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Enregistrement W7024885207

Star Formation and Quenching in Galaxies: Groups, Clusters, and Mergers

2023· dissertation· en· W7024885207 sur OpenAlexfundno aff

Notice bibliographique

RevueUWSpace (University of Waterloo) · 2023
Typedissertation
Langueen
DomaineEngineering
ThématiqueThermodynamic and Structural Properties of Metals and Alloys
Établissements canadiensnon disponible
Organismes subventionnairesScience and Technology Facilities CouncilNatural Sciences and Engineering Research Council of CanadaNuclear Safety and Security CommissionDurham UniversityNational Aeronautics and Space AdministrationNational Science Foundation
Mots-clésStar formationGalaxyQuenching (fluorescence)Stellar massHaloGalaxy groupGalaxy formation and evolution
DOInon disponible

Résumé

récupéré en direct d'OpenAlex

This thesis studies the impact of galaxy environment on star formation and ‘quenching’, by using simple physically-motivated models that can be fit using available observed quantities. Quenching refers to the close to total suppression, whether gradual or abrupt, of star formation in a galaxy, and remains a challenging process to understand due to the many tangled non-linear physical processes involved in galaxy formation. By examining the effects of specific environments on star formation, we are effectively given naturally controlled experiments. In particular, this work addresses gaps in our understanding of quenching during and prior to (‘pre-processing’) infall into galaxy groups and clusters, as well as the poorly studied star formation burst that occurs when one galaxy merges with another.
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\nThe first section of this thesis presents observed properties of galaxies of the GOGREEN 1 < z < 1.5 galaxy groups. Using publicly available COSMOS and SXDF, with supporting GOGREEN spectroscopic data for confirming group properties, I use background subtraction to determine stellar mass functions of groups at z > 1 for the first time. I see enhanced quenching in higher mass galaxies in these groups compared to galaxies in the average field population. Using this result and previously published work measuring the quiescent fraction of galaxies for GOGREEN 1 < z < 1.5 clusters, as well as similar measurements at lower redshifts, I find a halo mass dependence of quiescent fraction excess when controlled for stellar mass, with logarithmic slope, d(QFE)/dlog(Mhalo) ∼ 0.24 ± 0.04 at all redshifts. I find this trend is qualitatively reproduced in the BAHAMAS hydrodynamical simulation at z ∼ 1, but not the increasing quenched fraction with stellar mass trend. I then interpret my observational results using two toy accretion-quenching models. From this analysis, time until quenching in a group/cluster appears to be shorter for larger halos, with a particularly intense dependence required if there is no pre-processing. Our results strongly support a scenario where environmental quenching begins in low-mass < 10^14M⊙ at z > 1. 
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\nThe second section turns to infall quenching and preprocessing in z ∼ 0 galaxy clusters using SDSS data. Numerous works have looked at low redshift clusters using quiescent fractions and star formation rates, but struggle to break degeneracies in infall quenching timescales or come to agreement. To address this, I build on a string of works by Kyle Oman and Michael Hudson, which employ statistical infall time in projected phase space information from N-body simulations, by adding an additional observable: spectroscopically derived mass-weighted stellar ages (MWAs). I then forward model the MWAs and quiescent fractions in projected phase space using star-formation histories from the stochastic UniverseMachine model, finding overall infall quenching times of ∼ 4 Gyr after first pericentre. The use of MWAs enables breaking degeneracy in a two-parameter model, yielding both time of quenching onset and SFR suppression timescale for our stellar mass bins 9 < log(M⋆/M⊙) < 10 and 10 < log(M⋆/M⊙) < 10.5. The results of this modeling suggest quenching begins close to, or just after first pericentre, but the suppression timescale is relatively long (∼ 2.3 Gyr versus τ < 1 Gyr) for the higher stellar mass bin, indicating ram-pressure stripping is not complete on first pericentric passage. Prior works required short suppression timescales to maintain the SFR bimodality, but we show that the use of stochastic star formation histories removes the need for this constraint.
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\nThe third section determines the mass of stars created when two galaxies merge, a long-standing unknown due to not having large and pure samples of post-merger galaxies until this past year (2022). In particular, I forward model the difference in stellar age between post-coalescence mergers and a control sample, controlling for stellar mass, environmental density, and redshift. We find an age difference of up to 3 Gyr, best fit by a stellar mass burst fraction of 0.18 ± 0.02, consistent with some previously published measurements, but much higher than found in hydrodynamical simulations. Our model is robust to choice of analytic star formation history as well as differences in burst duration. Using published SFRs of Luminous InfraRed Galaxies (LIRGs), we estimate a burst duration of 120–250 Myr, which is consistent with simulations and longer than is estimated for post-starbursts in the literature. We find our stellar mass burst fraction is consistent with the amount of molecular gas reported for very close pairs (pre-coalescence) in the literature. Additionally, we find that the difference between published cold gas measurements for pre- and post-coalescence is consistent with our estimated stellar mass burst fraction, lending credence to our approach.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,000
score de la tête « metaresearch » (Gemma)0,000
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: Qualitatif
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,246
Score d'incertitude au seuil0,997

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,006
Tête enseignante GPT0,166
Écart entre enseignants0,159 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeQualitatif
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2023
Routes d'admission1
Résumé présentoui

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