Six lessons · White dwarf cooling and crystallization
The heat and clocks of white dwarfs
A white dwarf keeps shining after its main nuclear furnace fades. Follow its stored heat into light, watch a shell freeze, then ask what brightness can tell us about time. The route leads from a physical picture to calculations, original data and the papers on floating crystals.
A physical picture, then a calculation
1 · L1
Why white dwarfs cool so slowly
Stored heat keeps a white dwarf shining. Follow the cooling, the release of latent heat and the extra energy that can delay its stellar clock.
2 · L1
Where a dead star keeps its heat
Separate degenerate support from thermal energy. Count ions, derive a first cooling time and distinguish core temperature from the photosphere.
3 · L2
How crystallization slows white dwarf cooling
Derive the latent plateau, Debye limits and a controlled Mestel envelope. Track every heat term through a two-shell freezing example.
4 · L3
Inferring a cooling age from brightness
Build a conditional age likelihood with an explicit time prior. Test uneven sampling, model assumptions and a failed sparse quadrature.
5 · L3
Compare white dwarf cooling models with original Gaia data
Fetch named DR3 fields locally, verify fixed MIST inputs and recompute conditional age comparisons, native holdouts and published cooling delays.
6 · L4
Why crystals can float inside a white dwarf
Read fixed Bauer and Bédard papers from phase separation to neon distillation, including the composition prescription, smoothing and open questions.
Calculate from the original model inputs
The small archive contains fixed licensed MESA structures, MIST and STELUM outputs, standalone Python, units and original-input hashes. Its README separates seven model-only commands from the reader-local Gaia branch. Neither the website Engine nor installed scientific tools are included.
- white-dwarf-cooling-science.zip0.908 MB
- include-manifest.json0.008 MB
white-dwarf-cooling-science.zip
907651 bytes
SHA-256: 80a79ecda6908ce228f04a0c65b06efc918b8f75e23f21cb0b2a479b71032a8a
include-manifest.json
8262 bytes
SHA-256: fb07cf905b8862cfa73eacf46ecff53a715f77658d993b7333fadb4ee489a3e5
Dependencies, observations and reuse
Reference environment: Python 3.12.12, NumPy 2.4.4, SciPy 1.17.1 and Matplotlib 3.10.8. Install these separately before following the offline commands; no MESA or STELUM installation is required. New standalone Python is MIT, and our figures and documentation are CC BY 4.0. Original model attribution and source licences accompany the files.
Gaia source rows, per-object results and Gaia point figures are excluded. The archive supplies seven named queries and an explicit reader-run fetcher. Obtain observations locally under the current Gaia licence and ESDC terms; the public lessons use synthetic or licensed native-model figures. The cold replay used an existing private cache and made no fresh Gaia request.