UTILITY OF GALAXY CATALOGS FOR FOLLOWING UP GRAVITATIONAL WAVES FROM BINARY NEUTRON STAR MERGERS WITH WIDE-FIELD TELESCOPES
Bibliographic record
Abstract
The first detections of gravitational waves from binary neutron star mergers with advanced LIGO and Virgo observatories are anticipated in the next five years. These detections could pave the way for multi-messenger gravitational-wave (GW) and electromagnetic (EM) astronomy if GW triggers are successfully followed up with targeted EM observations. However, GW sky localization is relatively poor, with expected localization areas of ∼10–100 deg 2 ; this presents a challenge for following up GW signals from compact binary mergers. Even for wide-field instruments, tens or hundreds of pointings may be required. Prioritizing pointings based on the relative probability of successful imaging is important since it may not be possible to tile the entire gravitational-wave localization region in a timely fashion. Galaxy catalogs were effective at narrowing down regions of the sky to search in initial attempts at joint GW/EM observations. The relatively limited range of initial GW instruments meant that few galaxies were present per pointing and galaxy catalogs were complete within the search volume. The next generation of GW detectors will have a 10-fold increase in range thereby increasing the expected number of galaxies per unit solid angle by a factor of ∼1000. As an additional complication, catalogs will be highly incomplete. Nevertheless, galaxy catalogs can still play an important role in prioritizing pointings for the next era of GW searches. We show how to quantify the advantages of using galaxy catalogs to prioritize wide-field follow-ups as a function of only two parameters: the three-dimensional volume within the field of view of a telescope after accounting for the GW distance measurement uncertainty, and the fraction of the GW sky localization uncertainty region that can be covered with telescope pointings. We find that the use of galaxy catalogs can improve the probability of successful imaging by ∼10% to ∼300% relative to follow-up strategies that do not utilize such catalogs for the scenarios we considered. We determine that catalogs with a 75% completeness perform comparably to complete catalogs in most cases, while 33%-complete catalogs can lead to lower follow-up success rates than complete catalogs for small fields of view, though still providing an advantage over strategies that do not use a catalog at all.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".