Prerpints.org logo
Preprint
Article

Exoplanet Atmosphere Characterization via Transit Spectra Classification

Altmetrics

Downloads

79

Views

62

Comments

0

This version is not peer-reviewed

Atmospheric Chemistry, Aging, and Dynamics

Submitted:

17 August 2023

Posted:

18 August 2023

You are already at the latest version

Alerts
Abstract
This study focused on demonstrating the potential of classification algorithm in the chemical composition characterization of transiting exoplanets. The Python-based module PLATON 5.3 for forward modelling of transiting planet spectra was used to simulate a set of transmission spectra of an exoplanet with size Rp = 1.40*Rjupiter, and mass Mp = 0.73*Mjupiter orbiting around the host star of size Ms = 1.16*Msun and surface temperature of 1200 Kelvin. The gas composition of the exoplanet atmosphere was varied at low and high levels of 3-gas mix of CO2, O2, N2 and CH4 resulting to eight classes of spectra. The transit spectra were then used as input data to a forward neural network classifier with the eight gas composition classes as target outputs. The trained classifier achieved at most 97.9% overall accuracy.
Keywords: 
Subject: Environmental and Earth Sciences  -   Atmospheric Science and Meteorology

1. Introduction

As of February 2023, around 5040 exoplanets have been identified [1] but the characterization of exoplanet atmosphere is still a challenging task amid significant progress in improvement of telescopes and data analytics [2]. Only a handful of observed exoplanets have been characterized in terms of atmosphere composition [3], which is vital for various purposes including the search of habitable planets.
Among the various methods of exoplanet observation for atmosphere characterization, transmission spectroscopy is a common method used [4] specially when the orbit of the exoplanet aligns between the host star and the observing telescope (on Earth or around Earth). Typically, the data analysis task to be done given that an actual measurement has been made is by using calibration spectra to estimate from a measured transmission (absorption) spectra the amounts of the atmospheric chemicals. The data analytics approach study presented in this paper reformulates the data analysis problem by looking at it as a classification problem (Figure 1).
Posing the data analytics task as a classification problem requires two data components when training a classifier: (1) spectral data as input, and (2) label of the spectral data as output. This dataset structure can be created using known models about exoplanet atmosphere composition, and one platform that has been developed for simulation of transmission spectra of transiting exoplanet is the PLATON 5.3 [5], which is a Python-based module. With this data analytics workflow of simulating transmission spectra from randomized levels of gases (CO2, O2, N2 and CH4) via the PLATON 5.3 platform followed by the training of a forward neural network (NN) classifier (Figure 1), we show in this paper the potential of classification algorithm in characterizing the atmosphere composition of transiting exoplanet by using transit depth-versus-wavelength datasets.

2. Methodology

A schematic overview of the data analysis workflow is shown in Figure 1. This data analytics workflow leverages on the capability of transit-depth-versus-wavelength spectral data to capture the characteristic atmosphere fingerprint of the transiting exoplanet [4]. The datasets and Python codes in Jupyter Notebook files used in this study are provided online via the GitHub repository of the work [6].
The transit spectra datasets were simulated using the Python-based module PLATON 5.3 [5] (Figure 1a). The 3-gas mix combinations used in the simulation of spectra are summarized in Table 1. For the atmosphere composition simulations, the following star-planet parameters were used: an exoplanet with size Rp = 1.40*Rjupiter, and mass Mp = 0.73*Mjupiter orbiting around the host star of size Ms = 1.16*Msun and surface temperature of 1200 Kelvin. These parameter levels were the default settings in the PLATON 5.3 modules example simulation codes [5] and were kept the same in the work. The number of random simulations (n) in each class was varied at n = 10 and n = 100. A sample graphical rendering of the transit depth-versus-wavelength for the spectra Set I is shown in Figure 2.

3. Results

The performance of the trained forward neural network classifiers are shown as follows: Figure 3 for the confusion matrix, Figure 4 for the receiver operating characteristic (ROC) curve, and Table 2 for the summary of precision, recall, and F1-score for each class.

