diff --git a/joss.06346/10.21105.joss.06346.crossref.xml b/joss.06346/10.21105.joss.06346.crossref.xml new file mode 100644 index 0000000000..5d509fa23e --- /dev/null +++ b/joss.06346/10.21105.joss.06346.crossref.xml @@ -0,0 +1,602 @@ + + + + 20240718234024-9422a33353cc0e3b1ed4567df8ac8fc285a37baf + 20240718234024 + + JOSS Admin + admin@theoj.org + + The Open Journal + + + + + Journal of Open Source Software + JOSS + 2475-9066 + + 10.21105/joss + https://joss.theoj.org + + + + + 07 + 2024 + + + 9 + + 99 + + + + TelescopeML – I. An End-to-End Python Package for +Interpreting Telescope Datasets through Training Machine Learning +Models, Generating Statistical Reports, and Visualizing Results + + + + Ehsan (Sam) + Gharib-Nezhad + https://orcid.org/0000-0002-4088-7262 + + + Natasha E. + Batalha + https://orcid.org/0000-0003-1240-6844 + + + Hamed + Valizadegan + https://orcid.org/0000-0001-6732-0840 + + + Miguel J. S. + Martinho + https://orcid.org/0000-0002-2188-0807 + + + Mahdi + Habibi + https://orcid.org/0000-0001-8530-7746 + + + Gopal + Nookula + + + + 07 + 18 + 2024 + + + 6346 + + + 10.21105/joss.06346 + + + http://creativecommons.org/licenses/by/4.0/ + http://creativecommons.org/licenses/by/4.0/ + http://creativecommons.org/licenses/by/4.0/ + + + + Software archive + 10.5281/zenodo.11553655 + + + GitHub review issue + https://github.com/openjournals/joss-reviews/issues/6346 + + + + 10.21105/joss.06346 + https://joss.theoj.org/papers/10.21105/joss.06346 + + + https://joss.theoj.org/papers/10.21105/joss.06346.pdf + + + + + + 1D convolutional neural networks and +applications: A survey + Kiranyaz + Mechanical Systems and Signal +Processing + 151 + 10.1016/j.ymssp.2020.107398 + 0888-3270 + 2021 + Kiranyaz, S., Avci, O., Abdeljaber, +O., Ince, T., Gabbouj, M., & Inman, D. J. (2021). 1D convolutional +neural networks and applications: A survey. Mechanical Systems and +Signal Processing, 151, 107398. +https://doi.org/10.1016/j.ymssp.2020.107398 + + + Deep learning + Goodfellow + 2016 + Goodfellow, I., Bengio, Y., & +Courville, A. (2016). Deep learning. MIT Press. + + + ExoGAN: Retrieving Exoplanetary Atmospheres +Using Deep Convolutional Generative Adversarial Networks + Zingales + The Astronomical Journal + 6 + 156 + 10.3847/1538-3881/aae77c + 2018 + Zingales, T., & Waldmann, I. P. +(2018). ExoGAN: Retrieving Exoplanetary Atmospheres Using Deep +Convolutional Generative Adversarial Networks. The Astronomical Journal, +156(6), 268. +https://doi.org/10.3847/1538-3881/aae77c + + + The JWST Early-release Science Program for +Direct Observations of Exoplanetary Systems II: A 1 to 20 \mum Spectrum +of the Planetary-mass Companion VHS 1256-1257 b + Miles + The Astrophysical Journal +Letters + 1 + 946 + 10.3847/2041-8213/acb04a + 2023 + Miles, B. E., Biller, B. A., Patapis, +P., Worthen, K., Rickman, E., Hoch, K. K. W., Skemer, A., Perrin, M. D., +Whiteford, N., Chen, C. H., Sargent, B., Mukherjee, S., Morley, C. V., +Moran, S. E., Bonnefoy, M., Petrus, S., Carter, A. L., Choquet, E., +Hinkley, S., … Zhang, Z. (2023). The JWST Early-release Science Program +for Direct Observations of Exoplanetary Systems II: A 1 to 20 \mum +Spectrum of the Planetary-mass Companion VHS 1256-1257 b. The +Astrophysical Journal Letters, 946(1), L6. +https://doi.org/10.3847/2041-8213/acb04a + + + Early Release Science of the exoplanet +WASP-39b with JWST NIRSpec G395H + Alderson + Nature + 7949 + 614 + 10.1038/s41586-022-05591-3 + 2023 + Alderson, L., Wakeford, H. R., Alam, +M. K., Batalha, N. E., Lothringer, J. D., Adams Redai, J., Barat, S., +Brande, J., Damiano, M., Daylan, T., Espinoza, N., Flagg, L., Goyal, J. +M., Grant, D., Hu, R., Inglis, J., Lee, E. K. H., Mikal-Evans, T., +Ramos-Rosado, L., … Zhang, X. (2023). Early