Evolutionary models of cold and low-mass planets: Cooling curves,\n magnitudes, and detectability
Bibliographic record
Abstract
Future instruments like NIRCam and MIRI on JWST or METIS at the ELT will be\nable to image exoplanets that are too faint for current direct imaging\ninstruments. Evolutionary models predicting the planetary intrinsic luminosity\nas a function of time have traditionally concentrated on gas-dominated giant\nplanets. We extend these cooling curves to Saturnian and Neptunian planets. We\nsimulate the cooling of isolated core-dominated and gas giant planets with\nmasses of 5 Earthmasses to 2 Jupitermasses. The luminosity includes the\ncontribution from the cooling and contraction of the core and of the H/He\nenvelope, as well as radiogenic decay. For the atmosphere we use grey,\nAMES-Cond, petitCODE, and HELIOS models. We consider solar and non-solar\nmetallicities as well as cloud-free and cloudy atmospheres. The most important\ninitial conditions, namely the core-to-envelope ratio and the initial\nluminosity are taken from planet formation simulations based on the core\naccretion paradigm. We first compare our cooling curves for Uranus, Neptune,\nJupiter, Saturn, GJ 436b, and a 5 Earthmass-planet with a 1% H/He envelope with\nother evolutionary models. We then present the temporal evolution of planets\nwith masses between 5 Earthmasses and 2 Jupitermasses in terms of their\nluminosity, effective temperature, radius, and entropy. We discuss the impact\nof different post formation entropies. For the different atmosphere types and\ninitial conditions magnitudes in various filter bands between 0.9 and 30\nmicrometer wavelength are provided. Using black body fluxes and non-grey\nspectra, we estimate the detectability of such planets with JWST. It is found\nthat a 20 (100) Earthmass-planet can be detected with JWST in the background\nlimit up to an age of about 10 (100) Myr with NIRCam and MIRI, respectively.\n
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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 | 0.003 |
| 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 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".