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Record W4394684094 · doi:10.3847/1538-4365/ad24e2

The Initial Mass Function Based on the Full-sky 20 pc Census of ∼3600 Stars and Brown Dwarfs

2024· article· en· W4394684094 on OpenAlexaff
J. Davy Kirkpatrick, Federico Marocco, Christopher R. Gelino, Yadukrishna Raghu, Jacqueline K. Faherty, Daniella C. Bardalez Gagliuffi, Steven D. Schurr, Kevin Apps, Adam C. Schneider, Aaron Meisner, Marc J. Kuchner, Dan Caselden, R. L. Smart, S. L. Casewell, R. Raddi, Aurora Y. Kesseli, Nikolaj Stevnbak Andersen, Edoardo Antonini, Paul Beaulieu, Thomas P. Bickle, Martin Bilsing, Raymond Chieng, Guillaume Colin, Sam Deen, Alexandru Dereveanco, Katharina Doll, Hugo A. Durantini Luca, Anya Frazer, Jean Marc Gantier, Léopold Gramaize, Kristin Grant, Leslie K. Hamlet, Hiro 村滉 Higashimura 東, Michiharu Hyogo, Peter A. Jałowiczor, Alexander F. Jonkeren, Martin Kabatnik, Frank Kiwy, David W. Martin, Marianne N. Michaels, William Pendrill, Celso Pessanha Machado, Benjamin Pumphrey, Austin Rothermich, Rebekah Russwurm, Arttu Sainio, John Sánchez, Fyodor Theo Sapelkin-Tambling, J. Schümann, Karl Selg-Mann, Harshdeep Singh, Andrés Stenner, G. Sun, Christopher Tanner, Melina Thévenot, Maurizio Ventura, Nikita V. Voloshin, Jim Walla, Zbigniew Wędracki, Jose I. Adorno, Christian Aganze, Katelyn Allers, Hunter Brooks, Adam J. Burgasser, Emily Calamari, Thomas Connor, E. Costa, Peter Eisenhardt, Jonathan Gagné, Roman Gerasimov, Eileen C. Gonzales, Chih-Chun Hsu, Rocio Kiman, Guodong Li, Ryan Low, Eric E. Mamajek, B. Pantoja, Mark Popinchalk, Jon M. Rees, Daniel Stern, Genaro Suárez, Christopher A. Theissen, Chao‐Wei Tsai, Johanna M. Vos, David Zurek

Bibliographic record

VenueThe Astrophysical Journal Supplement Series · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicStellar, planetary, and galactic studies
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsBrown dwarfSkyStarsInitial mass functionAstronomyCensusFunction (biology)PhysicsAstrophysicsStar formationDemographyBiologySociology

Abstract

fetched live from OpenAlex

Abstract A complete accounting of nearby objects—from the highest-mass white dwarf progenitors down to low-mass brown dwarfs—is now possible, thanks to an almost complete set of trigonometric parallax determinations from Gaia, ground-based surveys, and Spitzer follow-up. We create a census of objects within a Sun-centered sphere of 20 pc radius and check published literature to decompose each binary or higher-order system into its separate components. The result is a volume-limited census of ∼3600 individual star formation products useful in measuring the initial mass function across the stellar (&lt;8 M ⊙ ) and substellar (≳5 M Jup ) regimes. Comparing our resulting initial mass function to previous measurements shows good agreement above 0.8 M ⊙ and a divergence at lower masses. Our 20 pc space densities are best fit with a quadripartite power law, <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>ξ</mml:mi> <mml:mo stretchy="false">(</mml:mo> <mml:mi>M</mml:mi> <mml:mo stretchy="false">)</mml:mo> <mml:mo>=</mml:mo> <mml:mi mathvariant="italic">dN</mml:mi> <mml:mrow> <mml:mo stretchy="true">/</mml:mo> </mml:mrow> <mml:mi mathvariant="italic">dM</mml:mi> <mml:mo>∝</mml:mo> <mml:msup> <mml:mrow> <mml:mi>M</mml:mi> </mml:mrow> <mml:mrow> <mml:mo>−</mml:mo> <mml:mi>α</mml:mi> </mml:mrow> </mml:msup> </mml:math> , with long-established values of α = 2.3 at high masses (0.55 &lt; M &lt; 8.00 M ⊙ ), and α = 1.3 at intermediate masses (0.22 &lt; M &lt; 0.55 M ⊙ ), but at lower masses, we find α = 0.25 for 0.05 &lt; M &lt; 0.22 M ⊙ , and α = 0.6 for 0.01 &lt; M &lt; 0.05 M ⊙ . This implies that the rate of production as a function of decreasing mass diminishes in the low-mass star/high-mass brown dwarf regime before increasing again in the low-mass brown dwarf regime. Correcting for completeness, we find a star to brown dwarf number ratio of, currently, 4:1, and an average mass per object of 0.41 M ⊙ .

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.320
Threshold uncertainty score0.626

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 studies0.0010.000
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 head0.014
GPT teacher head0.237
Teacher spread0.223 · 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 designTheoretical or conceptual
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

Citations97
Published2024
Admission routes1
Has abstractyes

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