|
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", |
|
), |
|
] |
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.
straxen/straxen/plugins/events/event_pattern_fit.py
Lines 109 to 174 in 0893040