Reweighting to points with vanishing cross-sections
Hi,
I have quite a few benchmarks to which I am reweighting my samples. Somewhat by mistake, some parameter points have a certain coupling set to 0, which makes the cross-section vanish. This all good and expected.
However, I have multiple parameter points with this vanishing cross-section and I think it causes some problems.
Even though in the log files I see that the reweighting seems to be done correctly and I only get zeroes where they are supposed to be, the resulting lhe file has zeroes for all the parameter points in nearly all the events.
As far as I have checked, this really seems to be caused by having a few parameter points with vanishing (identical?) cross-sections.
In principle, this is not a big deal - I will be able to avoid all but one such point with a vanishing cross-section, the problem does not occur in that case.
I am using MadGraph_aMC-2.8.0 with python2.
Sincerely,
Simonas
Question information
- Language:
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- Solved
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- Solved by:
- Simonas Drauksas
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Revision history for this message
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#1 |
Can you give your setup where you do have that problem such that I can reproduce your problem?
thanks,
Olivier
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Revision history for this message
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#2 |
Is it okay if I just copy paste a few events from the lhe file along with the header?
(I have trimmed the number of benchmarks, so that it is shorter)
<LesHouchesEvents version="3.0">
<header>
<!--
#******
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# MadGraph5_aMC@NLO *
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# Going Beyond *
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# http://
# http://
# http://
# *
# The MadGraph5_aMC@NLO team *
# *
#......
# *
# This file contains all the information necessary to reproduce *
# the events generated: *
# *
# 1. software version *
# 2. proc_card : code generation info including model *
# 3. param_card : model primary parameters in the LH format *
# 4. run_card : running parameters (collider and cuts) *
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2.8.0
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#* VERSION 2.8.0 2020-08-21 *
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set group_subprocesses Auto
set ignore_
set max_t_for_channel 99
set loop_optimized_
set low_mem_
set default_
set zerowidth_tchannel True
set loop_color_flows False
set gauge unitary
set complex_mass_scheme False
set max_npoint_
import model VPrime_NLO
define p = g u c d s u~ c~ d~ s~
define j = g u c d s u~ c~ d~ s~
define l+ = e+ mu+
define l- = e- mu-
define vl = ve vm vt
define vl~ = ve~ vm~ vt~
