Marginalise n(z) uncertainty with modes of an n(z) ensemble (as DES Y6), not shift/stretch

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#469 0 comentarios 0 reacciones 0 asignados Ver en GitHub

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Evaluación

Dificultad
5/5
Tiempo estimado
Más de una semana
Aptitud para principiantes
35/100
Tipo de issue
Nueva funcionalidad
Claridad
Bastante claro
Estado de actividad
Activo
Stack tecnológico
python
Área
data, machine-learning

Línea de trabajo

Read the photoz_bias templates in cosmo_inference/cosmosis_config/templates/ and the related issues #455, #468, #274, #220, and #460 to trace how n(z) is produced and passed into CosmoSIS. Start by defining an ensemble for a tomographic n(z), then identify where to reduce it to modes and provide the mode amplitudes and priors to the likelihood. Done means mock recovery is demonstrated and the Δz-only model's bias is quantified.

Escrito por el modelo de indexación a partir del texto del issue.

Descripción

Claude (Opus) on behalf of Cail.

Our inference models n(z) uncertainty with a per-bin mean shift Δz: CosmoSIS photoz_bias in cosmo_inference/cosmosis_config/templates/. A shift plus stretch is the usual extension. Both leave the n(z) shape fixed. With a SOM n(z) built from a few spectroscopic fields, the shape is where much of the uncertainty is: tails, spikes from structure in the spectroscopic fields, and correlated changes across bins.

DES Y6 instead takes an ensemble of n(z) realisations, compresses it into a few modes, and samples their amplitudes in the likelihood. The modes capture shape changes and cross-bin correlations that a shift cannot. We should do the same.

Done when:

  • An n(z) ensemble exists for a tomographic n(z). For the working SOM n(z) (#468), this can be bootstrap realisations over calibrators and fields, emitted by the calibration.
  • A step reduces the ensemble to its leading modes plus the mean, and writes them where the inference reads the n(z) (SACC or the CosmoSIS nz input).
  • CosmoSIS samples the mode amplitudes with their priors in place of photoz_bias. On mocks, the posterior recovers the input when the true n(z) is drawn from the ensemble, and the Δz-only model's bias is quantified against it.

Related: #455 and #468 (where the n(z) is made), #274 (the photo-z group's SOM n(z), whose own ensemble should feed this), #220 and #460 (CosmoSIS with tomography).

Lenguaje dominante
Python
Estrellas
2
Forks
5
Merge medio
22 h 26 min
PR fusionados (30 d)
30

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