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A Cosmic Variance Cookbook

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Paper breakdownThe authors' claims, conditions and measurements, as JSON Signed receiptProof this review has not been altered
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Benjamin P. Moster, Rachel S. Somerville, Jeffrey A. Newman, Hans-Walter Rix

Cosmology and Nongalactic Astrophysics · Submitted 11 Jan 2010

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim This implies that cosmic variance is a significant source of uncertainty at z=2 for small fields and massive galaxies, while for larger fields and intermediate mass galaxies cosmic variance is less serious.

how it is stated · asserted 0.83 read the record →

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arXiv ID1001.1737 (version 1)
Pith IDFFQROE6G, Pith's permanent record of this paper and its review
CitationBenjamin P. Moster, Hans-Walter Rix, Jeffrey A. Newman, Rachel S. Somerville. "A Cosmic Variance Cookbook." arXiv:1001.1737 (2010). https://pith.science/paper/FFQROE6G
Claim paper

The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

Open the compiled record

Compiled by classifier-1.13-20260917 via OpenRouter, question bank abstract-p1-20260923, 2026-09-23: 1 request, 10,147 input tokens, $0.0004 reported.

What the abstract itself states, as probabilities: claims first 0.04 · states limitation 0.25 · human participants 0.01 · controlled comparison 0.04 · randomized assignment 0.01 · states quantitative result 0.99 · compares with prior results 0.23 · mentions code or data availability 0.96.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

Deep pencil beam surveys (<1 deg^2) are of fundamental importance for studying the high-redshift universe. However, inferences about galaxy population properties are in practice limited by 'cosmic variance'. This is the uncertainty in observational estimates of the number density of galaxies arising from the underlying large-scale density fluctuations. This source of uncertainty can be significant, especially for surveys which cover only small areas and for massive high-redshift galaxies. Cosmic variance for a given galaxy population can be determined using predictions from cold dark matter theory and the galaxy bias. In this paper we provide tools for experiment design and interpretation. For a given survey geometry we present the cosmic variance of dark matter as a function of mean redshift z and redshift bin size Dz. Using a halo occupation model to predict galaxy clustering, we derive the galaxy bias as a function of mean redshift for galaxy samples of a given stellar mass range. In the linear regime, the cosmic variance of these galaxy samples is the product of the galaxy bias and the dark matter cosmic variance. We present a simple recipe using a fitting function to compute cosmic variance as a function of the angular dimensions of the field, z, Dz and stellar mass m*. We also provide tabulated values and a software tool. We find that for GOODS at z=2 and with Dz=0.5 the relative cosmic variance of galaxies with m*>10^11 Msun is ~38%, while it is ~27% for GEMS and ~12% for COSMOS. For galaxies of m*~10^10 Msun the relative cosmic variance is ~19% for GOODS, ~13% for GEMS and ~6% for COSMOS. This implies that cosmic variance is a significant source of uncertainty at z=2 for small fields and massive galaxies, while for larger fields and intermediate mass galaxies cosmic variance is less serious.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 Deep pencil beam surveys (<1 deg^2) are of fundamental importance for studying the high-redshift universe. background 1.00 0.06 0.02 asserted 1.00
s1 However, inferences about galaxy population properties are in practice limited by 'cosmic variance'. background 0.91 0.07 0.05 asserted 1.00
s2 This is the uncertainty in observational estimates of the number density of galaxies arising from the underlying large-scale density fluctuations. background 1.00 0.05 0.03 asserted 0.99
s3 This source of uncertainty can be significant, especially for surveys which cover only small areas and for massive high-redshift galaxies. background 1.00 0.09 0.08 asserted 0.82
s4 Cosmic variance for a given galaxy population can be determined using predictions from cold dark matter theory and the galaxy bias. background 0.82 0.08 0.04 asserted 0.97
s5 In this paper we provide tools for experiment design and interpretation. contribution 1.00 0.86 0.06 asserted 0.97
s6 For a given survey geometry we present the cosmic variance of dark matter as a function of mean redshift z and redshift bin size Dz. contribution 0.89 0.93 0.07 asserted 1.00
s7 Using a halo occupation model to predict galaxy clustering, we derive the galaxy bias as a function of mean redshift for galaxy samples of a given stellar mass range. method 0.96 0.94 0.04 asserted 1.00
s8 In the linear regime, the cosmic variance of these galaxy samples is the product of the galaxy bias and the dark matter cosmic variance. method 0.89 0.44 0.09 asserted 1.00
s9 We present a simple recipe using a fitting function to compute cosmic variance as a function of the angular dimensions of the field, z, Dz and stellar mass m*. method 0.51 0.94 0.14 asserted 0.99
s10 We also provide tabulated values and a software tool. contribution 0.98 0.91 0.04 asserted 1.00
s11 We find that for GOODS at z=2 and with Dz=0.5 the relative cosmic variance of galaxies with m*>10^11 Msun is ~38%, while it is ~27% for GEMS and ~12% for COSMOS. result 1.00 0.94 0.20 asserted 1.00
s12 For galaxies of m*~10^10 Msun the relative cosmic variance is ~19% for GOODS, ~13% for GEMS and ~6% for COSMOS. result 1.00 0.92 0.09 asserted 1.00
s13 This implies that cosmic variance is a significant source of uncertainty at z=2 for small fields and massive galaxies, while for larger fields and intermediate mass galaxies cosmic variance is less serious. interpretation 1.00 0.81 0.70 asserted 0.83

