A deep learning approach to multi-track location and orientation in gaseous drift chambers

Ai, Pengcheng; Wang, Dong; Sun, X.; Li, Zili

doi:10.1016/j.nima.2020.164640

Cited by 4 publications

(2 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For simplicity, the radiative correction leading up to a 4% reduction [41] of the cross-section is not included, as the effect is much smaller than the uncertainty arising from the neutrino flux. is approximately 0.22, including the effect from oscillation and the difference in crosssections.…”

Section: A Electron Scatteringmentioning

confidence: 99%

Solar neutrino background in high-pressure gaseous ⁸²SeF₆ TPC neutrinoless double beta decay experiments*

Wang 王,

Wang 汪,

Zhang 张

2024

Chinese Phys. C

View full text Add to dashboard Cite

The possible solar neutrino background in a future neutrinoless double beta decay experiment using high-pressure gaseous SeF6 TPC is discussed. Various contributions are simulated and possible features that could be used for event classification are discussed.Two types of backgrounds are identified. The rate of the multi-site background events is about 0.63 events/(ton yr) in a 30 keV ROI window. This background could be effectively reduced to less than 0.0001~events/(ton yr) (95% C.L.) while keeping a high signal efficiency of 93% by applying a selection based on the number of clusters and the energy of the leading cluster. The rate of the single-electron background events is about 0.01 events/(ton yr) in the ROI. Assuming a factor of 10 reduction of the single-electron background events from algorithms developed for radioactive background rejection, the total background induced by the solar neutrino would be 0.001 events/(ton yr). It's small enough for ton-level experiments.

show abstract

Section: A Electron Scatteringmentioning

confidence: 99%

Solar neutrino background in high-pressure gaseous ⁸²SeF₆ TPC neutrinoless double beta decay experiments*

Wang 王,

Wang 汪,

Zhang 张

2024

Chinese Phys. C

View full text Add to dashboard Cite

show abstract

“…For example, [15,16] use three-dimensional convolutional neural networks (CNN) for particle/event identification and energy regression in a sensitive volume. [17,18] utilize two-dimensional CNNs for regression of particular physical information (position, energy) on a grid. Besides, one-dimensional CNNs have been applied to pulse timing for upgrades of calorimeters in ALICE experiment [9].…”

Section: Related Workmentioning

confidence: 99%

Neural network-featured timing systems for radiation detectors: performance evaluation based on bound analysis

Deng

Wang

et al. 2021

J. Inst.

View full text Add to dashboard Cite

Waveform sampling systems are used pervasively in the design of front end electronics for radiation detection. The introduction of new feature extraction algorithms (eg. neural networks) to waveform sampling has the great potential to substantially improve the performance and enrich the capability. To analyze the limits of such algorithms and thus illuminate the direction of resolution optimization, in this paper we systematically simulate the detection procedure of contemporary radiation detectors with an emphasis on pulse timing. Neural networks and variants of constant fraction discrimination are studied in a wide range of analog channel frequency and noise level. Furthermore, we propose an estimation of multivariate Cramér Rao lower bound within the model using intrinsic-extrinsic parametrization and prior information. Two case studies (single photon detection and shashlik-type calorimeter) verify the reliability of the proposed method and show it works as a useful guideline when assessing the abilities of various feature extraction algorithms.

show abstract

An online fast multi-track locating algorithm for high-resolution single-event effect test platform

Yang

Zhang

et al. 2023

NUCL SCI TECH

View full text Add to dashboard Cite

To improve the efficiency and accuracy of single-event effect (SEE) research at the Heavy Ion Research Facility at Lanzhou, Hi’Beam-SEE must precisely localize the position at which each heavy ion hitting the integrated circuit (IC) causes SEE. In this study, we propose a fast multi-track location (FML) method based on deep learning to locate the position of each particle track with high speed and accuracy. FML can process a vast amount of data supplied by Hi’Beam-SEE online, revealing sensitive areas in real time. FML is a slot-based object-centric encoder–decoder structure in which each slot can learn the location information of each track in the image. To make the method more accurate for real data, we designed an algorithm to generate a simulated dataset with a distribution similar to that of the real data, which was then used to train the model. Extensive comparison experiments demonstrated that the FML method, which has the best performance on simulated datasets, has high accuracy on real datasets as well. In particular, FML can reach 238 fps and a standard error of 1.6237 μm. This study discusses the design and performance of FML.

show abstract

A deep learning approach to multi-track location and orientation in gaseous drift chambers

Cited by 4 publications

References 21 publications

Solar neutrino background in high-pressure gaseous ⁸²SeF₆ TPC neutrinoless double beta decay experiments*

Solar neutrino background in high-pressure gaseous ⁸²SeF₆ TPC neutrinoless double beta decay experiments*

Neural network-featured timing systems for radiation detectors: performance evaluation based on bound analysis

An online fast multi-track locating algorithm for high-resolution single-event effect test platform

Contact Info

Product

Resources

About

A deep learning approach to multi-track location and orientation in gaseous drift chambers

Cited by 4 publications

References 21 publications

Solar neutrino background in high-pressure gaseous 82SeF6 TPC neutrinoless double beta decay experiments*

Solar neutrino background in high-pressure gaseous 82SeF6 TPC neutrinoless double beta decay experiments*

Neural network-featured timing systems for radiation detectors: performance evaluation based on bound analysis

An online fast multi-track locating algorithm for high-resolution single-event effect test platform

Contact Info

Product

Resources

About

Solar neutrino background in high-pressure gaseous ⁸²SeF₆ TPC neutrinoless double beta decay experiments*

Solar neutrino background in high-pressure gaseous ⁸²SeF₆ TPC neutrinoless double beta decay experiments*