Unveiling the dynamic infrared sky with Gattini-IR
Bibliographic record
Abstract
While optical and radio transient surveys have enjoyed a renaissance over the past decade, the dynamic infrared sky remains virtually unexplored. The infrared is a powerful tool for probing transient events in dusty regions that have high optical extinction, and for detecting the coolest of stars that are bright only at these wavelengths. The fundamental roadblocks in studying the infrared time-domain have been the overwhelmingly bright sky background (250 times brighter than optical) and the narrow field-of-view of infrared cameras (largest is 0.6 sq deg). To begin to address these challenges and open a new observational window in the infrared, we present Palomar Gattini-IR: a 25 sq degree, 300mm aperture, infrared telescope at Palomar Observatory that surveys the entire accessible sky (20,000 sq deg) to a depth of 16.4 AB mag (J band, 1.25μm) every night. Palomar Gattini-IR is wider in area than every existing infrared camera by more than a factor of 40 and is able to survey large areas of sky multiple times. We anticipate the potential for otherwise infeasible discoveries, including, for example, the elusive electromagnetic counterparts to gravitational wave detections. With dedicated hardware in hand, and a F/1.44 telescope available commercially and cost-effectively, Palomar Gattini-IR will be on-sky in early 2017 and will survey the entire accessible sky every night for two years. We present an overview of the pathfinder Palomar Gattini-IR project, including the ambitious goal of sub-pixel imaging and ramifications of this goal on the opto-mechanical design and data reduction software. \n \nPalomar Gattini-IR will pave the way for a dual hemisphere, infrared-optimized, ultra-wide field high cadence machine called Turbo Gattini-IR. To take advantage of the low sky background at 2.5 μm, two identical systems will be located at the polar sites of the South Pole, Antarctica and near Eureka on Ellesmere Island, Canada. Turbo Gattini-IR will survey 15,000 sq. degrees to a depth of 20AB, the same depth of the VISTA VHS survey, every 2 hours with a survey efficiency of 97%.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".