Realistic synthetic integral field spectroscopy with RealSim-IFS
Bibliographic record
Abstract
ABSTRACT The most direct way to confront observed galaxies with those formed in numerical simulations is to forward-model simulated galaxies into synthetic observations. Provided that synthetic galaxy observations include similar constraints and limitations as real observations, they can be used to (1) carry out even-handed comparisons of observation and theory and (2) map the observable characteristics of simulated galaxies to their a priori known origins. In particular, integral field spectroscopy (IFS) expands the scope of such comparisons and mappings to an exceptionally broad set of physical properties. We therefore present RealSim-IFS, a tool for forward-modelling galaxies from hydrodynamical simulations into synthetic IFS observations. The core components of RealSim-IFS model the detailed spatial sampling mechanics of any fibre-bundle, image slicer, or lenslet array IFU and corresponding observing strategy, real or imagined, and support the corresponding propagation of noise adopted by the user. The code is highly generalized and can produce cubes in any light- or mass-weighted quantity (e.g. specific intensity, gas/stellar line-of-sight velocity, stellar age/metallicity, etc.). We show that RealSim-IFS exactly reproduces the spatial reconstruction of specific intensity and variance cubes produced by the MaNGA survey Data Reduction Pipeline using the calibrated fibre spectra as input. We then apply RealSim-IFS by producing a public synthetic MaNGA stellar kinematic survey of 893 galaxies with log (M⋆/M⊙) > 10 from the TNG50 cosmological hydrodynamical simulation.
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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".