Beyond CCDs: characterization of sCMOS detectors for optical astronomy
Bibliographic record
Abstract
Modern scientific complementary metal-oxide semiconductor (sCMOS) detectors provide a highly competitive alternative to charge-coupled devices (CCDs), the latter of which have historically been dominant in optical imaging. sCMOS boast comparable performances to CCDs with faster frame rates, lower read noise, and a higher dynamic range. Furthermore, their lower production costs are shifting the industry to abandon CCD support and production in favour of CMOS, making their characterization urgent. In this work, we characterized a variety of high-end commercially available sCMOS detectors to gauge the state of this technology in the context of applications in optical astronomy. We evaluated a range of sCMOS detectors, including larger pixel models such as the Teledyne Prime 95B and the Andor Sona-11, which are similar to CCDs in pixel size and suitable for wide-field astronomy. Additionally, we assessed smaller pixel detectors like the Ximea xiJ and Andor Sona-6, which are better suited for deep-sky imaging. Furthermore, high-sensitivity quantitative sCMOS detectors such as the Hamamatsu Orca-Quest C15550-20UP, capable of resolving individual photoelectrons, were also tested. In-lab testing showed low levels of dark current, read noise, faulty pixels, and fixed pattern noise, as well as linearity levels above 98% across all detectors. The Orca-Quest had particularly low noise levels with a dark current of 0.0067 ± 0.0003 e−/s (at −20◦C with air cooling) and a read noise of 0.37 ± 0.09 e− using its standard readout mode. Our tests revealed that the latest generation of sCMOS detectors excels in optical imaging performance, offering a more accessible alternative to CCDs for future optical astronomy instruments.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".