Unfolding in particle physics: a window on solving inverse problems

Spanò, F.

doi:10.1051/epjconf/20135503002

Cited by 16 publications

(19 citation statements)

References 31 publications

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“…Unfolding particle physics events is a classic example for an inverse problem [51][52][53]. In the limit where detector effects can be described by Gaussian noise, it is similar to unblurring images.…”

Section: Unfolding Basicsmentioning

confidence: 99%

Invertible networks or partons to detector and back again

et al. 2020

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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.

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Section: Unfolding Basicsmentioning

confidence: 99%

Invertible networks or partons to detector and back again

et al. 2020

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“…It is well-known that it is therefore necessary to model the finite detector resolution in order to get a realistic detector response for the jet mass. This can be done by for example dividing up the η-φ space into realistic calorimeter cells, summing up the energies of the particles entering each (potentially with a species-dependent factor), and clustering jets with these pseudo-calorimeter cells as inputs rather than the raw particles 4 . For observables which are designed to be extremely sensitive to the spatial distribution of the radiation inside the jet it is not clear that even such a treatment is sufficient.…”

Section: Jet Substructure Smearingmentioning

confidence: 99%

Fast simulation of detector effects in Rivet

2020

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We describe the design and implementation of detector-bias emulation in the Rivet MC event analysis system. Implemented using C++ efficiency and kinematic smearing functors, it allows detector effects to be specified within an analysis routine, customised to the exact phase-space and reconstruction working points of the analysis. A set of standard detector functions for the physics objects of Runs 1 and 2 of the ATLAS and CMS experiments is also provided. Finally, as jet substructure is an important class of physics observable usually considered to require an explicit detector simulation, we demonstrate that a smearing approach, tuned to available substructure data and implemented in Rivet, can accurately reproduce jet-structure biases observed by ATLAS. MC truth Detector hits Digitization TriggerDet Reco

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“…by considering LHC data as observed by a perfect detector with an infinite resolution and an ideal calibration. This requires an excellent understanding of the background and is a complex, ill-defined and time-consuming problem, as there is no unique solution to the inversion of the convolution of the detector response [1]. Consequently, reinterpretations are usually performed by adopting a second approach in which the detector effects are included, or folded forward, in the simulation of the new physics signals to be confronted to data.…”

Section: Introductionmentioning

confidence: 99%

“…The new features introduced in this work are available from version 1.8.51 of the code, that can can be downloaded from LaunchPad. 1 As said above, this release of the programme allows for the simulation of the impact of a detector in a way that combines realism and efficiency. In addition, it allows for dedicated studies singling out specific detector effects, that could potentially be applied directly at the particle level.…”

Section: Introductionmentioning

confidence: 99%

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Simplified fast detector simulation in MadAnalysis 5

2021

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We introduce a new simplified fast detector simulator in the MadAnalysis 5 platform. The Python-like interpreter of the programme has been augmented by new commands allowing for a detector parametrisation through smearing and efficiency functions. On run time, an associated C++ code is automatically generated and executed to produce reconstructed-level events. In addition, we have extended the MadAnalysis 5 recasting infrastructure to support our detector emulator, and we provide predefined LHC detector configurations. We have compared predictions obtained with our approach to those resulting from the usage of the Delphes 3 software, both for Standard Model processes and a few new physics signals. Results generally agree to a level of about 10% or better, the largest differences in the predictions stemming from the different strategies that are followed to model specific detector effects. Equipped with these new functionalities, MadAnalysis 5 now offers a new user-friendly way to include detector effects when analysing collider events, the simulation of the detector and the analysis being both handled either through a set of intuitive Python commands or directly within the C++ core of the platform.

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Unfolding in particle physics: a window on solving inverse problems

Cited by 16 publications

References 31 publications

Invertible networks or partons to detector and back again

Invertible networks or partons to detector and back again

Fast simulation of detector effects in Rivet

Simplified fast detector simulation in MadAnalysis 5

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