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

Aperta
  • Qualcosa non va?
#469 0 commenti 0 reazioni 0 assegnatari Vedi su GitHub

I maintainer di solito rispondono entro 1 giorno

Nessuno ha ancora preso questa issue.

Valutazione

Difficoltà
5/5
Tempo stimato
Più di una settimana
Idoneità per principianti
35/100
Tipo di issue
Funzionalità
Chiarezza
Abbastanza chiara
Stato di attività
Attiva
Stack tecnologico
python
Ambito
data, machine-learning

Direzione di ricerca

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.

Scritto dal modello di indicizzazione a partire dal testo della issue.

Descrizione

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).

Lingua principale
Python
Stelle
2
Fork
5
Merge medio
22h 26m
PR unite (30g)
30

Preparare l'ambiente

Come iniziare

  1. Leggi tutta la issue e poi la guida ai contributi del progetto.
  2. Commenta sulla issue per dire che te ne occupi tu — evita che due persone facciano lo stesso lavoro.
  3. Fai un fork del repository e lavora su un branch.
  4. Apri una pull request che faccia riferimento al numero della issue.

Altre issue di CosmoStat/sp_validation

Tutte le issue di CosmoStat/sp_validation

Issue simili

Altre issue su Python

Ricevi le nuove issue nella tua casella

Un breve riepilogo di issue GitHub adatte ai principianti.