Sensitivity of the Advanced LIGO detectors at the beginning of gravitational wave astronomy
Bibliographic record
Abstract
The Laser Interferometer Gravitational Wave Observatory (LIGO) consists of two widely separated 4 km laser interferometers designed to detect gravitational waves from distant astrophysical sources in the frequency range from 10 Hz to 10 kHz. The first observation run of the Advanced LIGO detectors started in September 2015 and ended in January 2016. A strain sensitivity of better than ${10}^{\ensuremath{-}23}/\sqrt{\mathrm{Hz}}$ was achieved around 100 Hz. Understanding both the fundamental and the technical noise sources was critical for increasing the astrophysical strain sensitivity. The average distance at which coalescing binary black hole systems with individual masses of $30\text{ }\text{ }{M}_{\ensuremath{\bigodot}}$ could be detected above a signal-to-noise ratio (SNR) of 8 was 1.3 Gpc, and the range for binary neutron star inspirals was about 75 Mpc. With respect to the initial detectors, the observable volume of the Universe increased by a factor 69 and 43, respectively. These improvements helped Advanced LIGO to detect the gravitational wave signal from the binary black hole coalescence, known as GW150914.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".