Data Release: "A neural network emulator of the Advanced LIGO and Advanced Virgo selection function"
Bibliographic record
Abstract
This dataset contains results presented in "A neural network emulator of the Advanced LIGO and Advanced Virgo selection function" (arXiv: 2408.16828). The code used to generate this data and produce figures in the paper can be found at https://github.com/tcallister/learning-p-det/. Specific instructions about the workflow are provided in the accompanying documentation. The primary deliverable of this work is a trained neural network emulator for the compact binary selection function during the Advanced LIGO and Advanced Virgo O3 observing run. This emulator is made available in a standalone companion repository, https://github.com/tcallister/pdet. Additional information: The files endo3_bbhpop-LIGO-T2100113-v12.hdf5, endo3_bnspop-LIGO-T2100113-v12.hdf5, and endo3_nsbhpop-LIGO-T2100113-v12.hdf5, used for network training, were created and released by the LIGO-Virgo-KAGRA Collaboration at https://zenodo.org/records/7890437. The file sampleDict_FAR_1_in_1_yr.pickle, used during hierarchical inference, was created via code in the repository https://github.com/tcallister/get-lvk-data. Inference results (popsummary_standardInjections.h5 and popsummary_dynamicInjections.h5) are provided in the popsummary results format; see https://git.ligo.org/christian.adamcewicz/popsummary. Changelog: v2: Added missing file sampleDict_FAR_1_in_1_yr.pickle
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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.048 | 0.082 |
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".