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Record W2921985287 · doi:10.48550/arxiv.1903.06218

Astro2020 Science White Paper: First Stars and Black Holes at Cosmic Dawn with Redshifted 21-cm Observations

2019· preprint· en· W2921985287 on OpenAlexaff
Jordan Mirocha, Daniel Jacobs, Joshua S. Dillon, Steve R. Furlanetto, Jonathan C. Pober, Adrian Liu, James Aguirre, Yacine Ali-Haïmoud, Marcelo A. Alvarez, Adam P. Beardsley, George D. Becker, Judd D. Bowman, Patrick C. Breysse, Volker Bromm, Jack O. Burns, Xuelei Chen, Tzu‐Ching Chang, H. C. Chiang, J. D. Cohn, David R. DeBoer, Cora Dvorkin, Anastasia Fialkov, Nickolay Y. Gnedin, B. J. Hazelton, Masui Kiyoshi, Saul A. Kohn, L. V. E. Koopmans, Ely D. Kovetz, Paul La Plante, Adam Lidz, Yin-Zhe Ma, Yi Mao, Andrei Mesinger, Julián B. Muñoz, Steven Murray, Aaron R. Parsons, Jonathan R. Pritchard, Jonathan Sievers, Eric R. Switzer, Nithyanandan Thyagarajan, Eli Visbal, Matías Zaldarriaga

Bibliographic record

VenuearXiv (Cornell University) · 2019
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicRadio Astronomy Observations and Technology
Canadian institutionsMcGill University
FundersHigh Energy PhysicsOffice of ScienceU.S. Department of Energy
KeywordsPhysicsRedshiftStarsAstrophysicsReionizationCOSMIC cancer databaseIntergalactic travelAstronomySkyBlack hole (networking)Galaxy

Abstract

fetched live from OpenAlex

The "cosmic dawn" refers to the period of the Universe's history when stars and black holes first formed and began heating and ionizing hydrogen in the intergalactic medium (IGM). Though exceedingly difficult to detect directly, the first stars and black holes can be constrained indirectly through measurements of the cosmic 21-cm background, which traces the ionization state and temperature of intergalactic hydrogen gas. In this white paper, we focus on the science case for such observations, in particular those targeting redshifts z $\gtrsim$ 10 when the IGM is expected to be mostly neutral. 21-cm observations provide a unique window into this epoch and are thus critical to advancing first star and black hole science in the next decade.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.068
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.036
GPT teacher head0.168
Teacher spread0.131 · 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.

Study designObservational
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

Citations4
Published2019
Admission routes1
Has abstractyes

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