# METAPHOR: Probability density estimation for machine learning based photometric redshifts

## TL;DR

METAPHOR is a flexible machine learning workflow that estimates accurate probability density functions for galaxy photometric redshifts, validated on SDSS data and adaptable with various models.

## Contribution

Introduces METAPHOR, a modular and adaptable workflow for deriving photometric redshift PDFs using machine learning techniques.

## Key findings

- METAPHOR accurately estimates photometric redshift PDFs.
- The workflow is validated on SDSS-DR9 galaxy data.
- It performs well with different models like KNN and Random Forest.

## Abstract

We present METAPHOR (Machine-learning Estimation Tool for Accurate PHOtometric Redshifts), a method able to provide a reliable PDF for photometric galaxy redshifts estimated through empirical techniques. METAPHOR is a modular workflow, mainly based on the MLPQNA neural network as internal engine to derive photometric galaxy redshifts, but giving the possibility to easily replace MLPQNA with any other method to predict photo-z's and their PDF. We present here the results about a validation test of the workflow on the galaxies from SDSS-DR9, showing also the universality of the method by replacing MLPQNA with KNN and Random Forest models. The validation test include also a comparison with the PDF's derived from a traditional SED template fitting method (Le Phare).