Fitting of dust spectra with genetic algorithms

Baier, A.; Kerschbaum, F.; Lebzelter, T.

doi:10.1051/0004-6361/200913968

Cited by 7 publications

(8 citation statements)

References 26 publications

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“…Genetic algorithms are often applied to optimize noisy objective functions (Metcalfe et al 2000;Larsen & Humphreys 2003). During the past decade, genetic algorithms have become increasingly more popular in numerous applications in astronomy and astrophysics ranging from cosmology and gravitational lens modeling to stellar structure and spectral fitting (Charbonneau 1995;Metcalfe et al 2000;Theis & Kohle 2001;Larsen & Humphreys 2003;Fletcher et al 2003;Liesenborgs et al 2006;Baier et al 2010;Schechtman-Rook et al 2012;Rajpaul 2012a). For a recent overview of the use of genetic algorithms in astronomy and astrophysics, see Rajpaul (2012b).…”

Section: Introductionmentioning

confidence: 99%

FitSKIRT: genetic algorithms to automatically fit dusty galaxies with a Monte Carlo radiative transfer code

Geyter

Baes

Fritz

et al. 2013

A&A

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We present FitSKIRT, a method to efficiently fit radiative transfer models to UV/optical images of dusty galaxies. These images have the advantage that they have better spatial resolution compared to FIR/submm data. FitSKIRT uses the GAlib genetic algorithm library to optimize the output of the SKIRT Monte Carlo radiative transfer code. Genetic algorithms prove to be a valuable tool in handling the multi-dimensional search space as well as the noise induced by the random nature of the Monte Carlo radiative transfer code. FitSKIRT is tested on artificial images of a simulated edge-on spiral galaxy, where we gradually increase the number of fitted parameters. We find that we can recover all model parameters, even if all 11 model parameters are left unconstrained. Finally, we apply the FitSKIRT code to a V-band image of the edge-on spiral galaxy NGC 4013. This galaxy has been modeled previously by other authors using different combinations of radiative transfer codes and optimization methods. Given the different models and techniques and the complexity and degeneracies in the parameter space, we find reasonable agreement between the different models. We conclude that the FitSKIRT method allows comparison between different models and geometries in a quantitative manner and minimizes the need of human intervention and biasing. The high level of automation makes it an ideal tool to use on larger sets of observed data.

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Section: Introductionmentioning

confidence: 99%

FitSKIRT: genetic algorithms to automatically fit dusty galaxies with a Monte Carlo radiative transfer code

Geyter

Baes

Fritz

et al. 2013

A&A

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“…The crossover rate is defined as 80% in Pyevolve and provides good solutions for many problems in the literature (e.g. Peng et al 2003;Baier et al 2010). However, a crossover rate of 90% provided better results in the present study when the tournament selection method was adopted.…”

Section: Genetic Algorithm Modulementioning

confidence: 99%

“…As such, it has been used to solve complex problems in astrophysics such as the huge degeneracy behind the physical parameters of protoplanetary discs and the silicate composition in the mid-IR (e.g. Charbonneau 1995;Hetem & Gregorio-Hetem 2007;Baier et al 2010;Woitke et al 2019).…”

Section: Genetic Algorithm Modulementioning

confidence: 99%

“…The code uses a genetic modelling algorithm (Holland 1975) to decompose IR spectra of protostars using a linear combination of laboratory data themselves. Evolutionary algorithms have successfully been used in the literature to derive the dust composition of asymptotic giant branch (AGB) stars (Baier et al 2010), and to derive the physical properties of protostars (Woitke et al 2016). This paper is laid out as follows: Section 2 lists the public databases containing ice spectra used in this work and Section 3 details the code methodology.…”

Section: Introductionmentioning

confidence: 99%

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Fitting infrared ice spectra with genetic modelling algorithms

