Custom FPGA Data Acquisition for Experimental Astrophysics
Bibliographic record
Abstract
Field-Programmable Gate Array (FPGA) technology is becoming more advanced and commercially accessible with many off-the-shelf options available for a variety of applications ranging from the casual hobbyist to leading edge hardware development research requiring precision real-time operation. FPGAs allow for task-configurable, customizable designs, which can be highly optimized for specific applications. FPGAs are also very easily reconfigurable for other appications via software modification, and are thus very useful for use in an experimental physics laboratory. The Digilent Cmod A7 available for ~$90 USD was selected as an ideal solution for an application requiring 43 digital I/O pins, sub-microsecond level timing accuracy, and a state-machine cycle rates in excess of 100 kHz. This work presents the use of the CmodA7 FPGA module as part of a custom electronic data acquisition system for a laboratory-based interferometry testbed supporting the development of instrument concepts and techniques for experimental astrophysics. Using serial communication over a custom Universal Asynchronous Receiver- Transmitter (UART) designed in the Verilog hardware descriptive language, we demonstrated non-standard serial baud rates up to 4 Mbps, allowing for serial transmission rates over 2 Mbps. This work presents the validation of this data acquisition (DAQ) system, and identification of the barriers to higher data rates through the UART interface. We also present preliminary results from other FPGA interface options using USB and ethernet communication which all significantly higher data rates, at the cost of increased complexity in FPGA design and the associated development and validation cycle(s). *Indicates presenter
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.035 | 0.006 |
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".