View Bootstrap with weighting.ipynb
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View covariance_to_correlation.py
 import numpy as np def correlation_from_covariance(covariance): v = np.sqrt(np.diag(covariance)) outer_v = np.outer(v, v) correlation = covariance / outer_v correlation[covariance == 0] = 0 return correlation
View RoundingError.ipynb
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View loop_rooargset.py
 def loop_iterator(iterator): object = iterator.Next() while object: yield object object = iterator.Next() def iter_collection(rooAbsCollection): iterator = rooAbsCollection.createIterator() return loop_iterator(iterator)
View newton optimization.py
 import numpy as np import tensorflow as tf # Newton's optimization method for multivariate function in tensorflow def cons(x): return tf.constant(x, dtype=tf.float32) def compute_hessian(fn, vars): mat = []
View 4Stefano.ipynb
 # Ricerca di fisica esotica a LHC con risonanze a due corpi ## E/gamma energy calibration * multivariate regression optimized on MC to calibrate the energy of electron / converted / unconverted photons * intercalibration of calorimeter layers from 2012 + additional uncertainty * energy scale and resolution corrections validated with 13 TeV $Z\to ee$ * For $E>100-200$ GeV resolution dominated by the constant term $c=0.6%-1.5%$ * Scale uncertainty (0.4%-2%) for diphoton analysis * Preliminary photon energy resolution at 300 GeV: $\pm 80\%-100\%$