Hi Zach, For CKKWL, I’m not sure that there is a problem at all actually (at least not in MG). I have do the following: 1) mv models/MSSM_SLHA2 models/mssm 2) run the following script (with different version): import model mssm generate p p > go go add process p p > go go j output -f launch set ickkw 0 set xqcut 0 set mgo 1000 If i run with version 2.6.1 (not released so after the patch) , I have === Results Summary for run: run_01 tag: tag_1 === Cross-section : 0.4667 +- 0.001416 pb Nb of events : 10000 INFO: Running Systematics computation INFO: Idle: 0, Running: 4, Completed: 0 [ current time: 21h31 ] INFO: Idle: 0, Running: 3, Completed: 1 [ 1m 42s ] INFO: # events generated with PDF: NNPDF23_lo_as_0130_qed (247000) INFO: #Will Compute 235 weights per event. INFO: #*************************************************************************** # # original cross-section: 0.46658878736 # scale variation: +51.1% -31.7% # emission scale variation: + 0% -31.7% # central scheme variation: + 0% -37.1% # PDF variation: +18.5% -18.5% # # dynamical scheme # 1 : 0.395043 +49.4% -30.9% # \sum ET # dynamical scheme # 2 : 0.313735 +46.1% -29.6% # \sum\sqrt{m^2+pt^2} # dynamical scheme # 3 : 0.458261 +50.8% -31.5% # 0.5 \sum\sqrt{m^2+pt^2} # dynamical scheme # 4 : 0.293286 +45.1% -29.2% # \sqrt{\hat s} #*************************************************************************** If i run with version 2.3.3, I have: === Results Summary for run: run_01 tag: tag_1 === Cross-section : 0.4671 +- 0.001455 pb Nb of events : 10000 If i run with version 2.4.1, I have: === Results Summary for run: run_01 tag: tag_1 === Cross-section : 0.4667 +- 0.001416 pb Nb of events : 10000 If i run with version 2.6.0: === Results Summary for run: run_01 tag: tag_1 === Cross-section : 0.4667 +- 0.001416 pb Nb of events : 10000 INFO: #*************************************************************************** # # original cross-section: 0.46658878736 # scale variation: +51.2% -31.7% # emission scale variation: + 0% -31.7% # central scheme variation: + 0% -37.2% # PDF variation: +18.5% -18.5% # # dynamical scheme # 1 : 0.394494 +49.5% -31% # \sum ET # dynamical scheme # 2 : 0.3136 +46.1% -29.6% # \sum\sqrt{m^2+pt^2} # dynamical scheme # 3 : 0.4583 +50.9% -31.6% # 0.5 \sum\sqrt{m^2+pt^2} # dynamical scheme # 4 : 0.293178 +45.2% -29.2% # \sqrt{\hat s} #*************************************************************************** So here I do not see anything different. As you can see (and as I was expecting my patch has not effect at all in the case of ickkw=0) Since my patch was related to the pdf reweighting of the MLM matching (ickkw=1). I also check the result for each type of final state: here it is for 2.6.0: Graph Cross-Section ↓ Error Events (K) Unwgt Luminosity P2 sum 0.29763812117 P2_gg_gogog 0.2134 0.00117 116.599 8689.0 0 P1 sum 0.169090624 P1_gg_gogo 0.1469 0.000425 89.093 5333.0 0 P2_gq_gogoq 0.06981 0.000667 315.828 24045.0 0 P1_qq_gogo 0.02222 0.000109 63.122 3782.0 0 P2_qq_gogog 0.01438 9.11e-05 308.632 23087.0 0 Here it is for 2.6.1: Graph Cross-Section ↓ Error Events (K) Unwgt Luminosity P2 sum 0.29763812117 P2_gg_gogog 0.2134 0.00117 116.599 8689.0 0 P1 sum 0.169090624 P1_gg_gogo 0.1469 0.000425 89.093 5333.0 0 P2_gq_gogoq 0.06981 0.000667 315.828 24045.0 0 P1_qq_gogo 0.02222 0.000109 63.122 3782.0 0 P2_qq_gogog 0.01438 9.11e-05 308.632 23087.0 0 Here it is for 2.3.3 Graph Cross-Section ↓ Error Events (K) Unwgt Luminosity P2 sum 0.29766184171 P2_gg_gogog 0.2135 0.0012 116.803 8472.0 0 P1 sum 0.169410557 P1_gg_gogo 0.1472 0.000459 63.685 3939.0 0 P2_gq_gogoq 0.0698 0.00067 315.842 24161.0 0 P1_qq_gogo 0.02219 0.00012 63.09 3722.0 0 P2_qq_gogog 0.01439 9.23e-05 308.44 23235.0 0 and here it is for 2.4.1 Graph Cross-Section ↓ Error Events (K) Unwgt Luminosity P2 sum 0.29763896615 P2_gg_gogog 0.2134 0.00117 116.599 8689.0 0 P1 sum 0.169090624 P1_gg_gogo 0.1469 0.000425 89.093 5333.0 0 P2_gq_gogoq 0.06981 0.000667 315.828 24042.0 0 P1_qq_gogo 0.02222 0.000109 63.122 3782.0 0 P2_qq_gogog 0.01438 9.11e-05 308.632 23088.0 0 So everything seems quite under control here and do not see anything to worry about in this context. I have also check the contribution of each (unphysical) channel of integration (which can have influence on the parton-shower) and they are all in pretty good agreement (pure statistical difference) So from those number it seems that this process is not impacted by any change at all. Now I would suggest to redo such test, with the setup that you are using and check those numbers and key distributions before parton-shower. Additionally, i would suggest to plot the scale of each events, to see how much that distribution change from version to version. If you observe anything weird please come back to me since for this second problem I do not see what can I check more. Cheers, Olivier PS: remember that CKKW-L was not included/supported in 2.3.3. The inclusion of such cut are now officially supported but only since 2.5.0. Since I guess that you include the partonic level cut associate to CKKW-L in 2.3.3 that means that you hack madgraph. The question do you do the same in 2.6.0? If you don’t then the setup might not be exactly the same, if you do you have to check that they do not interfere with each other. So please check how you handle those cuts (that I did not include in my test above since they are not in the official version in 2.3.3)