define p = 21 2 4 1 3 -2 -4 -1 -3 5 -5 # pass to 5 flavors
define j = p
define wp = wp+ wp-
define mu = mu+ mu-
define vmu = vm vm~
generate p p > wp, wp > mu vmu @0
add process p p > wp j, wp > mu vmu @1
add process p p > wp 2j, wp > mu vmu @2
output ./mg_processes_
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# Additional hidden parameters
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0.4 = drll # hidden_parameter
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<slha>
#######
## PARAM_CARD AUTOMATICALY GENERATED BY MG5 ####
#######
#######
## INFORMATION FOR LOOP
#######
BLOCK LOOP #
1 9.118800e+01 # mu_r
#######
## INFORMATION FOR MASS
#######
BLOCK MASS #
6 1.733000e+02 # mt
23 9.118760e+01 # mz
25 1.257000e+02 # mh
32 3.000000e+03 # mzp
34 2.000000e+02 # madminer
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3 0.000000e+00 # s : 0.0
4 0.000000e+00 # c : 0.0
5 0.000000e+00 # b : 0.0
11 0.000000e+00 # e- : 0.0
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13 0.000000e+00 # mu- : 0.0
14 0.000000e+00 # vm : 0.0
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21 1 1 2 502 501 -1.9628822946e+01 -1.9107179167e+01 -2.3184442362e+02 2.3345708759e+02 0.0000000000e+00 0.0000e+00 1.0000e+00
21 1 1 2 503 502 +2.7994185836e+00 -4.6715399520e+01 -3.3474401063e+02 3.3799958277e+02 0.0000000000e+00 0.0000e+00 1.0000e+00
<scales pt_clust_
<clustering>
<clus scale=" 23.262"> 6 7 -1 -1</clus>
<clus scale=" 74.433"> 2 6 1 -1</clus>
<clus scale=" 199.998"> 4 5 -1 -1</clus>
<clus scale=" 211.223"> 1 4 2 -1</clus>
</clustering>
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</rwgt>
</event>
<event>
7 2 +1.4230697e+01 2.04216600e+02 7.81616400e-03 1.05219000e-01
2 -1 0 0 503 0 -0.0000000000e+00 +0.0000000000e+00 +1.5518175205e+02 1.5518175205e+02 0.0000000000e+00 0.0000e+00 -1.0000e+00
21 -1 0 0 501 502 +0.0000000000e+00 -0.0000000000e+00 -8.4729845901e+02 8.4729845901e+02 0.0000000000e+00 0.0000e+00 -1.0000e+00
34 2 1 2 0 0 -3.4314084803e+01 +4.5688544584e+00 +7.9141782844e+01 2.1796425734e+02 2.0011662809e+02 0.0000e+00 0.0000e+00
-13 1 3 3 0 0 -1.0792715114e+02 -2.2003919441e+01 +8.5510076857e+01 1.3944323457e+02 0.0000000000e+00 0.0000e+00 1.0000e+00
14 1 3 3 0 0 +7.3613066334e+01 +2.6572773899e+01 -6.3682940135e+00 7.8521022767e+01 0.0000000000e+00 0.0000e+00 -1.0000e+00
21 1 1 2 503 502 +4.9368949984e+01 +4.0313421501e+00 -8.7575764788e+01 1.0061341620e+02 0.0000000000e+00 0.0000e+00 1.0000e+00
1 1 1 2 501 0 -1.5054865181e+01 -8.6001966085e+00 -6.8368272502e+02 6.8390253752e+02 0.0000000000e+00 0.0000e+00 -1.0000e+00
<scales pt_clust_
<clustering>
<clus scale=" 17.338"> 2 7 1 -1</clus>
<clus scale=" 40.716"> 2 6 1 -1</clus>
<clus scale=" 200.117"> 4 5 -1 -1</clus>
<clus scale=" 17.338"> 2 4 1 -1</clus>
</clustering>
<rwgt>
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</rwgt>
</event>
<event>
6 1 +1.4230697e+01 2.01238500e+02 7.81616400e-03 1.05426600e-01
