Event generation for the LHC can be supplemented by generative adversarial networks, which generate physical events and avoid highly inefficient event unweighting. For top pair production we show how such a network describes
LHC analyses directly comparing data and simulated events bear the danger of using first-principle predictions only as a black-box part of event simulation. We show how simulations, for instance, of detector effects can instead be inverted using generative networks. This allows us to reconstruct parton level information from measured events. Our results illustrate how, in general, fully conditional generative networks can statistically invert Monte Carlo simulations. As a technical by-product we show how a maximum mean discrepancy loss can be staggered or cooled.
For simulations where the forward and the inverse directions have
a physics meaning, invertible neural networks are especially useful.
A conditional INN can invert a detector simulation in terms of
high-level observables, specifically for ZW production at the
LHC. It allows for a per-event statistical interpretation. Next, we
allow for a variable number of QCD jets. We unfold detector effects
and QCD radiation to a pre-defined hard process, again with a
per-event probabilistic interpretation over parton-level phase
space.
Event generation with neural networks has seen
significant progress recently. The big open question is
still how such new methods will accelerate LHC simulations
to the level required by upcoming LHC runs. We target a
known bottleneck of standard simulations and show how their
unweighting procedure can be improved by generative
networks. This can, potentially, lead to a very significant
gain in simulation speed.
Subtracting event samples is a common task in LHC simulation and
analysis, and standard solutions tend to be inefficient. We employ
generative adversarial networks to produce new event samples
with a phase space distribution corresponding to added or subtracted
input samples. We first illustrate for a toy example how such a
network beats the statistical limitations of the training
data. We then show how such a network can be used to subtract
background events or to include non-local collinear subtraction
events at the level of unweighted 4-vector events.
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