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Record W4386563139 · doi:10.3847/1538-4357/ace9d9

Investigating the Lower Mass Gap with Low-mass X-Ray Binary Population Synthesis

2023· article· en· W4386563139 on OpenAlexfundno aff
Jared Siegel, Ilia Kiato, Vicky Kalogera, C. P. L. Berry, Thomas J. Maccarone, Katelyn Breivik, Jeff J. Andrews, Simone S. Bavera, Aaron Dotter, Tassos Fragos, Konstantinos Kovlakas, Devina Misra, Kyle A. Rocha, Philipp M. Srivastava, Meng Sun, Zepei Xing, Emmanouil Zapartas

Bibliographic record

VenueThe Astrophysical Journal · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNuclear physics research studies
Canadian institutionsnot available
FundersScience and Technology Facilities CouncilEuropean CommissionChina Scholarship CouncilFlatiron HealthCanadian Institute for Advanced ResearchSimons FoundationNorthwestern UniversitySchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungGordon and Betty Moore FoundationNational Science Foundation
KeywordsPhysicsBinary numberAstrophysicsAstronomyPopulationX-ray binaryLow MassNeutron starStarsMedicine

Abstract

fetched live from OpenAlex

Abstract Mass measurements from low-mass black hole X-ray binaries (LMXBs) and radio pulsars have been used to identify a gap between the most massive neutron stars (NSs) and the least massive black holes (BHs). BH mass measurements in LMXBs are typically only possible for transient systems: outburst periods enable detection via all-sky X-ray monitors, while quiescent periods enable radial velocity measurements of the low-mass donor. We quantitatively study selection biases due to the requirement of transient behavior for BH mass measurements. Using rapid population synthesis simulations ( COSMIC ), detailed binary stellar-evolution models ( MESA ), and the disk instability model of transient behavior, we demonstrate that transient LMXB selection effects introduce observational biases, and can suppress mass-gap BHs in the observed sample. However, we find a population of transient LMXBs with mass-gap BHs form through accretion-induced collapse of an NS during the LMXB phase, which is inconsistent with observations. These results are robust against variations of binary evolution prescriptions. The significance of this accretion-induced collapse population depends upon the maximum NS birth mass <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mi>M</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>NS</mml:mi> <mml:mo>,</mml:mo> <mml:mi>birth</mml:mi> <mml:mo>−</mml:mo> <mml:mi>max</mml:mi> </mml:mrow> </mml:msub> </mml:math> . To reflect the observed dearth of low-mass BHs, COSMIC and MESA models favor <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msub> <mml:mrow> <mml:mi>M</mml:mi> </mml:mrow> <mml:mrow> <mml:mi>NS</mml:mi> <mml:mo>,</mml:mo> <mml:mi>birth</mml:mi> <mml:mo>−</mml:mo> <mml:mi>max</mml:mi> </mml:mrow> </mml:msub> <mml:mo>≲</mml:mo> <mml:mn>2</mml:mn> <mml:msub> <mml:mrow> <mml:mi>M</mml:mi> </mml:mrow> <mml:mrow> <mml:mo>⊙</mml:mo> </mml:mrow> </mml:msub> </mml:math> . In the absence of further observational biases against LMXBs with mass-gap BHs, our results indicate the need for additional physics connected to the modeling of LMXB formation and evolution.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.543
Threshold uncertainty score0.887

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.021
GPT teacher head0.270
Teacher spread0.249 · 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 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

Citations22
Published2023
Admission routes1
Has abstractyes

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