2 -1 0 0 502 0 +0.0000000000e+00 +0.0000000000e+00 +6.6604724203e+01 6.6604724203e+01 0.0000000000e+00 0.0000e+00 -1.0000e+00
-1 -1 0 0 0 501 -0.0000000000e+00 -0.0000000000e+00 -1.7408197191e+02 1.7408197191e+02 0.0000000000e+00 0.0000e+00 1.0000e+00
34 2 1 2 0 0 +6.9366597796e+00 +1.2275799397e+01 -9.9649531897e+01 2.2455952542e+02 2.0074396317e+02 0.0000e+00 0.0000e+00
-13 1 3 3 0 0 -4.5515867245e+01 -3.1987109618e+01 -1.3596867813e+02 1.4690932845e+02 0.0000000000e+00 0.0000e+00 1.0000e+00
14 1 3 3 0 0 +5.2452527024e+01 +4.4262909015e+01 +3.6319146238e+01 7.7650196968e+01 0.0000000000e+00 0.0000e+00 -1.0000e+00
21 1 1 2 502 501 -6.9366597796e+00 -1.2275799397e+01 -7.8277158101e+00 1.6127170692e+01 0.0000000000e+00 0.0000e+00 -1.0000e+00
<scales pt_clust_
<clustering>
<clus scale=" 14.100"> 2 6 1 -1</clus>
<clus scale=" 200.744"> 4 5 -1 -1</clus>
<clus scale=" 201.239"> 1 4 2 -1</clus>
</clustering>
<rwgt>
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</rwgt>
</event>
<event>
6 1 +1.4230697e+01 2.00752900e+02 7.81616400e-03 1.05460800e-01
-1 -1 0 0 0 501 -0.0000000000e+00 +0.0000000000e+00 +1.2593499518e+02 1.2593499518e+02 0.0000000000e+00 0.0000e+00 1.0000e+00
2 -1 0 0 502 0 +0.0000000000e+00 -0.0000000000e+00 -9.6574492046e+01 9.6574492046e+01 0.0000000000e+00 0.0000e+00 -1.0000e+00
34 2 1 2 0 0 -1.8125604777e+01 -3.8709671018e-01 +1.9054377224e+01 2.0165514418e+02 1.9993258987e+02 0.0000e+00 0.0000e+00
-13 1 3 3 0 0 -4.8991841477e+01 +8.3056618277e+01 +4.8500499189e+01 1.0793933848e+02 0.0000000000e+00 0.0000e+00 1.0000e+00
14 1 3 3 0 0 +3.0866236700e+01 -8.3443714987e+01 -2.9446121965e+01 9.3715805698e+01 0.0000000000e+00 0.0000e+00 -1.0000e+00
21 1 1 2 502 501 +1.8125604777e+01 +3.8709671018e-01 +1.0306125907e+01 2.0854343039e+01 0.0000000000e+00 0.0000e+00 1.0000e+00
<scales pt_clust_
<clustering>
<clus scale=" 18.130"> 1 6 2 -1</clus>
<clus scale=" 199.933"> 4 5 -1 -1</clus>
<clus scale=" 200.753"> 2 4 1 -1</clus>
</clustering>
<rwgt>
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</rwgt>
</event>
<event>
7 2 +1.4230697e+01 2.01764600e+02 7.81616400e-03 1.05389700e-01
21 -1 0 0 501 502 +0.0000000000e+00 +0.0000000000e+00 +4.7352905079e+02 4.7352905079e+02 0.0000000000e+00 0.0000e+00 -1.0000e+00
1 -1 0 0 503 0 -0.0000000000e+00 -0.0000000000e+00 -5.5849953190e+02 5.5849953190e+02 0.0000000000e+00 0.0000e+00 -1.0000e+00
-34 2 1 2 0 0 -3.2681765762e+00 -2.6965754302e+01 -5.3915576520e+02 5.7569388869e+02 1.9999170342e+02 0.0000e+00 0.0000e+00
13 1 3 3 0 0 +4.5029882175e+01 -4.2414231170e+01 -5.1643689886e+02 5.2012856852e+02 0.0000000000e+00 0.0000e+00 -1.0000e+00
-14 1 3 3 0 0 -4.8298058751e+01 +1.5448476868e+01 -2.2718866344e+01 5.5565320161e+01 0.0000000000e+00 0.0000e+00 1.0000e+00
21 1 1 2 503 502 +7.1162127269e+00 +1.0404799454e+01 +4.3294162788e+01 4.5091960113e+01 0.0000000000e+00 0.0000e+00 -1.0000e+00
2 1 1 2 501 0 -3.8480361507e+00 +1.6560954848e+01 +4.1089112130e+02 4.1124273388e+02 0.0000000000e+00 0.0000e+00 -1.0000e+00
