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alt_s1 pattern likelihood #1344

@shenyangshi

Description

@shenyangshi

Neither is compulsory and maybe not useful. Next time if we plan to do a event_pattern_fit reprocessing we can add the pattern fit for alt_s1 as well, especially for some pile-up coincidence quality selection.

def infer_dtype(self):
dtype = [
("s2_2llh", np.float32, "Modified Poisson likelihood value for main S2 in the event"),
(
"s2_neural_2llh",
np.float32,
"Data-driven based likelihood value for main S2 in the event",
),
("alt_s2_2llh", np.float32, "Modified Poisson likelihood value for alternative S2"),
(
"alt_s2_neural_2llh",
np.float32,
"Data-driven based likelihood value for alternative S2 in the event",
),
("s1_2llh", np.float32, "Modified Poisson likelihood value for main S1"),
(
"s1_top_2llh",
np.float32,
"Modified Poisson likelihood value for main S1, calculated from top array",
),
(
"s1_bottom_2llh",
np.float32,
"Modified Poisson likelihood value for main S1, calculated from bottom array",
),
(
"s1_area_fraction_top_continuous_probability",
np.float32,
"Continuous binomial test for S1 area fraction top",
),
(
"s1_area_fraction_top_discrete_probability",
np.float32,
"Discrete binomial test for S1 area fraction top",
),
(
"s1_photon_fraction_top_continuous_probability",
np.float32,
"Continuous binomial test for S1 photon fraction top",
),
(
"s1_photon_fraction_top_discrete_probability",
np.float32,
"Discrete binomial test for S1 photon fraction top",
),
(
"alt_s1_area_fraction_top_continuous_probability",
np.float32,
"Continuous binomial test for alternative S1 area fraction top",
),
(
"alt_s1_area_fraction_top_discrete_probability",
np.float32,
"Discrete binomial test for alternative S1 area fraction top",
),
(
"alt_s1_photon_fraction_top_continuous_probability",
np.float32,
"Continuous binomial test for alternative S1 photon fraction top",
),
(
"alt_s1_photon_fraction_top_discrete_probability",
np.float32,
"Discrete binomial test for alternative S1 photon fraction top",
),
]

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