An untested update of the GNN flavor tagger payload to a new format to work with tensorflow 2.16 and keras 3. The underlying model has not been changed

ECLChargedPIDMVA payload containing the trained BDTs with HadronFrac and the version of v8 of the CNN (with HadronFrac) that has been retrained with release 9 (new keras interface) 2024_07_02_rel-09_w_PSD. Replaced the HadronFrac_good custom variable with a conditionalVariable that uses the same cuts.

Edited copies of the beamBackgroundMVA and FakePhotonMVA payloads that use E instead of clusterE. This is so that the energy bias correction applied on data is propagated to the MVAs.

Nominal XGB payload optimised for 20mb, includes crystal energy at low p

Nominal XGB payload optimised for 20mb, includes crystal energy at low p, uses the CNN in the barrel below 1GeV

Nominal XGB payload optimised for 20mb. Uses v2 of the CNN in the barrel for all p ranges

Nominal XGB payload optimised for 20mb. Uses v4 of the CNN in the barrel for p < 1 GeV. With dropout layers for CNN for p < 0.6. BDT and CNN trained with EP3 backgrounds.

payload for ECLChargedPIDMVA using the CNN files v9 withhout PSD info and a matching BDT

Providing a central training for the mva selector in the analysis modCurlTagger module

tag to add missing software trigger skim payloads to exp7-12. Intended for MC15rd and proc13 but can likely be used for later processings too. Based on the hlt_filters_exp24 GT.

tag to test uploading mva

Testing payload for new keras saving format. The mvas themselves are the same as the previous iteration (at least at the time of creation).

testing payload for patching the HLT skims