.. _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