The Richardson-Lucy deconvolution method to extract LAMOST 1D spectra

Li, Min; Li, Guangwei; Lü, Ke; Duan, Fuqing; Haerken, Hasitieer; Zhao, Yi

doi:10.1088/1674-4527/19/10/145

Cited by 7 publications

(3 citation statements)

References 12 publications

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“…Richardson-Lucy direct demodulation (LucyDDM) [32] with a non-linear iteration to calculate deconvolution has a wide application in astronomy [33]…”

Section: Richardson-lucy Direct Demodulationmentioning

confidence: 99%

Towards the ultimate PMT waveform analysis for neutrino and dark matter experiments

Xu¹,

Xu²,

Bao³

et al. 2022

J. Inst.

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Photomultiplier tube (PMT) voltage waveforms are the raw data of many neutrino and dark matter experiments. Waveform analysis is the cornerstone of data processing. We evaluate the performance of all the waveform analysis algorithms known to us and find fast stochastic matching pursuit the best in accuracy. Significant time (up to × 2) and energy (up to × 1.07) resolution boosts are attainable with fast stochastic matching pursuit, approaching theoretical limits. Other methods also outperform the traditional threshold crossing approach in time resolution.

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“…Richardson-Lucy direct demodulation (LucyDDM) [32] with a non-linear iteration to calculate deconvolution has a wide application in astronomy [33]…”

Section: Richardson-lucy Direct Demodulationmentioning

confidence: 99%

Towards the ultimate PMT waveform analysis for neutrino and dark matter experiments

Xu¹,

Xu²,

Bao³

et al. 2022

J. Inst.

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show abstract

“…Richardson-Lucy direct demodulation (LucyDDM) [15] with a non-linear iteration to calculate deconvolution has a wide application in astronomy [16] and image processing. We view V PE * (t − s) as a conditional probability distribution p(t|s) where t denotes PMT amplified electron time, and s represents the given PE time.…”

Section: Richardson-lucy Direct Demodulationmentioning

confidence: 99%

Towards the ultimate PMT waveform analysis for neutrino and dark matter experiments

Xu,

Bao

et al. 2021

Preprint

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Photomultiplier tube (PMT) voltage waveforms are the raw data of many neutrino and dark matter experiments. Waveform analysis is the cornerstone of data processing. We evaluate the performance of all the waveform analysis algorithms known to us and find fast stochastic matching pursuit the best in accuracy. Significant time (up to ×2) and energy (up to ×1.04) resolution boosts are attainable with fast stochastic matching pursuit, approaching theoretical limits. Other methods also outperform the traditional threshold crossing approach in time resolution.

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“…The Richardson-Lucy (R-L) algorithm was initially proposed by Richardson based on the Bayes formula [12]. It was further developed by Lucy using the Lagrange multiplier method for the log-likelihood function maximization problem [13], which has been used in various domains, including image processing and astronomy [14,15]. However, noise amplification and ringing effects will occur in the iterative process of the algorithm, and the convergence speed is slow [16].…”

Section: Introductionmentioning

confidence: 99%

Research on a deconvolution algorithm for laser-induced fluorescence diagnosis based on the maximum entropy principle

LEI 雷,

YANG 杨,

CHENG 程

et al. 2024

Plasma Sci. Technol.

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Laser-induced fluorescence (LIF) spectroscopy is employed for plasma diagnosis, necessitating the utilization of deconvolution algorithms to isolate the Doppler effect from the raw spectral signal. However, direct deconvolution becomes invalid in the presence of noise as it leads to infinite amplification of high-frequency noise components. To address this issue, we propose a deconvolution algorithm based on the maximum entropy principle. We validate the effectiveness of the proposed algorithm by utilizing simulated LIF spectra at various noise levels (signal-to-noise ratio, SNR = 20–80 dB) and measured LIF spectra with Xe as the working fluid. In the typical measured spectrum (SNR = 26.23 dB) experiment, compared with the Gaussian filter and the Richardson-Lucy (R-L) algorithm, the proposed algorithm demonstrates an increase in SNR by 1.39 dB and 4.66 dB respectively, along with a reduction in root-mean-square error (RMSE) by 35% and 64% respectively. Additionally, there is a decrease in spectral angle (SA) by 0.05 and 0.11 respectively. In the high quality spectrum (SNR = 43.96 dB) experiment, the results show that the running time of the proposed algorithm is reduced by about 98% compared with the R-L iterative algorithm. Moreover, the maximum entropy algorithm avoids parameter optimization settings and is more suitable for automatic implementation. In conclusion, the proposed algorithm can accurately resolve Doppler spectrum details while effectively suppressing noise, thus highlighting its advantage in LIF spectral deconvolution applications.

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The Richardson-Lucy deconvolution method to extract LAMOST 1D spectra

Cited by 7 publications

References 12 publications

Towards the ultimate PMT waveform analysis for neutrino and dark matter experiments

Towards the ultimate PMT waveform analysis for neutrino and dark matter experiments

Towards the ultimate PMT waveform analysis for neutrino and dark matter experiments

Research on a deconvolution algorithm for laser-induced fluorescence diagnosis based on the maximum entropy principle

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