4. Discussion

Overall, the trained classifiers can achieve very good classification performance reaching 97.9% overall accuracy. The higher number of spectral data, which is n = 100 in this study, favors higher prediction accuracy. The levels of precision, recall, and F1-score of the trained classifiers also indicate low misclassification rates. Based on these results of training forward NN classifiers on the transit spectra generated via PLATON 5.3, we conclude that a classification algorithm can be a potential method of characterizing the atmosphere of transiting exoplanets.

Author Contributions

Conceptualization, D.L.B.F., A.T., A.M., and W.S.; methodology, D.L.B.F. and A.T.; software, D.L.B.F. and A.T.; formal analysis, D.L.B.F., A.T., A.M. and W.S.; writing, D.L.B.F., A.T., A.M. and W.S.; funding acquisition, D.L.B.F. and A.T. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Louisiana Space Grant Consortium (LaSPACE), Louisiana USA under their LURA program with sub-award number PO-0000206339 under the primary NASA grant 80NSSC20M0110. A.T. was the funded student and D.L.B.F. was the principal investigator.

Data Availability Statement

The datasets and Python codes as Jupyter notebook files are provided online via the GitHub repository of the project: https://github.com/dhanfort/TransitExoplanet_Spectra_Classif.git [6] .

Acknowledgments

We appreciate the continued support of LaSPACE to undergraduate students who want to pursue research in STEM and space-related projects.

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Appendix A

In the following equations, these notations are used: TP = True positive; FP = False positive; FN = False negative. F1-score is a measure of accuracy at each class.
A.1 Equation of Precision
P r e c i s i o n = T P T P + F P  
A.2 Equation of Recall
R e c a l l = T P T P + F N
A.3 Equation of F1-Score
F 1   S c o r e = 2 × T P 2 × T P + F P + F N

