STRONG LENS TIME DELAY CHALLENGE. I. EXPERIMENTAL DESIGN
Bibliographic record
Abstract
The time delays between point-like images in gravitational lens systems can be used to measure cosmological parameters. The number of lenses with measured time delays is growing rapidly; the upcoming Large Synoptic Survey Telescope (LSST) will monitor ∼10 3 strongly lensed quasars. In an effort to assess the present capabilities of the community, to accurately measure the time delays, and to provide input to dedicated monitoring campaigns and future LSST cosmology feasibility studies, we have invited the community to take part in a "Time Delay Challenge" (TDC). The challenge is organized as a set of "ladders," each containing a group of simulated data sets to be analyzed blindly by participating teams. Each rung on a ladder consists of a set of realistic mock observed lensed quasar light curves, with the rungs' data sets increasing in complexity and realism. The initial challenge described here has two ladders, TDC0 and TDC1. TDC0 has a small number of data sets, and is designed to be used as a practice set by the participating teams. The (non-mandatory) deadline for completion of TDC0 was the TDC1 launch date, 2013 December 1. The TDC1 deadline was 2014 July 1. Here we give an overview of the challenge, we introduce a set of metrics that will be used to quantify the goodness of fit, efficiency, precision, and accuracy of the algorithms, and we present the results of TDC0. Thirteen teams participated in TDC0 using 47 different methods. Seven of those teams qualified for TDC1, which is described in the companion paper.
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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 | 0.022 | 0.062 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.021 | 0.007 |
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".