Bibliographic record
Abstract
The Cryogenic Dark Matter Search is the second iteration of SuperCDMS dark matter experiments with an increase in sensitivity to low mass dark matter particles. The experiment uses germanium and silicon crystals which are capable of detecting both the charges and phonon signals produced from dark matter interactions. Detectors are housed in towers which are then housed in a cryogenics system able to cool down to 15 mK. The experiment will be installed in an underground lab in Sudbury, Ontario in order to shield from cosmic rays and background radiation. SQUIDs are an acronym for superconducting quantum interference devices that are capable of detecting extremely small magnetic fields. A typical dark matter nuclear recoil interaction can be detected by a TES (transition edge sensor). SQUIDs are then used to read out and amplify the signals generated by the TES. It is essential that these SQUIDS add negligible noise to the intrinsic noise of the TES to be able to distinguish the internal circuit noise from a true dark matter interaction signal. The noise was measured while varying several parameters. Both an Agilent and an SRS785 spectrum analyzer were used. Furthermore, an amplifier which amplified the signal 10x was tested. The Agilent machine produced more noticeably greater noise in the region from 100-1000 Hz, whereas the SRS785 did not. Next, the amplifier was tested and the signal generated was analyzed in comparison to the original signal. The amplifier did not produce any noticeable additional noise over the intrinsic noise of the SQUID. Further work includes testing to see if detectors with higher voltage sources can also be used with negligible noise.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".