stellar-mass relations at z = 4–8 from CANDELS SED stacks — a UniverseMachine constraint re-derived in their Appendix D. arXiv:1507.05636
Finkelstein S.L. et al. 2015, ApJ 810, 71. UV luminosity functions at z = 4–8 from CANDELS/HUDF — a UniverseMachine UVLF constraint. arXiv:1410.5439
Harikane Y. et al. 2023, ApJS 265, 5. JWST UV luminosity functions at z ≈ 9–16 — the current high-z frontier of the UVLF benchmark. arXiv:2208.01612
Macquart J.-P. et al. 2020, Nature 581, 391. The FRB dispersion-measure–redshift relation: a direct census of the ionised cosmic baryons — a published probe of the same hot-gas sector. arXiv:2005.13161
Kollmeier J.A. et al. 2017, arXiv e-prints. SDSS-V: Pioneering Panoptic Spectroscopy — the Black Hole Mapper time-domain spectroscopic M_BH census now underway. arXiv:1711.03234
Powell M.C. et al. 2022, ApJ 938, 77 (BASS XXXVI).
Constraining the local SMBH–halo connection by forward-modelling the
clustering + luminosity function of Swift/BAT AGN — the AGN–halo model
implemented as hod_mod.agn.powell.PowellAGNModel.
arXiv:2209.02728
Ananna T.T. et al. 2022, ApJS 261, 9 (BASS XXX).
Distribution functions of BASS DR2 Eddington ratios, black-hole masses
and X-ray luminosities — the broken-power-law ERDF used by the Powell
chain (agn_log10_lstar, agn_delta1, agn_delta2).
arXiv:2201.05603
Euclid Collaboration: Walmsley M. et al. 2025. Euclid Q1: first visual morphology catalogue — 378k detailed morphologies (Zoobot + Galaxy Zoo), 0.4% of the eventual ~100M; the f_early(M*, z) data source of the tier-4 forecast. arXiv:2503.15310
Euclid Collaboration 2024 (prep. XLIII). Measuring detailed galaxy morphologies for Euclid with machine learning. arXiv:2402.10187
Kartaltepe J.S. et al. 2023, ApJL 946, L15. CEERS Key Paper III: the diversity of galaxy structure and morphology at z = 3–9 with JWST — the high-z anchor of f_early(z). arXiv:2210.14713
Ferreira L. et al. 2023, ApJ 955, 94. Galaxy morphology from z ~ 6 through the eyes of JWST. arXiv:2305.02478
COSMOS-Web 2025. The emergence of the Hubble sequence — morphological fractions across cosmic time from the largest JWST mosaic. arXiv:2502.03532
Skibba R.A. et al. 2009, MNRAS 399, 966. Galaxy Zoo: disentangling the environmental dependence of morphology and colour — morphology-marked clustering statistics. arXiv:0811.3970
Masters K.L. et al. 2010, MNRAS 405, 783.
Galaxy Zoo: passive red spirals — the E/Q off-diagonal census behind the
f_early_q observable and rho_morph_q.
arXiv:0910.4113
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Bluck A.F.L. et al. 2022, A&A 659, A160. The quenching of galaxies, bulges and disks since cosmic noon — the morphology–quenching causality the joint fractions constrain. arXiv:2201.07814
Kravtsov A.V. 2013, ApJL 764, L31.
The size–virial radius relation: R_e ≈ 0.015 R_200c with 0.2 dex scatter
over eight decades of M* — the size observable’s backbone.
arXiv:1212.2980
van der Wel A. et al. 2014, ApJ 788, 28.
3D-HST+CANDELS: the galaxy size–mass distribution since z = 3 — early
types smaller and faster-evolving (dsize_early, f_size_zs).
arXiv:1404.2844
Georgiou C. et al. 2025, A&A.
KiDS-1000 bright sample intrinsic alignments: dependence on colour,
luminosity, MORPHOLOGY and galaxy scale — IA is driven by morphology,
the basis of the a_ia · f_early NLA scaling.
arXiv:2502.09452