Bibliographic record
Abstract
This "habilitation thesis" consists in a series of three independent review papers, devoted to the recent activities of the author, within the LPNHE cosmology group. The first chapter details how we can map the cosmic expansion history using type Ia supernovae (SNe Ia) as luminosity distance indicators. We detail how the distances to SNe Ia are inferred from the (observer-frame) lightcurves. Finally, we present the measurements recently published by the Supernova Legacy Survey (SNLS). The second chapter is devoted to one of the essential ingredients of the supernova cosmology analyses: the flux calibration of the imagers used to measure the SN fluxes. We discuss the techniques developed to calibrate SNLS (Regnault et al, 2009) and, a few years later, to intercalibrate SNLS and SDSS-II (Betoule et al, 2013). In the last chapter, we present the innovative instrumental calibration techniques designed by several teams around the world. We discuss in particular the DICE project, developed within the LPNHE cosmology group. At the heart of DICE is a ultra-stable, multi-wavelength calibration source, built at LPNHE and currently under tests at the Canada-France-Hawaii Telescope (CFHT, Mauna Kea, Hawaii).
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.007 |
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".