Changes between Version 7 and Version 8 of LOEventGenerationBias

Timestamp:

Aug 23, 2016, 2:53:19 AM (8 years ago)

Author:

Valentin Hirschi

Comment:

--

LOEventGenerationBias

@@ -7 +7 @@
 
 The motivation for using a bias function typically falls in one of the following two categories:
- * a) Producing smoother distributions for the tail of a particular observable. This means that physical results obtained in presence of the bias will be identical but sampled differently.
- * b) One wants to modify the integrand so as to really impact the physical results. This can be useful for a plethora of applications: ad-hoc unitarisation of the matrix elements, merging weights, inclusion of higher order contributions, implementation of customized cuts, on-the-flight plotting, etc.. 
+ * a) Producing smoother distributions for the tail of a particular observable. This means that physical results obtained in presence of the bias will be identical but sampled differently. One can also use this mode if something specific must be done with each event, independently of the integration (for instance: on-the-flight plotting).
+ * b) One wants to modify the integrand so as to really impact the physical results. This can be useful for a plethora of applications: ad-hoc unitarisation of the matrix elements, merging weights, inclusion of higher order contributions, implementation of customized cuts, etc.. 
 
-Before going into the detail of the usage of the bias module and the instructions for building your customized bias, we present here an example of their use for the case a) above to smoothen the distribution of the jet transverse momentum for the process 'p p > e+ e- j'.
+Before going into the detail of the usage of the bias module and the instructions for building your customized bias, we present here an example of its use for the case a) above to smoothen the distribution of the jet transverse momentum for the process 'p p > e+ e- j'.
 
 These two plots were both obtained from 10k events, but for the right-hand side one, the matrix elements have been biased by the quantity $(\;{p_t(j_1)}/{1000.0}\;)^4$.
@@ -37 +37 @@
 {{{ {'ptj_bias_target_ptj': 300.0, 'ptj_bias_enhancement_power': 4.0} = bias_parameters }}}
 
-Alternatively, the commands '{{{set bias_parameter <param_dictionary>}}}' and '{{{add bias_parameter <param_name> <value>}}}' can be used when MG5aMC offers you to edit the cards.
+Alternatively, the commands '{{{set bias_parameter <param_dictionary>}}}' and '{{{set bias_parameter <param_name> <value>}}}' can be used when MG5aMC offers you to edit the cards.
 
 Finally, notice that when you choose to use a bias module that does *not* impact the physical results, the unweighted events produced will be re-weighted so as to undo the effect of the bias.