Release Science of the +exoplanet WASP-39b with JWST NIRSpec G395H. Nature, 614(7949), 664–669. +https://doi.org/10.1038/s41586-022-05591-3 + + + The James Webb Space Telescope +Mission + Gardner + Publications of the Astronomical Society of +the Pacific + 1048 + 135 + 10.1088/1538-3873/acd1b5 + 2023 + Gardner, J. P., Mather, J. C., +Abbott, R., Abell, J. S., Abernathy, M., Abney, F. E., Abraham, J. G., +Abraham, R., Abul-Huda, Y. M., Acton, S., & al., et. (2023). The +James Webb Space Telescope Mission. Publications of the Astronomical +Society of the Pacific, 135(1048), 068001. +https://doi.org/10.1088/1538-3873/acd1b5 + + + SpectRes: A Fast Spectral Resampling Tool in +Python + Carnall + arXiv e-prints + 10.48550/arXiv.1705.05165 + 2017 + Carnall, A. C. (2017). SpectRes: A +Fast Spectral Resampling Tool in Python. arXiv e-Prints, +arXiv:1705.05165. +https://doi.org/10.48550/arXiv.1705.05165 + + + SciPy 1.0: Fundamental Algorithms for +Scientific Computing in Python + Virtanen + Nature Methods + 17 + 10.1038/s41592-019-0686-2 + 2020 + Virtanen, P., Gommers, R., Oliphant, +T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, +P., Weckesser, W., Bright, J., van der Walt, S. J., Brett, M., Wilson, +J., Millman, K. J., Mayorov, N., Nelson, A. R. J., Jones, E., Kern, R., +Larson, E., … SciPy 1.0 Contributors. (2020). SciPy 1.0: Fundamental +Algorithms for Scientific Computing in Python. Nature Methods, 17, +261–272. +https://doi.org/10.1038/s41592-019-0686-2 + + + Bokeh: Python library for interactive +visualization + Bokeh Development Team + 2018 + Bokeh Development Team. (2018). +Bokeh: Python library for interactive visualization. +https://bokeh.pydata.org/en/latest/ + + + Seaborn: Statistical data +visualization + Waskom + Journal of Open Source +Software + 60 + 6 + 10.21105/joss.03021 + 2021 + Waskom, M. L. (2021). Seaborn: +Statistical data visualization. Journal of Open Source Software, 6(60), +3021. https://doi.org/10.21105/joss.03021 + + + Matplotlib: A 2D graphics +environment + Hunter + Computing in Science & +Engineering + 3 + 9 + 10.1109/MCSE.2007.55 + 2007 + Hunter, J. D. (2007). Matplotlib: A +2D graphics environment. Computing in Science & Engineering, 9(3), +90–95. https://doi.org/10.1109/MCSE.2007.55 + + + Array programming with NumPy + Harris + Nature + 7825 + 585 + 10.1038/s41586-020-2649-2 + 2020 + Harris, C. R., Millman, K. J., Walt, +S. J. van der, Gommers, R., Virtanen, P., Cournapeau, D., Wieser, E., +Taylor, J., Berg, S., Smith, N. J., Kern, R., Picus, M., Hoyer, S., +Kerkwijk, M. H. van, Brett, M., Haldane, A., Río, J. F. del, Wiebe, M., +Peterson, P., … Oliphant, T. E. (2020). Array programming with NumPy. +Nature, 585(7825), 357–362. +https://doi.org/10.1038/s41586-020-2649-2 + + + Pandas-dev/pandas: pandas + The pandas development team + 10.5281/zenodo.3509134 + 2020 + The pandas development team. (2020). +Pandas-dev/pandas: pandas (latest). Zenodo. +https://doi.org/10.5281/zenodo.3509134 + + + The Astropy Project: Sustaining and Growing a +Community-oriented Open-source Project and the Latest Major Release +(v5.0) of the Core Package + Astropy Collaboration + The Astrophysical Journal + 2 + 935 + 10.3847/1538-4357/ac7c74 + 2022 + Astropy Collaboration, Price-Whelan, +A. M., Lim, P. L., Earl, N., Starkman, N., Bradley, L., Shupe, D. L., +Patil, A. A., Corrales, L., Brasseur, C. E., N"othe, M., Donath, A., +Tollerud, E., Morris, B. M., Ginsburg, A., Vaher, E., Weaver, B. A., +Tocknell, J., Jamieson, W., … Astropy Project Contributors. (2022). The +Astropy Project: Sustaining and Growing a Community-oriented Open-source +Project and the Latest Major