Extremely low noise UHF-band amplifiers for square kilometer array
Bibliographic record
Abstract
This paper demonstrates two designs of extremely low noise amplifiers in the low frequency range of 350 MHz to 1070 MHz. Hybrid microwave integrated circuit is adapted for a low noise design at this low frequency range. Discrete passive components with high-Q and large values are selected to integrate with the best low noise transistors to optimize the LNA performance. The first UHF band cryogenic LNA was designed with InP HEMTs in all three stages for Square Kilometer Array - mid telescope band-1 receiver. This LNA extended the low end frequency to 350 MHz, and achieved averaging 1.4 Kelvin of a record low noise temperature, more than 47 dB gain, and good input and output return losses < -10 dB over the broad bandwidth from 350 to 1050 MHz at 15 K. The second UHF band cryogenic LNA was developed for MeerKAT Array, a precursor of Square Kilometer Array. This LNA was designed with InP HEMT transistor at first stage to achieve best low noise performance and GaAs HEMTs for second and third stages to replace InP HEMTs and realize high gain and good amplitude stability at cryogenic temperature. The second LNA achieved a record low noise temperature of averaging 0.6 Kelvin, more than 45 dB gain, and good input and output return losses ≤ -12 dB over the wide bandwidth from 580 to 1070 MHz at 15 K.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".