TESLA-X: an effective method to search for subthreshold lensed gravitational waves with a targeted population model
Bibliographic record
Abstract
ABSTRACT Strong gravitational lensing can produce copies of gravitational-wave signals from the same source with the same waveform morphologies but different amplitudes and arrival times. Some of these strongly lensed gravitational-wave signals can be demagnified and become subthreshold. We present TESLA-X, an enhanced approach to the original GstLAL-based TargetEd Subthreshold Lensing seArch (TESLA) method, for improving the detection efficiency of these potential subthreshold lensed signals. TESLA-X utilizes lensed injections to generate a targeted population model and a targeted template bank. We compare the performance of a full template bank search, TESLA, and TESLA-X methods via a simulation campaign, and demonstrate the performance of TESLA-X in recovering lensed injections, particularly targeting a mock event. Our results show that the TESLA-X method achieves a maximum of $\sim 10\%$ higher search sensitivity compared to the TESLA method within the subthreshold regime, presenting a step towards detecting the first lensed gravitational wave. TESLA-X will be employed for the LIGO–Virgo–KAGRA’s collaboration-wide analysis to search for lensing signatures in gravitational waves in the fourth observing run.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".