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Record W3011975349 · doi:10.1117/12.2629373

The EXoplanet Climate Infrared TElescope (EXCITE)

2022· article· en· W3011975349 on OpenAlexaff
Peter C. Nagler, Lee Bernard, Andrea Bocchieri, N. Butler, Quentin Changeat, Azzurra D'Alessandro, Billy Edwards, John Gamaunt, Qian Gong, John Hartley, Kyle Helson, Logan Jensen, Daniel P. Kelly, Kanchita Klangboonkrong, Annalies Kleyheeg, Nikole Lewis, Steven Li, Michael R. Line, Stephen F. Maher, Ryan S. McClelland, Laddawan Miko, Lorenzo V. Mugnai, Barth Netterfield, Vivien Parmentier, E. Pascale, Jennifer Patience, Tim Rehm, Javier Romualdez, Subhajit Sarkar, Paul A. Scowen, Gregory S. Tucker, Augustyn Waczynski, I. Waldmann

Bibliographic record

VenueGround-based and Airborne Instrumentation for Astronomy IX · 2022
Typearticle
Languageen
FieldPhysics and Astronomy
TopicStellar, planetary, and galactic studies
Canadian institutionsUniversity of Toronto
FundersGoddard Space Flight CenterRhode Island Space Grant ConsortiumNational Aeronautics and Space Administration
KeywordsExoplanetSpectrographPlanetPhysicsSpitzer Space TelescopeInfraredAstronomyInfrared telescopeTelescopeJupiter (rocket family)Remote sensingAstrobiologySpace explorationSpectral lineGeology

Abstract

fetched live from OpenAlex

The EXoplanet Climate Infrared TElescope (EXCITE) is a 0.5 meter near-infrared spectrograph that will fly from a high altitude balloon platform. EXCITE is designed to perform phase-resolved spectroscopy – continuous spectroscopic observations of a planet’s entire orbit about its host star – of transiting hot Jupiter-type exoplanets. With spectral coverage from 0.8 – 4 um, EXCITE will measure the peak of a target’s spectral energy distribution and the spectral signatures of many hydrogen and carbon-containing molecules. Phase curve observations are highly resource intensive, especially for shared-use facilities, and they require exceptional photometric stability that is difficult to achieve, even from space. In this work, we introduce the EXCITE experiment and explain how it will solve both these problems. We discuss its sensitivity and stability, then provide an update on its current status as we work toward a 2024 long duration science flight.

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)0.003
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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.018
Threshold uncertainty score0.059

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0180.009

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 head0.010
GPT teacher head0.224
Teacher spread0.214 · 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 designBench or experimental
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

Citations7
Published2022
Admission routes1
Has abstractyes

Explore more

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