.. _SHELensMcTraining:
  
Shear LensMC Training
---------------------

$SetSchemaPath she/euc-she-LensMcTraining.xsd 
  
Data product name
=================
.. DataProductNameStart
   
$PrintDataProductName
   
.. DataProductNameEnd
   
Data product custodian
======================
.. DataProductCustodianStart
   
$PrintDataProductCustodian
   
.. DataProductCustodianEnd
      
Name of the Schema file
=======================
.. NameSchemaStart
   
$PrintSchemaFilename
   
.. NameSchemaEnd
   
Last Edited for DPDD Version
____________________________
.. DpddVersionTagStart

1.1

.. DpddVersionTagEnd
   
Data product elements
=====================
.. DataProductElementsStart

$PrintDataProductElements
   
.. DataProductElementsEnd
   
Processing Element(s) creating/using the data product
=====================================================
.. PECreatorStart

SHE
   
.. PECreatorEnd
   
Processing function using the data product
==========================================
.. PFUsingStart

SHE
   
.. PFUsingEnd
   
Detailed description of the data product
========================================
.. DetailedDescStart

An XML data product containing training data for the LensMC shear measurement algorithm. This algorithm requires prior knowledge
on the unlensed distribution of galaxy shapes in order to properly estimate weights that will be associated to the shear catalogue.
Additionally, further prior distributions on other parameters (e.g. galaxy size) could be derived as well.
There is no particular constraint on what type of dataset should be used - most likely LensMC will get these priors directly 
from data - Euclid deep fields or HST images.
It is evident that the same dataset could be used for training (estimation of weights and priors) and calibration (bias subtraction).
The algorithm used for training (or calibration) should be the same of that applied to the wide survey.
The resulting training catalogue will be stored in a fits table in exactly the same format as per the shear measurement catalogue
and LensMC chain table.
 
.. DetailedDescEnd