The UTMOST: A Hybrid Digital Signal Processor Transforms the Molonglo Observatory Synthesis Telescope
Bibliographic record
Abstract
Abstract The Molonglo Observatory Synthesis Telescope (MOST) is an 18000 m2 radio telescope located 40 km from Canberra, Australia. Its operating band (820–851 MHz) is partly allocated to telecommunications, making radio astronomy challenging. We describe how the deployment of new digital receivers, Field Programmable Gate Array-based filterbanks, and server-class computers equipped with 43 Graphics Processing Units, has transformed the telescope into a versatile new instrument (UTMOST) for studying the radio sky on millisecond timescales. UTMOST has 10 times the bandwidth and double the field of view compared to the MOST, and voltage record and playback capability has facilitated rapid implementaton of many new observing modes, most of which operate commensally. UTMOST can simultaneously excise interference, make maps, coherently dedisperse pulsars, and perform real-time searches of coherent fan-beams for dispersed single pulses. UTMOST operates as a robotic facility, deciding how to efficiently target pulsars and how long to stay on source via real-time pulsar folding, while searching for single pulse events. Regular timing of over 300 pulsars has yielded seven pulsar glitches and three Fast Radio Bursts during commissioning. UTMOST demonstrates that if sufficient signal processing is applied to voltage streams, innovative science remains possible even in hostile radio frequency environments.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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".