Using the 2-MASS photometric redshift survey to optimize LIGO follow-up observations
Bibliographic record
Abstract
The initial discovery of Laser Interferometer Gravitational-Wave Observatory (LIGO) on 2015 September 14 was the inspiral merger and ring-down of the black hole binary at a distance of about 500 Mpc or a redshift of about 0.1. The search for electromagnetic counterparts for the inspiral of binary black holes is impeded by coarse initial source localizations and a lack of a compelling model for the counterpart; therefore, rapid electromagnetic follow-up is required to understand the astrophysical context of these sources. Because astrophysical sources of gravitational radiation are likely to reside in galaxies, it would make sense to search first in regions where the LIGO-Virgo probability is large and where the density of galaxies is large as well. Under the assumption that the probability of a gravitational-wave event from a given region of space is proportional to the density of galaxies within the probed volume, one can calculate an improved localization of the position of the source simply by multiplying the LIGO-Virgo skymap by the density of galaxies in the range of redshifts. We propose using the 2-MASS photometric redshift galaxy catalogue for this purpose and demonstrate that using it can dramatically reduce the search region for electromagnetic counterparts.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".