KilonovAE: Exploring Kilonova Spectral Features with Autoencoders
Bibliographic record
Abstract
Abstract Kilonovae are likely a key site of heavy r -process element production in the Universe, and their optical/infrared spectra contain insights into both the properties of the ejecta and the conditions of the r -process. However, the event GW170817/AT2017gfo is the only kilonova so far with well-observed spectra. To understand the diversity of absorption features that might be observed in future kilonovae spectra, we use the TARDIS Monte Carlo radiative transfer code to simulate a suite of optical spectra spanning a wide range of kilonova ejecta properties and r -process abundance patterns. To identify the most common and prominent absorption lines, we perform dimensionality reduction using an autoencoder, and we find spectra clusters in the latent space representation using a Bayesian Gaussian Mixture model. Our synthetic kilonovae spectra commonly display strong absorption by strontium 38 Sr ii , yttrium 38 Y ii , and zirconium 40 Zr i–ii , with strong lanthanide contributions at low electron fractions ( Y e ≲ 0.25). When a new kilonova is observed, our machine-learning framework will provide context on the dominant absorption lines and key ejecta properties, helping to determine where this event falls within the larger “zoo” of kilonovae spectra.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".