References

  1. Rein, H. A proposal for community driven and decentralized astronomical databases and the Open Exoplanet Catalogue. arXiv 2012, arXiv:1211.7121. [Google Scholar] [CrossRef]
  2. Charbonneau, D.; Brown, T.M.; Noyes, R.W.; Gilliland, R.L. Detection of an Extrasolar Planet Atmosphere*. The Astrophysical Journal 2002, 568, 377. [Google Scholar] [CrossRef]
  3. Rukdee, S. Ultra-high resolution spectroscopy from ground and space for exoplanet atmosphere characterization. In Proceedings of the 2nd Innovation Aviation & Aerospace Industry - International Conference 2021, Chiang Mai, Thailand; 2021. [Google Scholar]
  4. Kreidberg, L. Exoplanet Atmosphere Measurements from Transmission Spectroscopy and other Planet-Star Combined Light Observations. arXiv 2017, arXiv:1709.05941. [Google Scholar]
  5. Michael Zhang; Yayaati Chachan; Eliza M.-R. Kempton; Heather Knutson; Chang, W. PLATON II: New Capabilities And A Comprehensive Retrieval on HD 189733b Transit and Eclipse Data. arXiv 2004, arXiv:2004.09513.
  6. Fortela, D.L.B. GitHub repo: Transiting Exoplanet Spectra Classification. Available online: https://github.com/dhanfort/TransitExoplanet_Spectra_Classif.git (accessed on 17 August 2023).
Preprints 82726 g001
Figure 1. Schematic overview of the data analytics workflow implemented in this study. a) simulation of transit spectra using the PLATON 5.3 module, b) training a forward NN classifier.
Figure 1. Schematic overview of the data analytics workflow implemented in this study. a) simulation of transit spectra using the PLATON 5.3 module, b) training a forward NN classifier.
Preprints 82726 g001
Preprints 82726 g002
Figure 2. Graphical rendering of sample transit spectra for the Set I of experiments. One spectra sample was taken from each class.
Figure 2. Graphical rendering of sample transit spectra for the Set I of experiments. One spectra sample was taken from each class.
Preprints 82726 g002
Preprints 82726 g003
Figure 3. Multi-class confusion matrix for all the classifier models for varied gas-mix experiments at varying number of random samples in each class n=10 and n=100. a) Set I for CO2/CH4/O2 mix, b) Set II for CO2/CH4/N2 mix, and c) Set III for CO2/O2/N2 mix.
Figure 3. Multi-class confusion matrix for all the classifier models for varied gas-mix experiments at varying number of random samples in each class n=10 and n=100. a) Set I for CO2/CH4/O2 mix, b) Set II for CO2/CH4/N2 mix, and c) Set III for CO2/O2/N2 mix.
Preprints 82726 g003
Preprints 82726 g004
Figure 4. One-versus-rest ROC curves for all the classifier models for varied gas-mix experiments at varying number of random samples in each class n=10 and n=100. a) Set I for CO2/CH4/O2 mix, b) Set II for CO2/CH4/N2 mix, and c) Set III for CO2/O2/N2 mix. The highest posisble area under the curve (AUC) value is 1.0 indicating good discrimination performance of the classifier favoring very high true positive rate and very low false positive rate.
Figure 4. One-versus-rest ROC curves for all the classifier models for varied gas-mix experiments at varying number of random samples in each class n=10 and n=100. a) Set I for CO2/CH4/O2 mix, b) Set II for CO2/CH4/N2 mix, and c) Set III for CO2/O2/N2 mix. The highest posisble area under the curve (AUC) value is 1.0 indicating good discrimination performance of the classifier favoring very high true positive rate and very low false positive rate.
Preprints 82726 g004
Table 1. Gas mix simulated in the three spectra sets used in the study.
Table 1. Gas mix simulated in the three spectra sets used in the study.
Spectra Set Gas Component 1 Gas Component 2 Gas Component 3
I CO2 CH4 O2
II CO2 CH4 N2
III CO2 O2 N2
Table 2. Summary of classification performance of the forward NN classifier for sets at n=100.
Table 2. Summary of classification performance of the forward NN classifier for sets at n=100.
Set I: CO2/CH4/O2; n =100
Spectra Class Precision Recall F1-score Support
1 0.942308 0.98 0.960784 100
2 0.933333 0.98 0.956098 100
3 0.96 0.96 0.96 100
4 0.959596 0.95 0.954774 100
5 0.950495 0.96 0.955224 100
6 0.959596 0.95 0.954774 100
7 0.969388 0.95 0.959596 100
8 0.989362 0.93 0.958763 100
Overall Accuracy = 0.9575
Set II: CO2/CH4/N2; n =100
Spectra Class Precision Recall F1-score Support
1 0.961165 0.99 0.975369 100
2 0.979798 0.97 0.974874 100
3 0.970588 0.99 0.980198 100
4 0.980198 0.99 0.985075 100
5 0.970874 1 0.985222 100
6 1 0.96 0.979592 100
7 0.970297 0.98 0.975124 100
8 1 0.95 0.974359 100
Overall Accuracy = 0.97875
Set III: CO2/O2/N2; n =100
Spectra Class Precision Recall F1-score Support
1 0.951456 0.98 0.965517 100
2 0.932039 0.96 0.945813 100
3 0.850877 0.97 0.906542 100
4 0.904762 0.95 0.926829 100
5 0.942308 0.98 0.960784 100
6 0.968421 0.92 0.94359 100
7 0.988636 0.87 0.925532 100
8 0.988636 0.87 0.925532 100
Overall Accuracy = 0.9375
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
Copyright: This open access article is published under a Creative Commons CC BY 4.0 license, which permit the free download, distribution, and reuse, provided that the author and preprint are cited in any reuse.
Prerpints.org logo

Preprints.org is a free preprint server supported by MDPI in Basel, Switzerland.

facebook logotwitter logolinkedin logo
MDPI Initiatives
Important Links
Subscribe

Disclaimer

Terms of Use

Privacy Policy

© 2024 MDPI (Basel, Switzerland) unless otherwise stated