Bibliography

This page collects all references cited in the sum_stat documentation, organised by topic. Each entry links to the NASA Astrophysics Data System (ADS) abstract page.

Software packages

These are the codes called directly by sum_stat or used in its test suite.

  1. Sinha, M. & Garrison, L. H. (2020). CORRFUNC — a suite of blazing fast correlation functions on the CPU. MNRAS 491, 3022. ADS

  2. Jarvis, M., Bernstein, G. & Jain, B. (2004). The skewness of the aperture mass statistic. MNRAS 352, 338. Primary reference for the TreeCorr library. ADS

  3. Jarvis, M. (2015). TreeCorr: two-point correlation functions. Astrophysics Source Code Library, record ascl:1508.007. ADS

  4. Lange, J. U. (2022). dsigma: galaxy-galaxy lensing Python package. Astrophysics Source Code Library, record ascl:2204.006. ADS

  5. Górski, K. M., Hivon, E., Banday, A. J., et al. (2005). HEALPix: A Framework for High-Resolution Discretization and Fast Analysis of Data Distributed on the Sphere. ApJ 622, 759. ADS

  6. Zonca, A., Singer, L., Lenz, D., et al. (2019). healpy: equal area pixelization and spherical harmonics transforms for data on the sphere in Python. Journal of Open Source Software 4, 1298. ADS

  7. Astropy Collaboration, Price-Whelan, A. M., Sipőcz, B. M., et al. (2018). The Astropy Project: Building an Open-science Project and Status of the v2.0 Core Package. AJ 156, 123. ADS

  8. Astropy Collaboration, Price-Whelan, A. M., Lim, P. L., et al. (2022). The Astropy Project: Sustaining and Growing a Community-oriented Open-source Project and the Latest Major Release (v5.0) of the Core Package. ApJ 935, 167. ADS

  9. Foreman-Mackey, D., Hogg, D. W., Lang, D. & Goodman, J. (2013). emcee: The MCMC Hammer. PASP 125, 306. ADS

One-point estimators — luminosity and stellar mass functions

These papers define the estimators implemented in sum_stat.lf_smf.

  1. Schmidt, M. (1968). Space Distribution and Luminosity Functions of Quasi-Stellar Radio Sources. ApJ 151, 393. Introduced the 1/Vmax method. ADS

  2. Efstathiou, G., Ellis, R. S. & Peterson, B. A. (1988). Analysis of a complete galaxy redshift survey — II. The field-galaxy luminosity function. MNRAS 232, 431. Introduced the stepwise maximum-likelihood (SWML) estimator. ADS

  3. Lynden-Bell, D. (1971). A method of allowing for known observational selection in small samples applied to 3CR quasars. MNRAS 155, 95. Introduced the C− cumulative estimator. ADS

  4. Efron, B. & Petrosian, V. (1992). A simple test of independence for truncated data with applications to redshift surveys. ApJ 399, 345. Introduced the τ statistic for magnitude–redshift independence. ADS

  5. Rauzy, S. (2001). A likelihood-based approach to the estimation of the luminosity function. MNRAS 324, 51. Introduced the Tc/Tv completeness statistics. ADS

  6. Johnston, R., Teodoro, L. F. A. & Hendry, M. A. (2007). The use of photometric survey data in luminosity function determination. MNRAS 376, 1757. Completeness scan with bright limit (Tc / Tv). ADS

  7. Teodoro, L. F. A., Davis, M. & Strigari, L. E. (2010). Completing the census of the Local Group. MNRAS 405, 1187. Adaptive S/N mode for the completeness scan. ADS

  8. Johnston, R., Henriques, B. & Teodoro, L. F. A. (2012). Completeness corrections — III. Identifying characteristic systematics and evolution in galaxy redshift surveys. MNRAS 421, 270. Error propagation for the completeness scan. ADS

Two-point estimators — galaxy clustering

These papers define the correlation-function estimators and projections used by sum_stat.twopcf.

  1. Peebles, P. J. E. & Hauser, M. G. (1974). Statistical Analysis of Catalogs of Extragalactic Objects. I. Theory. ApJS 28, 19. The first formulation of the 2-point correlation function estimator DD/RR. ADS

  2. Davis, M. & Peebles, P. J. E. (1977). On the integration of the BBGKY equations for the development of strongly nonlinear clustering in an expanding universe. ApJS 34, 425. Early measurement of ξ(r) from the CfA survey. ADS

  3. Davis, M. & Peebles, P. J. E. (1983). A survey of galaxy redshifts. V. The two-point position and velocity correlations. ApJ 267, 465. Introduced the Davis–Peebles (DP) estimator and the projected correlation function wp(rp). ADS

  4. Landy, S. D. & Szalay, A. S. (1993). Bias and variance of angular correlation functions. ApJ 412, 64. Introduced the Landy–Szalay (LS) estimator, used for both angular w(θ) and projected wp(rp). ADS

  5. Limber, D. N. (1953). The Analysis of Counts of the Extragalactic Nebulae in Terms of a Fluctuating Density Field. ApJ 117, 134. The Limber approximation relating angular to spatial correlations. ADS

  6. Hamilton, A. J. S. (1992). Linear redshift distortions: a review. ApJ 385, L5. Legendre decomposition of the redshift-space 2PCF into multipoles ξℓ(s). ADS

  7. Norberg, P., Baugh, C. M., Gaztañaga, E. & Croton, D. J. (2009). Statistical analysis of galaxy surveys — I. Robust error estimation for two-point clustering statistics. MNRAS 396, 19. Comprehensive comparison of jackknife and bootstrap covariance estimators. ADS

  8. Vargas-Magaña, M., Bautista, J. E., Hamilton, J.-C., et al. (2013). An optimized correlation function estimator for galaxy surveys. A&A 554, A131. Detailed comparison of LS and related estimators. ADS

Weak gravitational lensing

These papers define the excess surface density estimator used by sum_stat.lensing.

