Changes between Version 21 and Version 22 of Reweight

Timestamp:

Feb 1, 2016, 4:02:59 PM (9 years ago)

Author:

Olivier Mattelaer

Comment:

--

Reweight

@@ -32 +32 @@
     $$d\sigma^{H} = d\sigma^E - d\sigma^{MC} $$
     $$ d\sigma^{S} = d\sigma^{MC} + \sum_{\alpha=S,C,SC} d\sigma^\alpha $$
-    Each of the $d\sigma^\alpha$ can be written as 
+ Each of the $d\sigma^\alpha$ can be written as 
     $$ d\sigma^\alpha=f_1(x_1,\mu_F)f_2(x_2,\mu_F) \left[\mathcal{W}^\alpha_0 + \mathcal{W}^\alpha_F log\left(\mu_F/Q\right)^2 + \mathcal{W}^\alpha_R log\left(\mu_R/Q\right)^2 \right] d\chi$$
  
@@ -43 +43 @@
  the final weight is then computed by recombining the weight according to the above formula.
 
- However in MadGraph5_aMC@NLO, we use the virt-tricks method which avoid the computation of the virtual for some of the phase-space points. This speed optimisation method forbids the simple above reweighting since the generation will have $\mathcal{W}_V^{old}=0$ even if $V_{old} \neq 0$. To avoid this problem, $\mathcal{W}_B$ is splitted in two $\mathcal{W}_{BC}$, $\mathcal{W}_{BB}$ for the part proportional to the Born due to the counter-term and from the part really comming from the born or from the approximate virtual.
+ However in MadGraph5_aMC@NLO, we use the virt-tricks method which avoid the computation of the virtual for some of the phase-space points. This speed optimisation method forbids the simple above reweighting since the generation will have $\mathcal{W}_V^{old}=0$ even if $V_{old} \neq 0$. To avoid this problem, $\mathcal{W}_B$ is splitted in two piece :$\mathcal{W}_{BC}$, $\mathcal{W}_{BB}$. $\mathcal{W}_{BC}$ is the part proportional to the born and related to the one counter-term, while $\mathcal{W}_{BB}$ is for the other contribution (the born itself and the approximate virtual).
  The reweighting is then done as 
    $$\mathcal{W}_{BB}^{new} = \frac{(B^{new}+V^{new})}{(B^{old}+V^{old})} * \mathcal{W}_{BB}^{old} $$
@@ -50 +50 @@
    $$\mathcal{W}_R^{new} = \frac{R^{new}}{R^{old}} * \mathcal{W}_R^{old} $$ 
  Such reweighting is fully NLO accurate. As in the LO case, the statistical uncertainty can be enhanced by the reweighting. Additionally the trick to support the virt-tricks adds an additional contribution to statistical uncertainty.  
- This method will be released in a future version of MadGraph5_aMC@NLO and can currently be provided on request. Since this reweighting is based on a dedicated basis the NLO sample must be generated in a specific way to have the additional information in the leshouches event.
+ '''This method will be released in a future version of MadGraph5_aMC@NLO''' and can currently be provided on request. Since this reweighting is based on a dedicated basis the NLO sample must be generated in a specific way to have the additional information in the leshouches event.