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Record W6959383171 · doi:10.7488/era/2497

Detection and characterisation of young planetary-mass objects: novel techniques and optimised survey strategies

2022· other· en· W6959383171 on OpenAlexaboutno aff

Bibliographic record

VenueERA · 2022
Typeother
Languageen
FieldAgricultural and Biological Sciences
TopicAgricultural Practices and Plant Genetics
Canadian institutionsnot available
Fundersnot available
KeywordsBrown dwarfSerpensPhotometry (optics)ExoplanetPlanetStarsPopulationTelescope

Abstract

fetched live from OpenAlex

Young, low-mass brown dwarfs can be similar in size and composition to young, giant exoplanets. Many exist without host stars and are uncontaminated by starlight, making them useful analogues for studying planets in solar systems. Increasing the population of well-studied brown dwarfs and exoplanets will improve our understanding of the underlying distribution of planets, and of which formation scenarios are viable. Young star-forming regions, such as Serpens and Taurus, are ideal targets when looking for populations of planetary-mass brown dwarfs, as they are relatively nearby, young and active in star formation. In this thesis, I present surveys, past and future, of nearby star-forming regions, conducted in the hope of finding new, very low-mass brown dwarf and planetary-mass members. I also focus on the characterisation of newly-identified individual objects, and of populations as a whole. I aim to demonstrate how custom-designed narrowband photometric filters can be incredibly effective at selecting brown dwarf members of young regions for spectroscopic follow-up. In Chapter 2, I present a survey of the Serpens star-forming region using the novel W-band technique. I obtain photometry using the Wide-field Infrared Camera (WIRCAM) on the Canada-France-Hawaii Telescope (CFHT), and the custom-designed W-band filter, which is centred on the 1.45 µm absorption feature present in brown dwarf atmospheres. I then describe a spectroscopic follow-up campaign, covering J−, H− and K−bands. Finally, I describe a subset of observations using the Hubble Space Telescope (HST), obtained to identify possible low-mass companions or binary components. Using this photometric, spectroscopic, and high-resolution imaging data, I identify five likely-members of Serpens Core and Serpens South, four of which are consistent with having spectral types of M5 or later. In Chapter 3, I describe a future direct imaging survey, optimised to detect young, giant planets using a custom filter and a target list informed by our current understanding of the underlying planet distribution. The survey will use the Near Infrared Camera System (NIX), a high-contrast imager, part of the Enhanced Resolution Imager and Spectrograph (ERIS) instrument that has recently been installed at the Very Large Telescope (VLT). I present the ‘spectral shape’ technique, which uses the custom-designed K−peak filter to efficiently identify promising targets for follow-up observations. I discuss possible targets for such a survey, and conclude that a nearby, young star-forming region is an ideal target to maximise the yield of planet and brown dwarf detections. Finally, in Chapter 4 I use an additional W-band data set to investigate the the form of the initial mass function (IMF) in the Taurus star-forming region, and the question of the possible environmental dependence of the IMF. I combine CFHT and Gaia photometry to isolate likely Taurus members from field contaminants. Using the isolated cluster population, I run multiple Monte Carlo Markov Chain simulations to assess the likely form of the IMF. I use different IMF functional forms (broken power law and log-normal) and Taurus star-formation histories, and find evidence for a spread of stellar ages in Taurus from 1–10 Myr. I also find that both functional forms provide a reasonable fit to the data (with a slight preference for the broken power law), and that the best-fit IMF parameters extracted are consistent with literature values for other clusters and the general Galactic population, supporting the theory of a universal IMF.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.003
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0000.000
Scholarly communication0.0020.002
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.003

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 head0.019
GPT teacher head0.207
Teacher spread0.188 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

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
Published2022
Admission routes1
Has abstractyes

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