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""" | ||
The pattern likelihood cut defined in SR0 was defined with a data-driven S2 map and a Gaussian | ||
smeared likelihood definition, using the Tensorflow framework. The data-driven S2 map is trained | ||
using a neural network, using S2 optical Monte Carlo simulation as an initial guess, to best-fit | ||
event_area_per_channel. Details can be found in the SR0 note. As the SR0 method relied on trainable | ||
parameters for each PMT the S2 pattern likelihood needs to be retrained to reflect the changed PMT list in SR1. | ||
SR0 S2 pattern map note | ||
https://xe1t-wiki.lngs.infn.it/doku.php?id=shenyang:s2_pattern_likelihood (Shenyang) | ||
SR2 S2 pattern map note | ||
https://xe1t-wiki.lngs.infn.it/doku.php?id=xenon:xenon1t:jacques:nt_sr1_s2pl (Jacques) | ||
""" | ||
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from .base_references import BaseMap | ||
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class S2XYMap(BaseMap): | ||
_ALIAS = "s2_pattern_maps" |