| | 58 | {{{--lambdaCMS=(1.0e-6,5)}}}:: |
| | 59 | Sets what values of the scaling parameter $\lambda$ must be used for the test. This option can either be a tuple '(min_val, points_per_decade)', a float 'min_val' or a python list. The float 'min_val' is the minimal value of $\lambda$ to probe and $points_per_decade$ is the number points one should spread uniformly in each decade (i.e. interval [$10^{-i+1},10^-i$]. Notice that the list must always contain the value 1. |
| | 60 | * Example: --lambdaCMS=(1.0e-2,5) |
| | 61 | With this option, the list of $\lambda$ values used will be: [1, 0.8, 0.6, 0.4, 0.2, 0.1, 0.08, 0.06, 0.04, 0.02, 0.01] |
| | 62 | * Example: --lambdaCMS=[float('1.0e-%d'%exp)\ for\ exp\ in\ range(8)] |
| | 63 | With this option, the list will be evaluated and the return list will be used, i.e.: [1.0, 0.1, 0.01, 0.001, 0.0001, 1e-05, 1e-06, 1e-07]. Notice that spaces must be escaped and this options should be placed last for parsing reasons. |
| | 64 | |
| | 65 | {{{--show_plot=True}}}:: |
| | 66 | Allow to turn off the matplotlib generation and only report the outcome of the numerical check. Turning it off also removes the progress_bar display during the check |
| | 67 | * Example: --show_plot=False |
| | 68 | |
| | 69 | {{{--recompute_width=auto}}}:: |
| | 70 | Decides how to compute the leading-order accurate width necessary for the test to pass. Four possible values 'never', 'first_time', 'always' or 'auto'. 'never' means that the width for $\lambda=1$ will be taken from the value in the default param_card .dat and the widths for subsequent smaller values of $\lambda$ will be computed via a simple scaling law. 'first_time' means that the widths will be computed (numerically or analytically if the model has a decay module) with MadWidth' for $\lambda=1$ and scaled down for lower $\lambda$ values. 'always' means that the widths will be recomputed for all $\lambda$ values (this mode is only to be used for checking the width computation). Finally the default value 'auto' will be interpreted as 'never' for an LO test where LO-accurate width aren't necessary in the offshell region and 'first_time' if the check is NLO. |
| | 71 | * Example: --recompute_width=never |
| | 72 | |
| | 85 | |
| | 86 | {{{--diff_lambda_power=1}}}:: |
| | 87 | This controls by which power to divide the difference term $\Lambda$. The default is of course equal to one so as to test $\kappa^{\text{NLO|LO}}_0=0$ but at LO it is sometimes interesting to divide $\Delta$ by $\lambda^2$ so a to see if $\kappa^{\text{LO}}_1$ is vanishing as well. This is expected to be the case for all $2\rightarrow2$ processes (i.e. the plot of $\Delta/\lambda^2$ has a constant asymptot as well, so that $\kappa^{\text{LO}}_1=0$). For such processes, the higher order contribution $\kappa^{\text{NLO}}_0$ is zero by construction and the test is not sensitive to the CMS implementation |
| | 88 | * Example: check cms u d~ > e+ ve --diff_lambda_power=2 |
| | 89 | |
| | 90 | {{{ --loop_filter=None}}}: |
| | 91 | Allows to specify a conditional expression to impose a requirement on the loop diagrams to be kept. This expression can only be an pyton expression of involving the following variables : 'n', the number of loop propagators; 'id' the loop diagram number as it can be read in the postscript generated with the command display diagrams; 'loop_pdgs' the list of absolute values of the PDG of the particles running in the loop; 'loop_masses' and 'struct_masses' the list of the parameter names of the masses running in the loop and the masses of the particles directly attached to the loop. |
| | 92 | * Example: --loop_filter='n>3' |
| | 93 | Selects only box diagrams and above. |
| | 94 | * Example: --loop_filter='n<4 and 6 in loop_pdgs and 3<=id<=7' |
| | 95 | Selects only triangle loop or smaller, with at least a top running in the loop and whose ID is in the range [3,7]. |
