An unfolding method based on conditional Invertible Neural Networks (cINN) using iterative training
M Backes, A Butter, M Dunford, B Malaescu - SciPost Physics Core, 2024 - scipost.org
The unfolding of detector effects is crucial for the comparison of data to theory predictions.
While traditional methods are limited to representing the data in a low number of …
While traditional methods are limited to representing the data in a low number of …
Event-by-event comparison between machine-learning-and transfer-matrix-based unfolding methods
M Backes, A Butter, M Dunford, B Malaescu - The European Physical …, 2024 - Springer
The unfolding of detector effects is a key aspect of comparing experimental data with
theoretical predictions. In recent years, different Machine-Learning methods have been …
theoretical predictions. In recent years, different Machine-Learning methods have been …
Application of an LSTM model based on deep learning through X-ray fluorescence spectroscopy
T Lin, LI Yong, T Yufeng, LIU Ze, LIU Bingqi - Nuclear Techniques, 2023 - hjs.sinap.ac.cn
BackgroundTraditional X-ray fluorescence spectrum analysis has the limitations of poor
accuracy of the characteristic peak counting rate and shadow peak. PurposeThis study aims …
accuracy of the characteristic peak counting rate and shadow peak. PurposeThis study aims …
Deep learning-based pulse height estimation for separation of pile-up pulses from NaI (Tl) detector
Measured spectra in a high count rate environment are difficult to analyze because of the
spectral distortions caused by the pulse pile-up effect. This study proposes a deep learning …
spectral distortions caused by the pulse pile-up effect. This study proposes a deep learning …
Trapezoidal pile-up nuclear pulse parameter identification method based on deep learning transformer model
Q Wang, H Huang, X Ma, Z Shen, C Zhong… - Applied Radiation and …, 2022 - Elsevier
Pile-up between adjacent nuclear pulses is unavoidable in the actual detection process.
Some scholars have tried to apply deep learning techniques to identify pile-up nuclear pulse …
Some scholars have tried to apply deep learning techniques to identify pile-up nuclear pulse …
[HTML][HTML] Trajectory determination at Muon Impact Tracer and Observer (MITO) using artificial neural networks
We propose a method for the determination of the impact point of muons in each of the two
detection planes of the Muon Impact Tracer and Observer (MITO) telescope, which is part of …
detection planes of the Muon Impact Tracer and Observer (MITO) telescope, which is part of …
Three topologies of deep neural networks for pulse height extraction
Pulse shaping is a common technique for optimizing signal-to-noise ratio (SNR) in particle
detectors. Although analog or digital linear shapers are typically used for this purpose, there …
detectors. Although analog or digital linear shapers are typically used for this purpose, there …
Feed-forward neural network unfolding
ML Wong, A Edmonds, C Wu - arXiv preprint arXiv:2112.08180, 2021 - arxiv.org
A feed-forward neural network is demonstrated to efficiently unfold the energy distribution of
protons and alpha particles passing through passive material. This model-independent …
protons and alpha particles passing through passive material. This model-independent …
Application of a neural network model with multimodal fusion for fluorescence spectroscopy
L Tang, S Zhou, KB Shi, HT Shen, L You - Nuclear Science and …, 2024 - Springer
In energy-dispersive X-ray fluorescence spectroscopy, the estimation of the pulse amplitude
determines the accuracy of the spectrum measurement. The error generated by the …
determines the accuracy of the spectrum measurement. The error generated by the …
Transformer algorithm for pile-up correction in synchrotron radiation spectroscopic detection experiments
S Wang, N Pan, S Gu, Y Wang, Y Huang - Nuclear Instruments and …, 2025 - Elsevier
In this study, a Transformer-based algorithm is proposed for pulse pile-up correction in
synchrotron radiation spectroscopy detection experiments. The Transformer model …
synchrotron radiation spectroscopy detection experiments. The Transformer model …