Release (v5.0) of the Core Package. The +Astrophysical Journal, 935(2), 167. +https://doi.org/10.3847/1538-4357/ac7c74 + + + TensorFlow: Large-scale machine learning on +heterogeneous systems + Abadi + 2015 + Abadi, M., Agarwal, A., Barham, P., +Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., +Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, +M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., … Zheng, X. (2015). +TensorFlow: Large-scale machine learning on heterogeneous systems. +https://www.tensorflow.org/ + + + Scikit-learn: Machine learning in +Python + Pedregosa + Journal of Machine Learning +Research + 12 + 2011 + Pedregosa, F., Varoquaux, G., +Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., +Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., +Cournapeau, D., Brucher, M., Perrot, M., & Duchesnay, E. (2011). +Scikit-learn: Machine learning in Python. Journal of Machine Learning +Research, 12, 2825–2830. + + + The NEMESIS planetary atmosphere radiative +transfer and retrieval tool + Irwin + Journal of Quantitative Spectroscopy and +Radiative Transfer + 109 + 10.1016/j.jqsrt.2007.11.006 + 2008 + Irwin, P. G. J., Teanby, N. A., de +Kok, R., Fletcher, L. N., Howett, C. J. A., Tsang, C. C. C., Wilson, C. +F., Calcutt, S. B., Nixon, C. A., & Parrish, P. D. (2008). The +NEMESIS planetary atmosphere radiative transfer and retrieval tool. +Journal of Quantitative Spectroscopy and Radiative Transfer, 109, +1136–1150. +https://doi.org/10.1016/j.jqsrt.2007.11.006 + + + The PYRAT BAY framework for exoplanet +atmospheric modelling: a population study of Hubble/WFC3 transmission +spectra + Cubillos + Monthly Notices of the Royal Astronomical +Society + 2 + 505 + 10.1093/mnras/stab1405 + 2021 + Cubillos, P. E., & Blecic, J. +(2021). The PYRAT BAY framework for exoplanet atmospheric modelling: a +population study of Hubble/WFC3 transmission spectra. Monthly Notices of +the Royal Astronomical Society, 505(2), 2675–2702. +https://doi.org/10.1093/mnras/stab1405 + + + Tau-REx I: A Next Generation Retrieval Code +for Exoplanetary Atmospheres + Waldmann + The Astrophysical Journal + 2 + 802 + 10.1088/0004-637X/802/2/107 + 2015 + Waldmann, I. P., Tinetti, G., +Rocchetto, M., Barton, E. J., Yurchenko, S. N., & Tennyson, J. +(2015). Tau-REx I: A Next Generation Retrieval Code for Exoplanetary +Atmospheres. The Astrophysical Journal, 802(2), 107. +https://doi.org/10.1088/0004-637X/802/2/107 + + + Forward Modeling and Retrievals with PLATON, +a Fast Open-source Tool + Zhang + Publications of the Astronomical Society of +the Pacific + 997 + 131 + 10.1088/1538-3873/aaf5ad + 2019 + Zhang, M., Chachan, Y., Kempton, E. +M.-R., & Knutson, H. A. (2019). Forward Modeling and Retrievals with +PLATON, a Fast Open-source Tool. Publications of the Astronomical +Society of the Pacific, 131(997), 034501. +https://doi.org/10.1088/1538-3873/aaf5ad + + + petitRADTRANS. A Python radiative transfer +package for exoplanet characterization and retrieval + Mollière + Astronomy & Astrophysics + 627 + 10.1051/0004-6361/201935470 + 2019 + Mollière, P., Wardenier, J. P., van +Boekel, R., Henning, Th., Molaverdikhani, K., & Snellen, I. A. G. +(2019). petitRADTRANS. A Python radiative transfer package for exoplanet +characterization and retrieval. Astronomy & Astrophysics, 627, A67. +https://doi.org/10.1051/0004-6361/201935470 + + + A Systematic Retrieval Analysis of Secondary +Eclipse Spectra. I. A Comparison of Atmospheric Retrieval +Techniques + Line + The Astrophysical Journal + 2 + 775 + 10.1088/0004-637X/775/2/137 + 2013 + Line, M. R., Wolf, A. S., Zhang, X., +Knutson, H., Kammer, J. A., Ellison, E., Deroo, P., Crisp, D., & +Yung, Y. L. (2013). A