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Record W1996260578 · doi:10.1086/589849

The Impact of Halo Properties, Energy Feedback, and Projection Effects on the Mass‐SZ Flux Relation

2008· article· en· W1996260578 on OpenAlexaff

Bibliographic record

VenueThe Astrophysical Journal · 2008
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Chemical Physics Studies
Canadian institutionsMcGill University
Fundersnot available
KeywordsHaloSubstructureRADIUSCluster (spacecraft)Measure (data warehouse)Line-of-sightProjection (relational algebra)Galaxy cluster

Abstract

fetched live from OpenAlex

We present a detailed analysis of the intrinsic scatter in the integrated SZ effect - cluster mass (Y-M) relation, using semi-analytic and simulated cluster samples. Specifically, we investigate the impact on the Y-M relation of energy feedback, variations in the host halo concentration and substructure populations, and projection effects due to unresolved clusters along the line of sight (the SZ background). Furthermore, we investigate at what radius (or overdensity) one should measure the integrated SZE and define cluster mass so as to achieve the tightest possible scaling. We find that the measure of Y with the least scatter is always obtained within a smaller radius than that at which the mass is defined; e.g. for M_{200} (M_{500}) the scatter is least for Y_{500} (Y_{1100}). The inclusion of energy feedback in the gas model significantly increases the intrinsic scatter in the Y-M relation due to larger variations in the gas mass fraction compared to models without feedback. We also find that variations in halo concentration for clusters of a given mass may partly explain why the integrated SZE provides a better mass proxy than the central decrement. Substructure is found to account for approximately 20% of the observed scatter in the Y-M relation. Above M_{200} = 2x10^{14} h^{-1} msun, the SZ background does not significantly effect cluster mass measurements; below this mass, variations in the background signal reduce the optimal angular radius within which one should measure Y to achieve the tightest scaling with M_{200}.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.385
Threshold uncertainty score0.751

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.011
GPT teacher head0.223
Teacher spread0.212 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations49
Published2008
Admission routes1
Has abstractyes

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