Using GeoSnap Mid-Infrared Detectors for Exoplanet Research on Large Telescopes
Notice bibliographique
Résumé
In the past few decades, the study of planets around other stars, or exoplanets, has flourished. Various methods can be used to discover and characterize these targets, enabling astronomers to learn more about how distant worlds and our own came to be. A key means of studying exoplanets is through direct imaging which can be used to determine a target's luminosity, temperature (and thus radius), and atmospheric abundances. The robust information gained via direct imaging is essential to constraining existing exoplanet models which struggle to accurately predict certain structural and evolutionary properties, including the current luminosity of Jupiter. In this thesis, I advance the study of exoplanets by developing and commissioning new infrared instruments that can be used to study wide-orbit gas giants and brown dwarfs. These targets will enable better constraints on evolutionary models. They are of further interest since they can significantly influence their neighboring terrestrial worlds, possibly enhancing or destroying habitability. At the University of Michigan, I tested multiple GeoSnap detectors in cryogenic environments. These particular GeoSnap arrays have been bonded with photosensitive material that can capture mid-infrared light, making them well-suited for studying exoplanets that emit primarily in this wavelength regime due to their cool temperatures. GeoSnap detectors are at the forefront of mid-infrared technology, featuring a combination of extremely deep wells, fast readouts, high quantum efficiency, and low noise while remaining sensitive from 2 to 13 microns. These traits are critical since Earth's atmosphere and the telescope emission in the mid-infrared is immense and will otherwise saturate most detectors. I characterize properties of the detectors including quantum efficiency and prominent noise sources. Following the characterization of the detectors, they must then be implemented into instruments that can be paired with telescopes. I discuss our implementation of one of the GeoSnap detectors in MIRAC-5, a ground-based mid-infrared instrument on the 6.5 m MMT telescope. MIRAC-5 science cases include estimates of the abundance of nitrogen in nearby brown dwarfs. We commissioned MIRAC-5 on the MMT telescope, calculated the on-sky performance, determined the future sensitivity limits, and built a SNR model code and exposure time calculator for the instrument. I discuss potential applications of the instrument and future work necessary to achieve optimal performance. I pivot to the future of ground-based mid-IR astronomy by discussing the planned METIS instrument on the 39-meter ELT. METIS can observe rocky planets on the order of 2 Earth radii around the very nearest stars and has significant potential to detect at least one small and presumably rocky planet. I conclude by returning to the near future, outlining applications for MIRAC-5 including quantifying the negative impact of megasatellite constellations on infrared observing and constraining exoplanet evolutionary models via observations of poorly sampled gas giants and brown dwarfs (with dynamical mass constraints orbiting stars of known distance and age). With updated MIRAC-5 performance estimates, I rule out the feasibility of nitrogen abundance estimates even with future system upgrades.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,002 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».