Fail to reach target error -- adjusting phase-space integration?

Hi,

Did you check 2102.00773 <https://arxiv.org/abs/2102.00773> ?
This contains a lot of information about 2.9.x new version which contains (as explained in the paper)
a lot of tricks to improve the situation.

I would in particular check what happen if I change the SDE_strategy for that process.

Cheers,

Olivier

> On 8 Feb 2021, at 19:15, Radha Mastandrea <email address hidden> wrote:
>
> New question #695430 on MadGraph5_aMC@NLO:
> https://answers.launchpad.net/mg5amcnlo/+question/695430
>
> I am trying a process with a custom Lorentz model (by default, I ask for 10,000 events) that can be controlled with a 2-dimensional coupling. I am finding that I sometimes get a "Fall to reach target" error (only for large values of the coupling -- the process works fine for intermediate values so I don't think the issue is inherently in the model).
>
> I have tried changing the value of the hard_survey parameter, but this doesn't seem to work:
>
> -- when hard_survey = 0, I get 1,814 events
> -- when hard_survey = 1, I get 1,096 events
> -- when hard_survey = 2, I get 22 events
> -- when hard_survey = 3, I get 25 events
>
> I am wondering if there are any other parameters I can try to increase the number of events (such as second_refine_threshold)? If it helps, I am looking at the process u d > g u d, and I know exactly which of the 5 corresponding Feynman diagrams is causing the issue, as the corresponding subprocess is associated with a very low luminosity (2 orders of magnitude lower than the other 4 diagrams).
>
> You received this question notification because you are an answer
> contact for MadGraph5_aMC@NLO.

I've switched to the new MG version (it seems to be a lot faster which is great)! However, I'm running into another problem with the integration (which may not be specific to the version as I didn't make these checks for version 2.8). Namely, the "fail to reach target" seems to be highly variable with the random seed, which is not behavior I would expect.

As an example:
-- random seed 5: makes all 10,000 events
-- random seed 6: fail to reach target with 1,993 events
-- random seed 7: fail to reach target with 449 events
-- random seed 8: makes all 10,000 events

I am wondering how MadGraph decides to discard events, and why the failure is so variable?

Thank you!

Hi,

Not sure I have the full picture myself.
This is a bit of a chaotic system with machine learning algorithm and all the issue of false minima, over-fitting,...
and all the condition linked to threshold effect in a lot of place inside the code.
So it is very difficult to have a good understanding of the impact of any change (This is why I'm so proud of the above paper)

Cheers,

Olivier