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Enregistrement W6940301662 · doi:10.7907/akwv-r373

Probing the Nature of Black Holes with Gravitational Waves

2020· article· en· W6940301662 sur OpenAlexfundno aff

Notice bibliographique

RevueCaltechTHESIS (California Institute of Technology) · 2020
Typearticle
Langueen
DomaineAgricultural and Biological Sciences
ThématiqueMycorrhizal Fungi and Plant Interactions
Établissements canadiensnon disponible
Organismes subventionnairesCalifornia State University, FullertonCompute CanadaGovernment of OntarioNational Aeronautics and Space AdministrationCalifornia Institute of TechnologyNational Science FoundationSherman Fairchild FoundationUniversity of TorontoResearch Corporation for Science Advancement
Mots-clésBinary black holeBlack hole (networking)Intermediate-mass black holeGravitational waveStellar black holeSpin-flipSupermassive black holeWhite hole

Résumé

récupéré en direct d'OpenAlex

In this thesis, I present a number of studies intended to improve our understanding of black holes using gravitational waves. Although black holes are relatively well understood from a theory perspective, many questions remain about the nature of the black holes in our Universe. According to general relativity, astrophysical black holes are fully described by just their mass and spin. Yet, relying on electromagnetic-based observatories alone, we still know very little about the distribution of black hole masses or spins. Moreover, as merging black holes are invisible to these electromagnetic observatories, we cannot rely on them to provide us with information about the binary black hole merger rate or binary black hole formation channels. However, by observing gravitational wave signals from these inherently dark binaries, we will soon have some answers to these questions. Indeed, the Laser Interferometer Gravitational-Wave Observatory (LIGO) has already revealed a great deal of new information about binary black holes; giving us an early glimpse into their mass and spin distributions and placing the first constraints on the binary black hole merger rate. This thesis contributes to the goal of probing the nature of black holes with gravitational waves. Binary black holes can form as an isolated binary in the galactic field or through dynamical encounters in high-density environments. Dynamical formation can significantly alter the binary parameters, which then become imprinted on the gravitational waveform. By simulating varying black hole populations in high-density globular clusters, we identify a population of highly eccentric binary black hole mergers that are characteristic of dynamical formation. Although these systems would circularize by the time they are visible in LIGO's frequency band, the future Laser Interferometer Space Antenna (LISA) is capable of distinguishing this population of eccentric mergers from the circular mergers expected of isolated field-formed binaries. As these dynamically formed binaries depend on the size of the underlying black hole population in globular clusters, we can utilize the dynamically formed merger rate to infer globular cluster black hole populations -- allowing us to reveal information about binary black hole birth environments. In order to properly estimate the parameters of binary black holes from detected gravitational wave signals, such as their masses and spins, high-accuracy waveforms are a needed. The highest accuracy waveforms are those produced by numerical relativity simulations, which solve the full Einstein equations. Using the Spectral Einstein Code (SpEC), we expand the reach of numerical relativity to simulate binary black holes with nearly extremal spins, i.e., black holes with spins near the maximal value χ = 1. These waveforms are used to calibrate existing waveform approximants used in LIGO data analyses. This ensures that the systematic errors in these approximants are small enough that if highly-spinning systems are observed, the spins are recovered without bias. Although rapidly spinning binaries have remained elusive thus far, these waveforms ensure that the highest-spin systems can be detected in the quest to uncover the spin distribution of black holes. The end state of a binary black hole merger is a newly born, single black hole that rings down like a struck bell, sending its last few ripples of gravitational waves out into the spacetime. Embedded in this 'ringdown' signal are a multitude of specific frequencies. Einstein's theory of general relativity precisely predicts the ringdown frequencies of a black hole with a given mass and spin. The statement that a black hole is entirely described by just these two parameters is known as the no-hair theorem. For black holes that obey the laws of general relativity (and consequently, the no-hair theorem), these frequencies serve as a fingerprint for the black hole. However, if the objects we observe are not Einstein's black holes, but instead something more exotic, the frequencies will not have this property and this would be a spectacular surprise. A minimum of two tones are required for this test, each with an associated frequency and damping time that depend only on the mass and spin. The conventional no-hair test relies on the so-called 'fundamental' tones of a black hole. A test relying on the fundamental modes is not expected to be feasible for another ~10-15 years, after detector sensitivity has improved significantly. However, by analyzing the ringdown of high-accuracy numerical relativity waveforms, we show that modes beyond the fundamental, known as 'overtones', are detectable in current detectors. The overtones are short-lived, but this is countered by the fact that they can initially be much stronger than the fundamental mode. By measuring two tones in the ringdown of GW150914 we perform a first test of the no-hair theorem. While the current constraints are rather loose, this first test serves as a proof of principle. This is just one example of the powerful tests that can be employed with overtones using present day detectors and the even more precise tests that can be accomplished with LISA in the future.

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: Expérimental (laboratoire) · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,562
Score d'incertitude au seuil0,210

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0000,000
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,001
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 Opus0,013
Tête enseignante GPT0,205
Écart entre enseignants0,193 · 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'étudeExpérimental (laboratoire)
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
Publié2020
Routes d'admission1
Résumé présentoui

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