Rocha

Perotti

Kristensen

et al. 2021

A&A

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Context. A variety of laboratory ice spectra simulating different chemical environments, ice morphologies, and thermal and energetic processing are needed in order to provide an accurate interpretation of the infrared spectra of protostars. To decipher the combination of laboratory data that best fits the observations, an automated, statistics-based computational approach is necessary. Aims. We aim to introduce a new approach, based on evolutionary algorithms, to searching for molecules in ice mantles via spectral decomposition of infrared observational data with laboratory ice spectra. Methods. We introduce a publicly available and open-source fitting tool called ENIIGMA (dEcompositioN of Infrared Ice features using Genetic Modelling Algorithms). The tool has dedicated Python functions to carry out continuum determination of the protostellar spectra, silicate extraction, spectral decomposition, and statistical analysis to calculate confidence intervals and quantify degeneracy. We conducted fully blind and non-blind tests with known ice samples and constructed mixtures in order to asses the code. Additionally, we performed a complete analysis of the Elias 29 spectrum and compared our findings with previous results from the literature.Results. The ENIIGMA fitting tool can identify the correct ice samples and their fractions in all checks with known samples tested in this paper. In the cases where Gaussian noise was added to the experimental data, more robust genetic operators and more iterations became necessary. Concerning the Elias 29 spectrum, the broad spectral range between 2.5 and 20 µm was successfully decomposed after continuum determination and silicate extraction. This analysis allowed the identification of different molecules in the ice mantle, including a tentative detection of CH 3 CH 2 OH. Conclusions. The ENIIGMA is a toolbox for spectroscopy analysis of infrared spectra that is well-timed with the launch of the James Webb Space Telescope. Additionally, it allows different chemical environments and irradiation fields to be explored, allowing the user to correctly interpret astronomical observations.

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“…The code uses genetic modelling algorithm (Holland 1975) to decompose IR spectra of protostars using a linear combination of laboratory data themselves. The use of evolutionary algorithms have successfully been used in the literature to derive the dust composition in asymptotic giant branch (AGB) stars (Baier et al 2010), as well as to derive physical properties of protostars (Woitke et al 2016). This paper is laid out as follows: Section 2 list the public databases containing ice spectra used in this work and Section 3 details the code methodology.…”

Section: Introductionmentioning

confidence: 99%

Fitting infrared ice spectra with genetic modelling algorithms. Presenting the ENIIGMA fitting tool

Rocha,

Perotti,

Kristensen

et al. 2021

Preprint

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Context. A variety of laboratory ice spectra simulating different chemical environments, ice morphology as well as thermal and energetic processing are demanded to provide an accurate interpretation of the infrared spectra of protostars. To answer which combination of laboratory data best fit the observations, an automated statistically-based computational approach becomes necessary. Aims. To introduce a new approach, based on evolutionary algorithms, to search for molecules in ice mantles via spectral decomposition of infrared observational data with laboratory ice spectra. Methods. A publicly available and open-source fitting tool, called ENIIGMA (dEcompositioN of Infrared Ice features using Genetic Modelling Algorithms), is introduced. The tool has dedicated Python functions to carry out continuum determination of the protostellar spectra, silicate extraction, spectral decomposition and statistical analysis to calculate confidence intervals and quantify degeneracy. As assessment of the code, fully blind and fully sighted tests were conducted with known ice samples and constructed mixtures. Additionally, a complete analysis of the Elias 29 spectrum was performed and compared with previous results in the literature. Results. The ENIIGMA fitting tool can identify the correct ice samples and their fractions in all checks with known samples tested in this paper. In the cases where Gaussian noise was added to the experimental data, more robust genetic operators and more iterations became necessary. Concerning the Elias 29 spectrum, the broad spectral range between 2.5−20 µm was successfully decomposed after continuum determination and silicate extraction. This analysis allowed the identification of different molecules in the ice mantle, including a tentative detection of CH 3 CH 2 OH. Conclusions. The ENIIGMA is a toolbox for spectroscopy analysis of infrared spectra that is well-timed with the launch of the James Webb Space Telescope. Additionally, it allows for exploring different chemical environment and irradiation field in order to correctly interpret the astronomical observations.

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Fitting of dust spectra with genetic algorithms

Cited by 7 publications

References 26 publications

FitSKIRT: genetic algorithms to automatically fit dusty galaxies with a Monte Carlo radiative transfer code

FitSKIRT: genetic algorithms to automatically fit dusty galaxies with a Monte Carlo radiative transfer code

Fitting infrared ice spectra with genetic modelling algorithms

Fitting infrared ice spectra with genetic modelling algorithms. Presenting the ENIIGMA fitting tool

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