Orateur
Description
We compare the performances of a PhotoZ estimator Delight, a hybrid method of template fitting (based here on SED CWW templates) and machine learning.
In a first step, we compare the performance of Delight's z-phot vs z-spec regression on DC2 simulations with that of LePhare's "state of the art" template fitting method and a machine learning method, the ramdom forest trees.
In a second step, we try to improve the performance of Delight in two ways:
- Implementation of the photo detection bias effect called Edignton-Malsmquist bias in the templates after adjusting the threshold of photo detection in each of the filters on the DC2 data,
- A pre-classification of the DC2 simulations into the CWW template SED categories, followed by a learning of the Flux redshift relationship for each category.