<scales pt_clust_
<clustering>
<clus scale=" 12.606"> 1 6 2 -1</clus>
<clus scale=" 26.688"> 1 7 2 -1</clus>
<clus scale=" 199.992"> 4 5 -1 -1</clus>
<clus scale=" 12.606"> 1 4 2 -1</clus>
</clustering>
<rwgt>
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</rwgt>
</event>
<event>
5 0 +1.4230697e+01 2.00016800e+02 7.81616400e-03 1.05512900e-01
-1 -1 0 0 0 501 -0.0000000000e+00 +0.0000000000e+00 +1.2371104561e+02 1.2371104561e+02 0.0000000000e+00 0.0000e+00 1.0000e+00
2 -1 0 0 501 0 +0.0000000000e+00 -0.0000000000e+00 -8.0847068511e+01 8.0847068511e+01 0.0000000000e+00 0.0000e+00 -1.0000e+00
34 2 1 2 0 0 +0.0000000000e+00 +0.0000000000e+00 +4.2863977103e+01 2.0455811413e+02 2.0001675310e+02 0.0000e+00 0.0000e+00
-13 1 3 3 0 0 -3.2905743832e+01 +1.9938884667e+01 +1.1583894275e+02 1.2206149171e+02 0.0000000000e+00 0.0000e+00 1.0000e+00
14 1 3 3 0 0 +3.2905743832e+01 -1.9938884667e+01 -7.2974965652e+01 8.2496622420e+01 0.0000000000e+00 0.0000e+00 -1.0000e+00
<scales pt_clust_
<clustering>
<clus scale=" 200.017"> 4 5 -1 -1</clus>
<clus scale=" 200.017"> 2 4 1 -1</clus>
</clustering>
<rwgt>
<wgt id='bm_wp_0_kl_0'> +0.0000000e+00 </wgt>
<wgt id='bm_wp_1_kl_0'> +0.0000000e+00 </wgt>
<wgt id='bm_wp_1_kl_1'> +0.0000000e+00 </wgt>
<wgt id='bm_wp_2_kl_0'> +0.0000000e+00 </wgt>
<wgt id='bm_wp_2_kl_1'> +0.0000000e+00 </wgt>
<wgt id='bm_wp_3_kl_0'> +0.0000000e+00 </wgt>
<wgt id='bm_wp_3_kl_1'> +0.0000000e+00 </wgt>
<wgt id='bm_wp_4_kl_0'> +0.0000000e+00 </wgt>
<wgt id='bm_wp_4_kl_1'> +0.0000000e+00 </wgt>
<wgt id='bm_wp_5_kl_0'> +0.0000000e+00 </wgt>
<wgt id='bm_wp_5_kl_1'> +0.0000000e+00 </wgt>
<wgt id='bm_wp_6_kl_0'> +0.0000000e+00 </wgt>
<wgt id='bm_wp_6_kl_1'> +0.0000000e+00 </wgt>
<wgt id='bm_wp_7_kl_0'> +0.0000000e+00 </wgt>
<wgt id='bm_wp_7_kl_1'> +0.0000000e+00 </wgt>
<wgt id='bm_wp_8_kl_0'> +0.0000000e+00 </wgt>
<wgt id='bm_wp_8_kl_1'> +0.0000000e+00 </wgt>
<wgt id='bm_wp_9_kl_0'> +0.0000000e+00 </wgt>
<wgt id='bm_wp_9_kl_1'> +0.0000000e+00 </wgt>
<wgt id='bm_wp_10_kl_0'> +0.0000000e+00 </wgt>
<wgt id='bm_wp_10_kl_1'> +0.0000000e+00 </wgt>
<wgt id='bm_wp_11_kl_0'> +0.0000000e+00 </wgt>
<wgt id='bm_wp_11_kl_1'> +0.0000000e+00 </wgt>
<wgt id='bm_wp_12_kl_0'> +0.0000000e+00 </wgt>
<wgt id='bm_wp_12_kl_1'> +0.0000000e+00 </wgt>
<wgt id='bm_wp_13_kl_0'> +0.0000000e+00 </wgt>
<wgt id='bm_wp_13_kl_1'> +0.0000000e+00 </wgt>
<wgt id='bm_wp_14_kl_0'> +0.0000000e+00 </wgt>
<wgt id='bm_wp_14_kl_1'> +0.0000000e+00 </wgt>
<wgt id='bm_wp_15_kl_0'> +0.0000000e+00 </wgt>
<wgt id='bm_wp_15_kl_1'> +0.0000000e+00 </wgt>
<wgt id='bm_wp_16_kl_0'> +0.0000000e+00 </wgt>
<wgt id='bm_wp_16_kl_1'> +0.0000000e+00 </wgt>
<wgt id='bm_wp_17_kl_0'> +0.0000000e+00 </wgt>
<wgt id='bm_wp_17_kl_1'> +0.0000000e+00 </wgt>
<wgt id='bm_wp_18_kl_0'> +0.0000000e+00 </wgt>
<wgt id='bm_wp_18_kl_1'> +0.0000000e+00 </wgt>
<wgt id='sm'> +0.0000000e+00 </wgt>
</rwgt>
</event>
and so on...
