Artificial neural networks-based track fitting of cosmic muons through stacked resistive plate chambers

Samuel, D.; Suresh, K.S.

doi:10.1088/1748-0221/13/10/p10035

Cited by 9 publications

(10 citation statements)

References 16 publications

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“…It employs multiple layered Artificial Neural Networks to learn higher dimensional correlations in the data. Machine learning and Deep Learning methods have been widely used both in theory [34][35][36][37][38][39][40][41][42][43][44][45][46][47][48] and in experimental high energy physics [49][50][51][52][53][54][55][56][57][58][59][60][61][62][63][64][65][66][67][68]. Previous studies [18,20] on identifying the QCD phase transitions have shown that Convolutional Neural Network (CNN) based models can accurately classify the underlying equation of state from a hydrodynamic evolution using the p tφ spectra of pions (differential transverse and angular distributions in the transverse plane).…”

Section: Pointnet For Classifying the Eosmentioning

confidence: 99%

An equation-of-state-meter for CBM using PointNet

Kuttan

Zhou

Steinheimer

et al. 2021

J. High Energ. Phys.

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A novel method for identifying the nature of QCD transitions in heavy-ion collision experiments is introduced. PointNet based Deep Learning (DL) models are developed to classify the equation of state (EoS) that drives the hydrodynamic evolution of the system created in Au-Au collisions at 10 AGeV. The DL models were trained and evaluated in different hypothetical experimental situations. A decreased performance is observed when more realistic experimental effects (acceptance cuts and decreased resolutions) are taken into account. It is shown that the performance can be improved by combining multiple events to make predictions. The PointNet based models trained on the reconstructed tracks of charged particles from the CBM detector simulation discriminate a crossover transition from a first order phase transition with an accuracy of up to 99.8%. The models were subjected to several tests to evaluate the dependence of its performance on the centrality of the collisions and physical parameters of fluid dynamic simulations. The models are shown to work in a broad range of centralities (b=0–7 fm). However, the performance is found to improve for central collisions (b=0–3 fm). There is a drop in the performance when the model parameters lead to reduced duration of the fluid dynamic evolution or when less fraction of the medium undergoes the transition. These effects are due to the limitations of the underlying physics and the DL models are shown to be superior in its discrimination performance in comparison to conventional mean observables.

show abstract

Section: Pointnet For Classifying the Eosmentioning

confidence: 99%

An equation-of-state-meter for CBM using PointNet

Kuttan

Zhou

Steinheimer

et al. 2021

J. High Energ. Phys.

View full text Add to dashboard Cite

show abstract

“…It employs multiple layered Artificial Neural Networks to learn higher dimensional correlations in the data. Machine learning and Deep Learning methods have been widely used both in theory [34][35][36][37][38][39][40][41][42][43][44][45][46][47][48] and in experimental high energy physics [49][50][51][52][53][54][55][56][57][58][59][60][61][62][63][64][65][66][67][68]. Previous studies [18,20] on identifying the QCD phase transitions have shown that Convolutional Neural Network (CNN) based models can accurately classify the underlying equation of state from a hydrodynamic evolution using the p t -φ spectra of pions (differential transverse and angular distributions in the transverse plane).…”

Section: Pointnet For Classifying the Eosmentioning

confidence: 99%

An equation-of-state-meter for CBM using PointNet

Kuttan,

Zhou,

Steinheimer

et al. 2021

Preprint

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A novel method for identifying the nature of QCD transitions in heavy-ion collision experiments is introduced. PointNet based Deep Learning (DL) models are developed to classify the equation of state (EoS) that drives the hydrodynamic evolution of the system created in Au-Au collisions at 10 AGeV. The DL models were trained and evaluated in different hypothetical experimental situations. A decreased performance is observed when more realistic experimental effects (acceptance cuts and decreased resolutions) are taken into account. It is shown that the performance can be improved by combining multiple events to make predictions. The PointNet based models trained on the reconstructed tracks of charged particles from the CBM detector simulation discriminate a crossover transition from a first order phase transition with an accuracy of up to 99.8%. The models were subjected to several tests to evaluate the dependence of its performance on the centrality of the collisions and physical parameters of fluid dynamic simulations. The models are shown to work in a broad range of centralities (b=0-7 fm). However, the performance is found to improve for central collisions (b=0-3 fm). There is a drop in the performance when the model parameters lead to reduced duration of the fluid dynamic evolution or when less fraction of the medium undergoes the transition. These effects are due to the limitations of the underlying physics and the DL models are shown to be superior in its discrimination performance in comparison to conventional mean observables.

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“…To generate these events, a uniform random number D1 is generated between 0 and 100 for each hit in a track. If the generated random number D1 is less than then the strip hit is removed from the track [3].…”

Section: Dataset Ii: Tracks Including Detector Efficiencymentioning

confidence: 99%

“…If S1 is greater than 100 hits is added on the right side of the main strip and if the generated random number is greater than 50 and less than 100, the hits corresponding to these numbers are allowed to remain the same. [3].…”

Section: Dataset Iii: Tracks Including and Strip Hit Multiplicity ( )mentioning

confidence: 99%

“…To account for this parameter, extra hits were added to each layer by generating a random integer number R1 for each layer between 0 and . Then, R1 random integers between 0 and 32 are generated and the corresponding strip numbers are set to be hits [3]. In this study the maximum noise Considered is = 3 hits per layer.…”

Section: Dataset Iv: Tracks Including and And Noise Multiplicity ( )mentioning

confidence: 99%

See 1 more Smart Citation

Performance of machine learning algorithm for predicting muon multiplicity in the INO-ICAL prototype

Murgod¹,

Samuel²,

Dutta³

2021

Proceedings of 37th International Cosmic Ray Conference — PoS(ICRC2021)

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In most of the analyses using the cosmic muon data from the INO-ICAL prototype stack, only single muon events are considered. Multi-muon events appear to be noisy events to the algorithm and thus get rejected, reducing the physics potential of the detector. To address this issue, we have developed an ML algorithm to predict muon multiplicity in cosmic muon event registered in the INO-ICAL detector at TIFR. In this work, we present the performance of this algorithm in terms of its efficiency to correctly tag multi-muon events and also to predict the multiplicity.

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Artificial neural networks-based track fitting of cosmic muons through stacked resistive plate chambers

Cited by 9 publications

References 16 publications

An equation-of-state-meter for CBM using PointNet

An equation-of-state-meter for CBM using PointNet

An equation-of-state-meter for CBM using PointNet

Performance of machine learning algorithm for predicting muon multiplicity in the INO-ICAL prototype

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