PHotometry Assisted Spectral Extraction (PHASE) and identification of SNLS supernovae
Bibliographic record
Abstract
\n Aims. We present new extraction and identification techniques for supernova (SN) spectra developed within the Supernova Legacy Survey (SNLS) collaboration.\n Methods. The new spectral extraction method takes full advantage of photometric information from the Canada-France-Hawaï telescope (CFHT) discovery and reference images by tracing the exact position of the supernova and the host signals on the spectrogram. When present, \nthe host \nspatial profile is measured on deep multi-band reference images and is\nused to model the host contribution to the full (supernova + host) signal. The supernova is modelled as a Gaussian function of width equal to the seeing. \nA $\\chi^2$ minimisation provides the flux of each component in each pixel of the \n2D spectrogram.\nFor a host-supernova separation greater than $\\ga$1 pixel, the two components are recovered separately and we do not use a spectral template in contrast to more standard analyses. This new procedure permits a clean extraction of the supernova separately from the host in about 70% of the 3rd year ESO/VLT spectra of the SNLS. A new supernova identification method is also proposed. It uses the SALT2 spectrophotometric template to combine the photometric and spectral data. A galaxy template is allowed for spectra for which a separate extraction of the supernova and the host was not possible.\n Results. These new techniques have been tested against more standard extraction and identification procedures. They permit a secure type and redshift\ndetermination in about 80% of cases. The present paper illustrates their performances on a few sample spectra.\n
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".