diff options
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 |