If needed, I can also post the log file...which seems to have nothing out of the usual.
Sincerely,
Simonas
|
Revision history for this message
|
#3 |
Thanks,
Could you paste the reweight_card in top of that?
and also send me a link to the model (if not a public model you can send it to my email address: <email address hidden>
Cheers,
Olivier
|
Revision history for this message
|
#4 |
No problem, here is the link to the model:
http://
And here is the reweight_card
# Reweight card generated by MadMiner
# Global setup
change output default
change helicity False
# MadMiner benchmark bm_wp_0_kl_0
launch --rwgt_
set mass 34 200
set ssmcoup 1 0.0
set decay 34 auto
# MadMiner benchmark bm_wp_1_kl_0
launch --rwgt_
set mass 34 300
set ssmcoup 1 0.0
set decay 34 auto
# MadMiner benchmark bm_wp_1_kl_1
launch --rwgt_
set mass 34 300
set ssmcoup 1 0.1
set decay 34 auto
# MadMiner benchmark bm_wp_2_kl_0
launch --rwgt_
set mass 34 400
set ssmcoup 1 0.0
set decay 34 auto
# MadMiner benchmark bm_wp_2_kl_1
launch --rwgt_
set mass 34 400
set ssmcoup 1 0.1
set decay 34 auto
# MadMiner benchmark bm_wp_3_kl_0
launch --rwgt_
set mass 34 500
set ssmcoup 1 0.0
set decay 34 auto
# MadMiner benchmark bm_wp_3_kl_1
launch --rwgt_
set mass 34 500
set ssmcoup 1 0.1
set decay 34 auto
# MadMiner benchmark bm_wp_4_kl_0
launch --rwgt_
set mass 34 600
set ssmcoup 1 0.0
set decay 34 auto
# MadMiner benchmark bm_wp_4_kl_1
launch --rwgt_
set mass 34 600
set ssmcoup 1 0.1
set decay 34 auto
# MadMiner benchmark bm_wp_5_kl_0
launch --rwgt_
set mass 34 700
set ssmcoup 1 0.0
set decay 34 auto
# MadMiner benchmark bm_wp_5_kl_1
launch --rwgt_
set mass 34 700
set ssmcoup 1 0.1
set decay 34 auto
# MadMiner benchmark bm_wp_6_kl_0
launch --rwgt_
set mass 34 800
set ssmcoup 1 0.0
set decay 34 auto
# MadMiner benchmark bm_wp_6_kl_1
launch --rwgt_
set mass 34 800
set ssmcoup 1 0.1
set decay 34 auto
# MadMiner benchmark bm_wp_7_kl_0
launch --rwgt_
set mass 34 900
set ssmcoup 1 0.0
set decay 34 auto
# MadMiner benchmark bm_wp_7_kl_1
launch --rwgt_
set mass 34 900
set ssmcoup 1 0.1
set decay 34 auto
# MadMiner benchmark bm_wp_8_kl_0
launch --rwgt_
set mass 34 1000
set ssmcoup 1 0.0
set decay 34 auto
# MadMiner benchmark bm_wp_8_kl_1
launch --rwgt_
set mass 34 1000
set ssmcoup 1 0.1
set decay 34 auto
# MadMiner benchmark bm_wp_9_kl_0
launch --rwgt_
set mass 34 1100
set ssmcoup 1 0.0
set decay 34 auto
# MadMiner benchmark bm_wp_9_kl_1
launch --rwgt_
set mass 34 1100
set ssmcoup 1 0.1
set decay 34 auto
# MadMiner benchmark bm_wp_10_kl_0
launch --rwgt_
