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| author | tlatorre <tlatorre@uchicago.edu> | 2020-10-05 11:15:33 -0500 |
|---|---|---|
| committer | tlatorre <tlatorre@uchicago.edu> | 2020-10-05 11:15:33 -0500 |
| commit | 1cd79549ee8ceb0f5e64e06611d891098905c05a (patch) | |
| tree | 326a84888fd7ae44846c206846e5be24982bd092 /utils/chi2 | |
| parent | 1cc5fc8014ab75ac55672aa962228ac54ae8d9db (diff) | |
| download | sddm-1cd79549ee8ceb0f5e64e06611d891098905c05a.tar.gz sddm-1cd79549ee8ceb0f5e64e06611d891098905c05a.tar.bz2 sddm-1cd79549ee8ceb0f5e64e06611d891098905c05a.zip | |
update how the energy bias is applied in chi2 and plot-michels
This commit updates how the energy bias is applied when we correct for
the energy bias in correct_energy_bias(). The correct way to apply this
correction is to compute:
T_corrected = T_reco/(1+bias)
whereas previously we were multiplying by (1-bias).
Diffstat (limited to 'utils/chi2')
| -rwxr-xr-x | utils/chi2 | 24 |
1 files changed, 13 insertions, 11 deletions
@@ -489,17 +489,19 @@ def correct_energy_bias(df): Corrects for the energy bias of the reconstruction relative to the true Monte Carlo energy. """ - # Note: We subtract here since the values in the energy_bias array are MC - # reconstruction relative to truth. So, for example if we have an energy - # bias of -2% at 100 MeV, then the array will contain -0.02. In this case, - # our reconstruction is low relative to the truth, so we need to *increase* - # our estimate. - df.loc[df['id1'] == 20,'energy1'] -= df.loc[df['id1'] == 20,'energy1']*np.interp(df.loc[df['id1'] == 20,'energy1'],ENERGY_BIAS['T'],ENERGY_BIAS['e_bias']) - df.loc[df['id2'] == 20,'energy2'] -= df.loc[df['id2'] == 20,'energy2']*np.interp(df.loc[df['id2'] == 20,'energy1'],ENERGY_BIAS['T'],ENERGY_BIAS['e_bias']) - df.loc[df['id3'] == 20,'energy3'] -= df.loc[df['id3'] == 20,'energy3']*np.interp(df.loc[df['id3'] == 20,'energy1'],ENERGY_BIAS['T'],ENERGY_BIAS['e_bias']) - df.loc[df['id1'] == 22,'energy1'] -= df.loc[df['id1'] == 22,'energy1']*np.interp(df.loc[df['id1'] == 22,'energy1'],ENERGY_BIAS['T'],ENERGY_BIAS['u_bias']) - df.loc[df['id2'] == 22,'energy2'] -= df.loc[df['id2'] == 22,'energy2']*np.interp(df.loc[df['id2'] == 22,'energy1'],ENERGY_BIAS['T'],ENERGY_BIAS['u_bias']) - df.loc[df['id3'] == 22,'energy3'] -= df.loc[df['id3'] == 22,'energy3']*np.interp(df.loc[df['id3'] == 22,'energy1'],ENERGY_BIAS['T'],ENERGY_BIAS['u_bias']) + # Note: We divide here since we define the bias as: + # + # bias = (T_reco - T_true)/T_true + # + # Therefore, + # + # T_true = T_reco/(1+bias) + df.loc[df['id1'] == 20,'energy1'] /= (1+np.interp(df.loc[df['id1'] == 20,'energy1'],ENERGY_BIAS['T'],ENERGY_BIAS['e_bias'])) + df.loc[df['id2'] == 20,'energy2'] /= (1+np.interp(df.loc[df['id2'] == 20,'energy1'],ENERGY_BIAS['T'],ENERGY_BIAS['e_bias'])) + df.loc[df['id3'] == 20,'energy3'] /= (1+np.interp(df.loc[df['id3'] == 20,'energy1'],ENERGY_BIAS['T'],ENERGY_BIAS['e_bias'])) + df.loc[df['id1'] == 22,'energy1'] /= (1+np.interp(df.loc[df['id1'] == 22,'energy1'],ENERGY_BIAS['T'],ENERGY_BIAS['u_bias'])) + df.loc[df['id2'] == 22,'energy2'] /= (1+np.interp(df.loc[df['id2'] == 22,'energy1'],ENERGY_BIAS['T'],ENERGY_BIAS['u_bias'])) + df.loc[df['id3'] == 22,'energy3'] /= (1+np.interp(df.loc[df['id3'] == 22,'energy1'],ENERGY_BIAS['T'],ENERGY_BIAS['u_bias'])) df['ke'] = df['energy1'].fillna(0) + df['energy2'].fillna(0) + df['energy3'].fillna(0) return df |