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from simulation or computation 0.64.

What was examined, and what was not

sources read
  • arXiv listing abstract v1
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
levels run
  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

Deep pencil beam surveys (<1 deg^2) are of fundamental importance for studying the high-redshift universe. However, inferences about galaxy population properties are in practice limited by 'cosmic variance'. This is the uncertainty in observational estimates of the number density of galaxies arising from the underlying large-scale density fluctuations. This source of uncertainty can be significant, especially for surveys which cover only small areas and for massive high-redshift galaxies. Cosmic variance for a given galaxy population can be determined using predictions from cold dark matter theory and the galaxy bias. In this paper we provide tools for experiment design and interpretation. For a given survey geometry we present the cosmic variance of dark matter as a function of mean redshift z and redshift bin size Dz. Using a halo occupation model to predict galaxy clustering, we derive the galaxy bias as a function of mean redshift for galaxy samples of a given stellar mass range. In the linear regime, the cosmic variance of these galaxy samples is the product of the galaxy bias and the dark matter cosmic variance. We present a simple recipe using a fitting function to compute cosmic variance as a function of the angular dimensions of the field, z, Dz and stellar mass m*. We also provide tabulated values and a software tool. We find that for GOODS at z=2 and with Dz=0.5 the relative cosmic variance of galaxies with m*>10^11 Msun is ~38%, while it is ~27% for GEMS and ~12% for COSMOS. For galaxies of m*~10^10 Msun the relative cosmic variance is ~19% for GOODS, ~13% for GEMS and ~6% for COSMOS. This implies that cosmic variance is a significant source of uncertainty at z=2 for small fields and massive galaxies, while for larger fields and intermediate mass galaxies cosmic variance is less serious.

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References
How later papers used it

99 later papers cite it

Evidence: substantial. 90 of the citing papers share no author with it, and 51 of those carry a confident label from Pith's citation classifier.

Pith read 31 of the 159 citing sentences, choosing only those whose citation markers point at this paper alone, and wrote the paraphrases from them. The summary, the counts, the evidence level, the links and the quotes come from code; the summary counts the papers listed below. A later paper is listed under a label when Pith's citation classifier gives one of its sentences that label at probability 0.8 or more (0.95 for support), Pith's reading finds that the citing paper itself did it, and a second check confirms the paraphrase against the sentence. This section lists uses only. It does not list criticism or disagreement, so their absence here says nothing about how the paper has been received. Papers that share an author with this one are listed apart, under "By the same authors". A quote is the citing authors' own sentence, shown when their paper's licence is CC BY, CC BY-SA or CC0. JSON

Uses its method or tool 15 of 44 papers with this label · 1 by the same authors

By the same authors

Uses its data or values 1 of 3 papers with this label
How the literature uses it

16 arXiv papers cite this in 21 sentences

Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

Papers citing it · 6 sentences without a confident label

Uses its method or tool 8 papers
Cites it as support 1 paper
Cites it as background 3 papers

What it cites · 3 sentences without a confident label

Uses data or values from 1 paper

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