set mass 34 1200
set ssmcoup 1 0.0
set decay 34 auto
# MadMiner benchmark bm_wp_10_kl_1
launch --rwgt_
set mass 34 1200
set ssmcoup 1 0.1
set decay 34 auto
# MadMiner benchmark bm_wp_11_kl_0
launch --rwgt_
set mass 34 1300
set ssmcoup 1 0.0
set decay 34 auto
# MadMiner benchmark bm_wp_11_kl_1
launch --rwgt_
set mass 34 1300
set ssmcoup 1 0.1
set decay 34 auto
# MadMiner benchmark bm_wp_12_kl_0
launch --rwgt_
set mass 34 1400
set ssmcoup 1 0.0
set decay 34 auto
# MadMiner benchmark bm_wp_12_kl_1
launch --rwgt_
set mass 34 1400
set ssmcoup 1 0.1
set decay 34 auto
# MadMiner benchmark bm_wp_13_kl_0
launch --rwgt_
set mass 34 1500
set ssmcoup 1 0.0
set decay 34 auto
# MadMiner benchmark bm_wp_13_kl_1
launch --rwgt_
set mass 34 1500
set ssmcoup 1 0.1
set decay 34 auto
# MadMiner benchmark bm_wp_14_kl_0
launch --rwgt_
set mass 34 1600
set ssmcoup 1 0.0
set decay 34 auto
# MadMiner benchmark bm_wp_14_kl_1
launch --rwgt_
set mass 34 1600
set ssmcoup 1 0.1
set decay 34 auto
# MadMiner benchmark bm_wp_15_kl_0
launch --rwgt_
set mass 34 1700
set ssmcoup 1 0.0
set decay 34 auto
# MadMiner benchmark bm_wp_15_kl_1
launch --rwgt_
set mass 34 1700
set ssmcoup 1 0.1
set decay 34 auto
# MadMiner benchmark bm_wp_16_kl_0
launch --rwgt_
set mass 34 1800
set ssmcoup 1 0.0
set decay 34 auto
# MadMiner benchmark bm_wp_16_kl_1
launch --rwgt_
set mass 34 1800
set ssmcoup 1 0.1
set decay 34 auto
# MadMiner benchmark bm_wp_17_kl_0
launch --rwgt_
set mass 34 1900
set ssmcoup 1 0.0
set decay 34 auto
# MadMiner benchmark bm_wp_17_kl_1
launch --rwgt_
set mass 34 1900
set ssmcoup 1 0.1
set decay 34 auto
# MadMiner benchmark bm_wp_18_kl_0
launch --rwgt_
set mass 34 2000
set ssmcoup 1 0.0
set decay 34 auto
# MadMiner benchmark bm_wp_18_kl_1
launch --rwgt_
set mass 34 2000
set ssmcoup 1 0.1
set decay 34 auto
# MadMiner benchmark sm
launch --rwgt_name=sm
set mass 34 0
set ssmcoup 1 0.0
set decay 34 auto
Sincerely,
Simonas
|
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|
#5 |
Thanks I can reproduce your run now.
Will report what I can find.
Thanks,
Olivier
|
Revision history for this message
|
#6 |
Hi,
I think that the issue is related to some optimization with the helicity filtering which are not correctly reset when changing benchmark. I'm working on a patch.
Cheers,
Olivier
|
Revision history for this message
|
#7 |
Hi,
You will find the patch (and a full tarball) fixing the issue here:
https:/
Note that such version is python2 only which should not be a problem for you.
Cheers,
Olivier
