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Substructure around M31 : Evolution and Effects

2006· preprint· en· W6948257433 on OpenAlexaff

Bibliographic record

VenuearXiv (Cornell University) · 2006
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicEvolution and Paleontology Studies
Canadian institutionsQueen's UniversityUniversity of Toronto
Fundersnot available
KeywordsSubstructureSurface brightnessPopulationGalaxyHaloVelocity dispersionDark matterDark matter haloRADIUS

Abstract

fetched live from OpenAlex

We investigate the evolution of a population of 100 dark matter satellites orbiting in the gravitational potential of a realistic model of M31. We find that after 10 Gyr, seven subhalos are completely disrupted by the tidal field of the host galaxy. The remaining satellites suffer heavy mass loss and overall, 75% of the mass initially in the subhalo system is tidally stripped. Not surprisingly, satellites with pericentric radius less than 30 kpc suffer the greatest stripping and leave a complex structure of tails and streams of debris around the host galaxy. Assuming that the most bound particles in each subhalo are kinematic tracers of stars, we find that the halo stellar population resulting from the tidal debris follows an r^{-3.5} density profile at large radii. We construct B-band photometric maps of stars coming from disrupted satellites and find conspicuous features similar both in morphology and brightness to the observed Giant Stream around Andromeda. An assumed star formation efficiency of 5-10% in the simulated satellite galaxies results in good agreement with the number of M31 satellites, the V-band surface brightness distribution, and the brightness of the Giant Stream. During the first 5 Gyr, the bombardment of the satellites heats and thickens the disk by a small amount. At about 5 Gyr, satellite interations induce the formation of a strong bar which, in turn, leads to a significant increase in the velocity dispersion of the disk.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.050
Threshold uncertainty score1.000

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 studies0.0000.000
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 head0.030
GPT teacher head0.156
Teacher spread0.127 · 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.

Study designObservational
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

Citations0
Published2006
Admission routes1
Has abstractyes

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