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The High Energy Light Isotope eXperiment program of direct cosmic-ray studies

2024· article· en· W4390990139 on OpenAlexaffabout
S. Coutu, P. Allison, Marta Baiocchi, J. J. Beatty, L. Beaufore, D.H. Calderón, A. Castaño, Yujie Chen, Noah Green, D. S. Hanna, Hayeon Jeon, S. B. Klein, Brandon Kunkler, Mike Lang, Rostom Mbarek, Keith McBride, S. I. Mognet, J. Musser, S. Nutter, S. O’Brien, N. Park, Kate M. Powledge, Kazuo Sakai, M. Tabata, G. Tarlé, J. Tuttle, G. Visser, S. P. Wakely, M. Yu

Bibliographic record

VenueJournal of Instrumentation · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicDark Matter and Cosmic Phenomena
Canadian institutionsMcGill UniversityQueen's University
Fundersnot available
KeywordsPhysicsCosmic rayCherenkov radiationNuclear physicsAstronomyAstrophysicsDetectorOptics

Abstract

fetched live from OpenAlex

Abstract HELIX is a new NASA-sponsored instrument aimed at measuring the spectra and composition of light cosmic-ray isotopes from hydrogen to neon nuclei, in particular the clock isotopes 10 Be (radioactive, with 1.4 Myr lifetime) and 9 Be (stable). The latter are unique markers of the production and Galactic propagation of secondary cosmic-ray nuclei, and are needed to resolve such important mysteries as the proportion of secondary positrons in the excess of antimatter observed by the AMS-02 experiment. By using a combination of a 1 T superconducting magnet spectrometer (with drift-chamber tracker) with a high-resolution time-of-flight detector system and ring-imaging Cherenkov detector, mass-resolved isotope measurements of light cosmic-ray nuclei will be possible up to 3 GeV/n in a first stratospheric balloon flight from Kiruna, Sweden to northern Canada, anticipated to take place in early summer 2024. An eventual longer Antarctic balloon flight of HELIX will yield measurements up to 10 GeV/n, sampling production from a larger volume of the Galaxy extending into the halo. We review the instrument design, testing, status and scientific prospects.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.945
Threshold uncertainty score0.202

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.011
GPT teacher head0.286
Teacher spread0.275 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreEmpirical

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

Citations6
Published2024
Admission routes2
Has abstractyes

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