Progress toward optimizing energy and arrival-time resolution with a transition-edge sensor
Bibliographic record
Abstract
Superconducting transition-edge sensors (TESs) carried by X-ray telescopes are powerful tools for the study of neutron stars and black holes. Several methods, such as optimal filtering or principal component analysis, have already been developed to analyse X-ray data from these sensors. However, these techniques may be hard to implement in space. Our goal is to develop a lower-computational-cost technique that optimizes energy and time resolution when X-ray photons are detected by a TES. TESs exhibit a non-linear response with photon energy. Therefore, at low energies we focus on the current-pulse height whereas at high energies we consider the current-pulse width, to retrieve energy and arrival time of X-ray photons. For energies between 0.1 keV and 30 keV and with a sampling rate of 195 kHz, we obtain an energy resolution (full width at half the maximum) between 1.32 eV and 2.98 eV. We also get an arrival-time resolution (full duration at half the maximum) between 163 ns and 3.85 ns. To improve the accuracy of these results it will be essential to get a thorough description of non-stationary noise in a TES, and to develop a robust on-board identification method of pile-up events.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".