NA61/SHINE online noise filtering using machine learning methods

Kawęcka, Anna; Bryliński, W.; Kuttan, Manjunath Omana; Linnyk, O.; Pawlowski, Janik; Schmidt, K.; Słodkowski, M.; Wyszyński, O.; Zieliński, Jakub

doi:10.1088/1742-6596/2438/1/012104

Cited by 2 publications

(2 citation statements)

References 4 publications

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“…The data is now a three dimensional array containing an "image-like" representation of the detector output. Convolution Neural Network (CNN) based algorithms are commonly used to train Neural Network models on "image-like" data [5,6]. However, most of the bins in such a histogram will not contain any hit.…”

Section: Deep Learning Heavy-ion Collisions With Pointnetmentioning

confidence: 99%

Extraction of global event features in heavy-ion collision experiments using PointNet

Kuttan¹,

Steinheimer²,

Zhou³

et al. 2023

Proceedings of FAIR Next Generation Scientists - 7th Edition Workshop — PoS(FAIRness2022)

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A Deep Learning based method to extract global event properties in heavy-ion collision experiments is introduced. The order invariant, point cloud representation of experimental data is chosen to train the models. The point clouds of hits recorded in detector planes or tracks reconstructed from these hits can be represented as a 2 dimensional array in which each row stores an arbitrary hit or track. The PointNet is a Deep Learning model that is designed to efficiently perform classification or regression tasks using such a representation of data. The PointNet based models are shown to accurately determine different properties of heavy-ion collisions by learning several global features of the input point cloud that can be mapped to a target property. In particular, we demonstrate that the PointNet based models can accurately determine the collision impact parameter on event-by-event basis, and classify the events based on the nature of the QCD transition as implemented in the hydrodynamic phase of the collision. These models outperform conventional analysis techniques and can work directly on free streaming detector output with extremely high event rates.

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Section: Deep Learning Heavy-ion Collisions With Pointnetmentioning

confidence: 99%

Extraction of global event features in heavy-ion collision experiments using PointNet

Kuttan¹,

Steinheimer²,

Zhou³

et al. 2023

Proceedings of FAIR Next Generation Scientists - 7th Edition Workshop — PoS(FAIRness2022)

View full text Add to dashboard Cite

show abstract

“…CNNs are a good choice of algorithm for extracting correlations from image-like heavy-ion collision data, i.e. data provided in the form of equally spaced multidimensional histograms [180,181]. However, histogramming of data can lead to loss of information.…”

Section: Convolution Neural Networkmentioning

confidence: 99%

Artificial intelligence in heavy-ion collisions : bridging the gap between theory and experiments

Omana Kuttan

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Artificial Intelligence (AI) methods are employed to study heavy-ion collisions at intermediate collision energies, where high baryon density and moderate temperature QCD matter is produced. The experimental measurements of various conventional observables such as collective flow, particle number fluctuations, etc. are usually compared with expensive model calculations to infer the physics governing the evolution of the matter produced in the collisions. Various experimental effects and processing algorithms can greatly affect the sensitivity of these observables. AI methods are used to bridge this gap between theory and experiments of heavy-ion collisions. The problems with conventional methods of analyzing experimental data are illustrated in a comparative study of the Glauber MC model and the UrQMD transport model. It is found that the centrality determination and the estimated fluctuations of the number of participant nucleons suffer from strong model dependencies for Au-Au collisions at 1.23 AGeV. This can bias the results of the experimental analysis if the number of participant nucleons used is not consistent throughout the analysis and in the final model-to-data comparison. The measurable consequences of this model dependence of the number of participant nucleons are also discussed. In this context, PointNet-based AI models are developed to accurately reconstruct the impact parameter or the number of participant nucleons in a collision event from the hits and/or reconstructed track of particles in 10 AGeV Au-Au collisions at the CBM experiment. In the last part of the thesis, different AI methods to study the equation of state (EoS) at high baryon densities are discussed. First, a Bayesian inference is performed to constrain the density dependence of the EoS from the available experimental measurements of elliptical flow and mean transverse kinetic energy of mid rapidity protons in intermediate energy collisions. The UrQMD model was augmented to include arbitrary potentials (or equivalently the EoSs) in the QMD part to provide a consistent treatment of the EoS throughout the evolution of the system. The experimental data constrain the posterior constructed for the EoS for densities up to four times saturation density. However, beyond three times saturation density, the shape of the posterior depends on the choice of observables used. There is a tension in the measurements at a collision energy of about 4 GeV. This could indicate large uncertainties in the measurements, or alternatively the inability of the underlying model to describe the observables with a given input EoS. Tighter constraints and fully conclusive statements on the EoS require accurate, high statistics data in the whole beam energy range of 2-10 GeV, which will hopefully be provided by the beam energy scan programme of STAR-FXT at RHIC, the upcoming CBM experiment at FAIR, and future experiments at HIAF and NICA. Finally, it is shown that the PointNet-based models can also be used to identify the equation of state in the CBM experiment. Despite the uncertainties due to limited detector acceptance and biases in the reconstruction algorithms, the PointNet-based models are able to learn the features that can accurately identify the underlying physics of the collision. The PointNet-based models are an ideal AI tool to study heavy-ion collisions, not only to identify the geometric event features, such as the impact parameter or the number of participant nucleons, but also to extract abstract physical features, such as the EoS, directly from the detector outputs.

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NA61/SHINE online noise filtering using machine learning methods

Cited by 2 publications

References 4 publications

Extraction of global event features in heavy-ion collision experiments using PointNet

Extraction of global event features in heavy-ion collision experiments using PointNet

Artificial intelligence in heavy-ion collisions : bridging the gap between theory and experiments

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