  1. Bartelmann, M. & Schneider, P. (2001). Weak gravitational lensing. Phys Rep 340, 291. Standard review of weak lensing theory. ADS

  2. Wright, C. O. & Brainerd, T. G. (2000). Gravitational Lensing by NFW Halos. ApJ 534, 34. Analytic expression for ΔΣ(rp) around an NFW profile. ADS

  3. Sheldon, E. S., Johnston, D. E., Frieman, J. A., et al. (2004). The Galaxy-Mass Correlation Function Measured from Weak Lensing in the Sloan Digital Sky Survey. AJ 127, 2544. Defined the ΔΣ estimator used in sum_stat. ADS

  4. Mandelbaum, R., Hirata, C. M., Broderick, T., Seljak, U. & Brinkmann, J. (2005). Systematic effects in weak lensing: Application to SDSS galaxy–galaxy weak lensing. MNRAS 361, 1287. ADS

Power spectra and angular statistics

These papers define the power-spectrum estimators in sum_stat.powspec.

  1. Feldman, H. A., Kaiser, N. & Peacock, J. A. (1994). Power-spectrum Analysis of Three-dimensional Redshift Surveys. ApJ 426, 23. The FKP minimum-variance weighting scheme. ADS

  2. Hivon, E., Górski, K. M., Netterfield, C. B., et al. (2002). MASTER of the Cosmic Microwave Background Anisotropy Power Spectrum: A Fast Method for Statistical Analysis of Large and Complex CMB Data Sets. ApJ 567, 2. The pseudo-Cℓ method used for the angular power spectrum. ADS

Surveys and datasets

Reference papers for the datasets used in validation runs.

  1. Laigle, C., McCracken, H. J., Ilbert, O., et al. (2016). The COSMOS2015 Catalog: Exploring the 1 < z < 6 Universe with Half a Million Galaxies. ApJS 224, 24. ADS

  2. Ilbert, O., McCracken, H. J., Le Fèvre, O., et al. (2013). Mass assembly in quiescent and star-forming galaxies since z ≃ 4 from UltraVISTA. A&A 556, A55. COSMOS stellar mass function across 0 < z < 4. ADS

  3. Davidzon, I., Ilbert, O., Laigle, C., et al. (2017). The COSMOS2015 galaxy stellar mass function. A&A 605, A70. ADS

  4. Weaver, J. R., Kauffmann, O. B., Ilbert, O., et al. (2022). COSMOS2020: A Panchromatic View of the Universe to z ∼ 10 from Two Complementary Catalogs. ApJS 258, 11. ADS

  5. Driver, S. P., Hill, D. T., Kelvin, L. S., et al. (2011). Galaxy and Mass Assembly (GAMA): survey diagnostics and core data release. MNRAS 413, 971. ADS

  6. Baldry, I. K., Driver, S. P., Loveday, J., et al. (2012). Galaxy And Mass Assembly (GAMA): the galaxy stellar mass function at z < 0.06. MNRAS 421, 621. ADS

  7. Loveday, J., Norberg, P., Baldry, I. K., et al. (2012). Galaxy And Mass Assembly (GAMA): ugriz galaxy luminosity functions. MNRAS 420, 1239. ADS

  8. Farrow, D. J., Cole, S., Norberg, P., et al. (2015). Galaxy and mass assembly (GAMA): projected galaxy clustering. MNRAS 454, 2120. ADS

  9. Hahn, C., Kwon, K. J., Tojeiro, R., et al. (2023). The DESI Bright Galaxy Survey: Final Target Selection, Design, and Validation. AJ 165, 253. ADS

k-nearest-neighbor statistics

These papers motivate and define the kNN-CDF estimators in sum_stat.knn.

  1. Banerjee, A. & Abel, T. (2021). Nearest neighbour distributions: new statistical tools for cosmological analysis. MNRAS 500, 5479. ADS

Theoretical background — textbooks and review papers

Essential references for the theoretical framework underlying all estimators.

  1. Peebles, P. J. E. (1980). The Large-Scale Structure of the Universe. Princeton University Press. ADS

  2. Peebles, P. J. E. (1993). Principles of Physical Cosmology. Princeton University Press. ADS

  3. Martínez, V. J. & Saar, E. (2002). Statistics of the Galaxy Distribution. Chapman & Hall/CRC. ADS

  4. Navarro, J. F., Frenk, C. S. & White, S. D. M. (1996). The Structure of Cold Dark Matter Halos. ApJ 462, 563. ADS

  5. Navarro, J. F., Frenk, C. S. & White, S. D. M. (1997). A Universal Density Profile from Hierarchical Clustering. ApJ 490, 493. ADS

  6. Cooray, A. & Sheth, R. (2002). Halo Models of Large Scale Structure. Phys Rep 372, 1. Standard review of the halo model framework. ADS

  7. Berlind, A. A. & Weinberg, D. H. (2002). The Halo Occupation Distribution and the Physics of Galaxy Formation. ApJ 575, 587. Introduced the halo occupation distribution (HOD) framework. ADS

  8. Zheng, Z., Berlind, A. A., Weinberg, D. H., et al. (2005). Theoretical Models of the Halo Occupation Distribution: Separating Central and Satellite Galaxies. ApJ 633, 791. Standard five-parameter HOD parameterisation. ADS