From 1cd79549ee8ceb0f5e64e06611d891098905c05a Mon Sep 17 00:00:00 2001 From: tlatorre Date: Mon, 5 Oct 2020 11:15:33 -0500 Subject: 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). --- utils/plot-michels | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) (limited to 'utils/plot-michels') diff --git a/utils/plot-michels b/utils/plot-michels index bec9e17..b313c3e 100755 --- a/utils/plot-michels +++ b/utils/plot-michels @@ -65,16 +65,16 @@ def print_particle_probs(data): print("P(%s) = %.2f +/- %.2f" % (particle_id_str,mode[i]*100,std[i]*100)) def make_nll(data, mc, bins, print_nll=False): + data_hist = np.histogram(data.ke.values,bins=bins)[0] + def nll(x, grad=None): if any(x[i] < 0 for i in (0,2)): return np.inf - data_hist = np.histogram(data.ke.values*(1+x[1]),bins=bins)[0] - # Get the Monte Carlo histograms. We need to do this within the # likelihood function since we apply the energy resolution parameters # to the Monte Carlo. - cdf = norm.cdf(bins[:,np.newaxis],mc.ke.values,mc.ke.values*x[2]) + cdf = norm.cdf(bins[:,np.newaxis],mc.ke.values*(1+x[1]),mc.ke.values*x[2]) mc_hist = np.sum(cdf[1:] - cdf[:-1],axis=-1)*x[0] # Calculate the negative log of the likelihood of observing the data -- cgit