Intelligent and automatic wavelength calibration method

Song, Qing; Zhang, Tian; Wang, Zhihui; Zhang, Shihui; Lan, Yang

doi:10.1364/ao.57.006876

Cited by 6 publications

(6 citation statements)

References 18 publications

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“…Users are only required to supply a list of calibration lines (wavelengths), a list of peaks (pixels), and some knowledge (e.g., minimum and maximum wavelength limits) of the system to obtain the most appropriate pixel-to-wavelength solution. RASCAL builds on the work by Song et al (2018), which searches only for plausible sets of arc line/peak correspondences that follow a linear approximation. RANSAC (Fischler & Bolles 1981) is used to fit a higher-order polynomial model to the candidate correspondences robustly.…”

Section: Arc Fitting For the Pixel-wavelength Relation (Amentioning

confidence: 99%

Automated SpectroPhotometric Image REDuction (ASPIRED)

Lam

Smith²,

Steele³

2023

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We provide a suite of public open-source spectral data-reduction software to rapidly obtain scientific products from all forms of long-slit-like spectroscopic observations. Automated SpectroPhotometric REDuction (ASPIRED) is a Python-based spectral data-reduction toolkit. It is designed to be a general toolkit with high flexibility for users to refine and optimize their data-reduction routines for the individual characteristics of their instruments. The default configuration is suitable for low-resolution long-slit spectrometers and provides a quick-look quality output. However, for repeatable science-ready reduced spectral data, some moderate one-time effort is necessary to modify the configuration. Fine-tuning and additional (pre)processing may be required to extend the reduction to systems with more complex setups. It is important to emphasize that although only a few parameters need updating, ensuring their correctness and suitability for generalization to the instrument can take time due to factors such as instrument stability. We compare some example spectra reduced with ASPIRED to published data processed with iraf-based and STARLINK-based pipelines, and find no loss in the quality of the final product. The Python-based, iraf-free ASPIRED can significantly ease the effort of an astronomer in constructing their own data-reduction workflow, enabling simpler solutions to data-reduction automation. This availability of near-real-time, science-ready data will allow adaptive observing strategies, particularly important in, but not limited to, time-domain astronomy.

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Section: Arc Fitting For the Pixel-wavelength Relation (Amentioning

confidence: 99%

Automated SpectroPhotometric Image REDuction (ASPIRED)

Lam

Smith²,

Steele³

2023

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“…), a software package developed in conjunction but independently of ASPIRED. It is improved on the work by Song et al (2018), which searches only for plausible sets of arc lines that follow good linear approximations to the system. RASCAL considers the top 𝑁 sets simultaneously.…”

Section: Wavelength Calibration (Not Part Of This Work)mentioning

confidence: 99%

Automated SpectroPhotometric Image REDuction (ASPIRED) -- A Python-based spectral data reduction toolkit

Lam¹,

Smith²,

Arcavi³

et al. 2021

Preprint

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We provide a suite of publicly available spectral data reduction software to facilitate rapid scientific products from time-domain observations. The Automated SpectroPhotometric REDuction (ASPIRED) toolkit is aimed to be as general as possible with high flexibility such that it can work with different instruments. The default settings support typical low-resolution long-slit spectrometer configurations, whilst it also offers a flexible set of functions for users to refine and tailor-make automated pipelines to an instrument's individual characteristics. Automation can provide immediate data reduction to allow adaptive observing strategies, which is particularly important in Time Domain Astronomy. ASPIRED is entirely P -based and is independent of .

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“…Using a more accurate fitting formula and adopting automatic programs, the effect and time cost of wavelength calibration were optimized. − Xue et al used a genetic algorithm and a particle swarm optimization (PSO) algorithm to calibrate the visible and infrared spectra of Large Sky Area Multi-Object Fiber Spectroscopy Telescope (LAMOST) and Sloan Digital Sky Survey (SDSS). , Compared with the traditional enumeration method, these two algorithms improved the accuracy, convergence, and time consumption. Yang et al proposed intelligent and automatic wavelength calibration methods such as maximum linear matching and random sample consensus matching, with which efficient and accurate spectral line corrections can be carried out automatically. , The above-mentioned methods utilize the spectral lines of standard element lamps to calibrate wavelengths. They are not suitable for LIBS spectrometers’ calibration in deep space exploration because it is difficult to carry on-board element lamps that cover the wide spectral range of LIBS.…”

Section: Introductionmentioning

confidence: 99%

“…Yang et al proposed intelligent and automatic wavelength calibration methods such as maximum linear matching and random sample consensus matching, with which efficient and accurate spectral line corrections can be carried out automatically. 19,20 The above-mentioned methods utilize the spectral lines of standard element lamps to calibrate wavelengths. They are not suitable for LIBS spectrometers' calibration in deep space exploration because it is difficult to carry on-board element lamps that cover the wide spectral range of LIBS.…”

Section: ■ Introductionmentioning

confidence: 99%

Elastic Particle Swarm Optimization for MarSCoDe Spectral Calibration on Tianwen-1 Mars Rover

et al. 2021

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China's Tianwen-1 Mars rover carries a laser-induced breakdown spectroscopy (LIBS) payload named MarSCoDe to analyze the mineral and rock composition on Mars. MarSCoDe is expected to experience a wide working temperature range of about 100 °C, which will lead to a spectral shift of up to ∼40 pixels (∼8.13 nm). Even worse, drastic changes in temperature and environment may cause a loss or increase of some spectral lines of an on-board calibration Ti target. An elastic particle swarm optimization (PSO) approach is proposed to fulfill the on-board spectral calibration of MarSCoDe under this harsh condition. Through establishing a standard wavelength set (SWS) and an individual particle wavelength set (PWS), and further elastically selecting a part of PWS to compare with SWS, the problem of spectral shift and number mismatch can be solved gradually with the evolution of the particle swarm. Some tests of standard lamps and Ti with MarSCoDe, placed in a Mars simulation environment chamber (MSEC) in a temperature range of 70 °C, were completed. Compared with the standard spectrum of the Ti target (obtained at 20 °C), the spectral shifts of the first, second, and third channels are approximately 0.33 nm (5 pixels), 0.85 nm (6.4 pixels), and 8.09 nm (39.8 pixels), respectively, at −40 °C before correction; after PSO correction, the spectral shifts are greatly reduced to up to 0.015 nm, and specially for the 626.28 nm line, the spectral shift is reduced from 8.09 nm to about 0 nm. Experimental results demonstrate that the PSO-based approach can not only correct the on-board spectral shift but also solve the number mismatch of spectral lines of MarSCoDe in the harsh working environment of Mars. Further, it can be extended to the on-board calibration of other spectral payloads for deep space exploration.

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Intelligent and automatic wavelength calibration method

Cited by 6 publications

References 18 publications

Automated SpectroPhotometric Image REDuction (ASPIRED)

Automated SpectroPhotometric Image REDuction (ASPIRED)

Automated SpectroPhotometric Image REDuction (ASPIRED) -- A Python-based spectral data reduction toolkit

Elastic Particle Swarm Optimization for MarSCoDe Spectral Calibration on Tianwen-1 Mars Rover

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