The Four-Year COBE Normalization and Large-Scale Structure

Emory F. Bunn (U.C. Berkeley); Martin White (Fermilab)

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The Four-Year COBE Normalization and Large-Scale Structure

Emory F. Bunn (U.C. Berkeley), Martin White (Fermilab)

Astrophysics · Submitted 12 Jul 1996

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The authors' main claim We use the COBE normalization to compute small-scale fluctuation amplitudes for a variety of models and discuss the implications of these results for theories of large-scale structure.

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arXiv IDastro-ph/9607060 (version 2)

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CitationEmory F. Bunn (U.C. Berkeley), Martin White (Fermilab). "The Four-Year COBE Normalization and Large-Scale Structure." arXiv:astro-ph/9607060 (1996). https://pith.science/paper/WYISGZ76

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What the abstract itself states, as probabilities: claims first 0.03 · states limitation 0.23 · human participants 0.01 · controlled comparison 0.04 · randomized assignment 0.01 · states quantitative result 0.96 · compares with prior results 0.11 · mentions code or data availability 0.02.

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arXiv listing abstract v2. Results and the main claim are underlined; show every role.

We present an analysis of the four-year data from the COBE DMR experiment. We use a Karhunen-Loeve expansion of the pixel data to calculate the normalization and goodness-of-fit of a range of models of structure formation. This technique produces unbiased normalization estimates and is capable of determining the normalization of any particular model with a statistical uncertainty of 7%. We present a parameterization of the normalization and likelihood function which summarizes our results for a wide range of models. We use the COBE normalization to compute small-scale fluctuation amplitudes for a variety of models and discuss the implications of these results for theories of large-scale structure.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#	sentence	role	the authors' own work	states the main conclusion	strength
s0	We present an analysis of the four-year data from the COBE DMR experiment.	contribution 0.92	0.93	0.03	asserted 0.89
s1	We use a Karhunen-Loeve expansion of the pixel data to calculate the normalization and goodness-of-fit of a range of models of structure formation.	method 0.99	0.96	0.04	asserted 0.99
s2	This technique produces unbiased normalization estimates and is capable of determining the normalization of any particular model with a statistical uncertainty of 7%.	method 0.71	0.87	0.15	asserted 1.00
s3	We present a parameterization of the normalization and likelihood function which summarizes our results for a wide range of models.	contribution 0.92	0.96	0.12	asserted 1.00
s4	We use the COBE normalization to compute small-scale fluctuation amplitudes for a variety of models and discuss the implications of these results for theories of large-scale structure.	interpretation 0.73	0.94	0.22	asserted 0.90

What kind of paper the classifier reports this is: empirical study 0.63 · evidence from analysis of existing data 0.99.

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arXiv listing abstract v2

not examined

full text
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T body sentences
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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

We present an analysis of the four-year data from the COBE DMR experiment. We use a Karhunen-Loeve expansion of the pixel data to calculate the normalization and goodness-of-fit of a range of models of structure formation. This technique produces unbiased normalization estimates and is capable of determining the normalization of any particular model with a statistical uncertainty of 7%. We present a parameterization of the normalization and likelihood function which summarizes our results for a wide range of models. We use the COBE normalization to compute small-scale fluctuation amplitudes for a variety of models and discuss the implications of these results for theories of large-scale structure.

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References

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Cited by 7 Pith papers

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How the literature uses it

11 arXiv papers cite this in 11 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 data or values 1 paper

From the observed amplitude of the density perturbations on the COBE scale [ref]

Inflation in minimal left-right symmetric model with spontaneous D-parity breaking · Introduction · 0.97

Compares with it 1 paper

Our results are similar to earlier studies [ref] , although Reiprich [ref] has found that the HIFLUGCS cluster survey is in still better agreement with the WMAP 3-year [ref] and COBE 4-year data [ref] .

Sensitivity and Insensitivity of Galaxy Cluster Surveys to New Physics · Flux Limited Cluster Counts for Standard Cosmology · 0.98

Cites it as background 3 papers

This problem is related to the “sigma 8 tension” and raises questions concerning the empirical normalisation (based on the abundance of galaxy clusters, e.g. [ref] ) of the power spectrum of perturbations generated during inflation and how this transfers during linear structure growth to the power spectrum of matter at $z_e ≈130$ used to seed cosmological structure formation simulations (e.g. [ref] ).

The many tensions with dark-matter based models and implications on the nature of the Universe · Tensions between the SMoC and observations · 0.81
Most cosmological constraints come from far-away objects such as supernovae [ref] and large scale structures [ref] but nearby objects can be considered as well.

Jeans Analysis of Bok globules in $f(R)$ gravity · Constraints for $f(R)$ models · 0.90
Cosmic microwave background [ref] and supernova observations [ref] lead to the discovery of accelerating expansion of the Universe.

f(R) gravity constraints from gravitational waves · Introduction · 0.80

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