Searches for Population III pair-instability supernovae: Impact of gravitational lensing magnification
Bibliographic record
Abstract
Abstract Superluminous supernovae have been proposed to arise from Population III progenitors that explode as pair-instability supernovae (PISNe). Population III stars are the first generation of stars in the Universe, and are thought to have formed as late as z ∼ 6. Future near-infrared imaging facilities such as ULTIMATE-Subaru will potentially be able to detect and identify these PISNe with a dedicated survey. Gravitational lensing by intervening structure in the Universe can aid in the detection of these rare objects by magnifying the high-z source population into detectability. We perform a mock survey with ULTIMATE-Subaru, taking into account lensing by line-of-sight (LOS) structure to evaluate its impact on the predicted detection rate. We compare a LOS mass reconstruction using observational data from the Hyper Suprime Cam survey to results from cosmological simulations to test their consistency in calculating the magnification distribution in the Universe to high z, but find that the data-based method is still limited by an inability to accurately characterize structure beyond z ∼ 1.2. We also evaluate a survey strategy of targeting massive galaxy clusters to take advantage of their large areas of high magnification. We find that targeting clusters can result in a gain of a factor of about two in the predicted number of detected PISNe at z > 5, and even higher gains with increasing redshift, given our assumed survey parameters. For the highest-redshift sources at z ∼ 7–9, blank field surveys will not detect any sources, and lensing magnification by massive clusters will be necessary to observe this population.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".