Changes between Version 43 and Version 44 of Reweight

Timestamp:

Feb 22, 2016, 11:03:41 PM (8 years ago)

Author:

Olivier Mattelaer

Comment:

--

Reweight

@@ -43 +43 @@
    $$\mathcal{W}_V^{new} = \frac{V^{new}}{V^{old}} * \mathcal{W}_V^{old} $$
    $$\mathcal{W}_R^{new} = \frac{R^{new}}{R^{old}} * \mathcal{W}_R^{old} $$
- with the final weight computed by recombining these weights according to the prescription given before.
-
- The actual implementation in MadGraph5_aMC@NLO is slightly more complicated, owing to the procedure adopted in the computation of the virtual contribution (see sect.2.4.3 http://arxiv.org/pdf/1405.0301.pdf). This speed optimisation method prevents one from performing the reweighting as written above, since there is the possibility that $\mathcal{W}_V^{old}=0$ even if $V_{old} \neq 0$. To avoid this problem, $\mathcal{W}_B$ is split in two pieces :$\mathcal{W}_{BC}$, $\mathcal{W}_{BB}$. $\mathcal{W}_{BC}$ is the part, proportional to the Born, related to the one of the counterterms, while $\mathcal{W}_{BB}$ includes all of the other contributions (the Born itself and the approximate virtual).
+ with the final weight computed by recombining these weights according to the prescription given before. This method of reweighting is called "NLO_basic".
+
+
+ In MadGraph5_aMC@NLO, we have implemented a second NLO accurate method of re-weighting. This method dubbed "NLO_VT" is expected to have a smaller statistical uncertainty than the basic one. One potential problem of the "NLO_basic" method is related to the procedure adopted in the computation of the virtual contribution (see sect.2.4.3 http://arxiv.org/pdf/1405.0301.pdf). This speed optimisation method can easily statistical error associated to a sub-sample of events. To estimate such effect we propose this second reweighting  method --which should be less sensitive to such effects--. The difference between those two methods should be seen as a systematics. 
+For this re-weighting, $\mathcal{W}_B$ is split in two pieces :$\mathcal{W}_{BC}$, $\mathcal{W}_{BB}$. $\mathcal{W}_{BC}$ is the part, proportional to the Born, related to the one of the counterterms, while $\mathcal{W}_{BB}$ includes all of the other contributions (the Born itself and the approximate virtual).
  The reweighting is then carried out as follows: 
    $$\mathcal{W}_{BB}^{new} = \frac{(B^{new}+V^{new})}{(B^{old}+V^{old})} * \mathcal{W}_{BB}^{old} $$
@@ -51 +53 @@
    $$\mathcal{W}_V^{new} = \frac{(B^{new}+V^{new})}{(B^{old}+V^{old})} * \mathcal{W}_V^{old} $$
    $$\mathcal{W}_R^{new} = \frac{R^{new}}{R^{old}} * \mathcal{W}_R^{old} $$ 
- Such reweighting is fully NLO accurate. As in the LO case, the statistical uncertainty can be enhanced by reweighting. 
+ Such reweighting is fully NLO accurate as well. 
   
  '''This method will be released in a future version of MadGraph5_aMC@NLO''' and can currently be provided on request. Since it is based on a dedicated decomposition, the NLO sample must be generated in a specific way for the Les Houches event file to contain the necessary information (see below).