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Record W3012214611 · doi:10.48550/arxiv.1712.07345

Towards the Design of Gravitational-Wave Detectors for Probing Neutron-Star Physics

2017· article· en· W3012214611 on OpenAlexaff
H. Miao, Huan Yang, Д. В. Мартынов

Bibliographic record

VenueDSpace@MIT (Massachusetts Institute of Technology) · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPulsars and Gravitational Waves Research
Canadian institutionsUniversity of GuelphPerimeter Institute
Fundersnot available
KeywordsPhysicsGravitational waveNeutron starDetectorEinstein TelescopeSupernovaLIGONoise (video)NeutronGravitational-wave astronomySensitivity (control systems)AstrophysicsOpticsNuclear physicsElectronic engineering

Abstract

fetched live from OpenAlex

The gravitational waveform of merging binary neutron stars encodes information about extreme states of matter. Probing these gravitational emissions requires the gravitational-wave detectors to have high sensitivity above 1 kHz. Fortunately for current advanced detectors, there is a sizeable gap between the quantum-limited sensitivity and the classical noise at high frequencies. Here we propose a detector design that closes such a gap by reducing the high-frequency quantum noise with an active optomechanical filter, frequency-dependent squeezing, and high optical power. The resulting noise level from 1 kHz to 4 kHz approaches the current facility limit and is a factor of 20 to 30 below the design of existing advanced detectors. This will allow for precision measurements of (i) the post-merger signal of the binary neutron star, (ii) late-time inspiral, merger, and ringdown of low-mass black hole-neutron star systems, and possible detection of (iii) high-frequency modes during supernovae explosions. This design tries to maximize the science return of current facilities by achieving a sensitive frequency band that is complementary to the longer-baseline third-generation detectors: the10 km Einstein Telescope, and 40 km Cosmic Explorer. We have highlighted the main technical challenges towards realizing the design, which requires dedicated research programs. If demonstrated in current facilities, the techniques can be transferred to new facilities with longer baselines.

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: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.159
Threshold uncertainty score0.762

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 studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.048
GPT teacher head0.322
Teacher spread0.274 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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