Systematic Retrieval Analysis of Secondary Eclipse +Spectra. I. A Comparison of Atmospheric Retrieval Techniques. The +Astrophysical Journal, 775(2), 137. +https://doi.org/10.1088/0004-637X/775/2/137 + + + POSEIDON: A multidimensional atmospheric +retrieval code for exoplanet spectra + MacDonald + Journal of Open Source +Software + 81 + 8 + 10.21105/joss.04873 + 2023 + MacDonald, R. J. (2023). POSEIDON: A +multidimensional atmospheric retrieval code for exoplanet spectra. +Journal of Open Source Software, 8(81), 4873. +https://doi.org/10.21105/joss.04873 + + + Supervised machine learning for analysing +spectra of exoplanetary atmospheres + Márquez-Neila + Nature Astronomy + 2 + 10.1038/s41550-018-0504-2 + 2018 + Márquez-Neila, P., Fisher, C., +Sznitman, R., & Heng, K. (2018). Supervised machine learning for +analysing spectra of exoplanetary atmospheres. Nature Astronomy, 2, +719–724. +https://doi.org/10.1038/s41550-018-0504-2 + + + Constructing a Flexible Likelihood Function +for Spectroscopic Inference + Czekala + The Astrophysical Journal + 2 + 812 + 10.1088/0004-637X/812/2/128 + 2015 + Czekala, I., Andrews, S. M., Mandel, +K. S., Hogg, D. W., & Green, G. M. (2015). Constructing a Flexible +Likelihood Function for Spectroscopic Inference. The Astrophysical +Journal, 812(2), 128. +https://doi.org/10.1088/0004-637X/812/2/128 + + + The sonora brown dwarf atmosphere and +evolution models. I. Model description and application to cloudless +atmospheres in rainout chemical equilibrium + Marley + The Astrophysical Journal + 2 + 920 + 10.3847/1538-4357/ac141d + 2021 + Marley, M. S., Saumon, D., Visscher, +C., Lupu, R., Freedman, R., Morley, C., Fortney, J. J., Seay, C., Smith, +A. J., Teal, D., & others. (2021). The sonora brown dwarf atmosphere +and evolution models. I. Model description and application to cloudless +atmospheres in rainout chemical equilibrium. The Astrophysical Journal, +920(2), 85. +https://doi.org/10.3847/1538-4357/ac141d + + + On the Cool Side: Modeling the Atmospheres of +Brown Dwarfs and Giant Planets + Marley + Annual Review of Astronomy and +Astrophysics + 53 + 10.1146/annurev-astro-082214-122522 + 2015 + Marley, M. S., & Robinson, T. D. +(2015). On the Cool Side: Modeling the Atmospheres of Brown Dwarfs and +Giant Planets. Annual Review of Astronomy and Astrophysics, 53, 279–323. +https://doi.org/10.1146/annurev-astro-082214-122522 + + + A systematic retrieval analysis of secondary +eclipse spectra. II. A uniform analysis of nine planets and their c to o +ratios + Line + The Astrophysical Journal + 2 + 783 + 10.1088/0004-637x/783/2/70 + 2014 + Line, M. R., Knutson, H., Wolf, A. +S., & Yung, Y. L. (2014). A systematic retrieval analysis of +secondary eclipse spectra. II. A uniform analysis of nine planets and +their c to o ratios. The Astrophysical Journal, 783(2), 70. +https://doi.org/10.1088/0004-637x/783/2/70 + + + The SPHINX M-dwarf Spectral Grid. I. +Benchmarking New Model Atmospheres to Derive Fundamental M-dwarf +Properties + Iyer + The Astrophysical Journal + 1 + 944 + 10.3847/1538-4357/acabc2 + 2023 + Iyer, A. R., Line, M. R., Muirhead, +P. S., Fortney, J. J., & Gharib-Nezhad, E. (2023). The SPHINX +M-dwarf Spectral Grid. I. Benchmarking New Model Atmospheres to Derive +Fundamental M-dwarf Properties. The Astrophysical Journal, 944(1), 41. +https://doi.org/10.3847/1538-4357/acabc2 + + + Exoplanet reflected-light spectroscopy with +PICASO + Batalha + The Astrophysical Journal + 1 + 878 + 10.3847/1538-4357/ab1b51 + 2019 + Batalha, N. E., Marley, M. S., Lewis, +N. K., & Fortney, J. J. (2019). Exoplanet reflected-light +spectroscopy with PICASO. The Astrophysical Journal, 878(1), 70. +https://doi.org/10.3847/1538-4357/ab1b51 + + + ExoMiner: A Highly Accurate and Explainable +Deep Learning Classifier That Validates 301 New +Exoplanets + Valizadegan + The Astrophysical Journal + 2 + 926 + 10.3847/1538-4357/ac4399 + 2022 + Valizadegan, H., Martinho, M. J. S., +Wilkens, L. S., Jenkins, J. M., Smith, J. C., Caldwell, D. A., Twicken, +J. D., Gerum, P. C. L., Walia, N., Hausknecht, K., Lubin, N. Y., Bryson, +S. T., & Oza, N. C. (2022). ExoMiner: A Highly Accurate and +Explainable Deep Learning Classifier That Validates 301 New Exoplanets. +The Astrophysical Journal, 926(2), 120. +https://doi.org/10.3847/1538-4357/ac4399 + + + Intercomparison of Brown Dwarf Model Grids +and Atmospheric Retrieval Using Machine Learning + Lueber + The Astrophysical Journal + 1 + 954 + 10.3847/1538-4357/ace530 + 2023 + Lueber, A., Kitzmann, D., Fisher, C. +E., Bowler, B. P., Burgasser, A. J., Marley, M., & Heng, K. (2023). +Intercomparison of Brown Dwarf Model Grids and Atmospheric Retrieval +Using Machine Learning. The Astrophysical Journal, 954(1), 22. +https://doi.org/10.3847/1538-4357/ace530 + + + + + + diff --git a/joss.06346/10.21105.joss.06346.pdf b/joss.06346/10.21105.joss.06346.pdf new file mode 100644 index 0000000000..3877dff11b Binary files /dev/null and b/joss.06346/10.21105.joss.06346.pdf differ diff --git a/joss.06346/paper.jats/10.21105.joss.06346.jats b/joss.06346/paper.jats/10.21105.joss.06346.jats new file mode 100644 index 0000000000..848201b08c --- /dev/null +++ b/joss.06346/paper.jats/10.21105.joss.06346.jats @@ -0,0 +1,1534 @@ + + +
+ + + + +Journal of Open Source Software +JOSS + +2475-9066 + +Open Journals + + + +6346 +10.21105/joss.06346 + +TelescopeML – I. An End-to-End Python Package for +Interpreting Telescope Datasets through Training Machine Learning +Models, Generating Statistical Reports, and Visualizing +Results + + + +https://orcid.org/0000-0002-4088-7262 + +Gharib-Nezhad +Ehsan (Sam) + + + + + +https://orcid.org/0000-0003-1240-6844 + +Batalha +Natasha E. + + + + +https://orcid.org/0000-0001-6732-0840 + +Valizadegan +Hamed + + + + + +https://orcid.org/0000-0002-2188-0807 + +Martinho +Miguel J. S. + + + + + +https://orcid.org/0000-0001-8530-7746 + +Habibi +Mahdi + + + + + +Nookula +Gopal + + + + + +Space Science and Astrobiology Division, NASA Ames Research +Center, Moffett Field, CA, 94035 USA + + + + +Bay Area Environmental Research Institute, NASA Research +Park, Moffett Field, CA 94035, USA + + + + +Universities Space Research Association (USRA), Mountain +View, CA 94043, USA + + + + +Intelligent Systems Division, NASA Ames Research Center, +Moffett Field, CA 94035, USA + + + + +Institute for Radiation Physics, Helmholtz-Zentrum +Dresden-Rossendorf, Dresden 01328, Germany + + + + +Department of Computer Science, University of California, +Riverside, Riverside, CA 92507 USA + + + + +10 +6 +2024 + +9 +99 +6346 + +Authors of papers retain copyright and release the +work under a Creative Commons Attribution 4.0 International License (CC +BY 4.0) +2022 +The article authors + +Authors of papers retain copyright and release the work under +a Creative Commons Attribution 4.0 International License (CC BY +4.0) + + + +Python +Astronomy +Exoplanets +Brown dwarfs +Spectroscopy +Atmospheric retrieval +Atmospheric models +Machine learning +Convolutional Neural Network +Telescope datasets + + + + + + Summary +

We are in the verge of a revolutionary era in space exploration, + thanks to advancements in telescopes such as the James Webb Space + Telescope (JWST). High-resolution, high + Signal-to-Noise spectra from exoplanet and brown dwarf atmospheres + have been collected over the past few decades, requiring the + development of accurate and reliable pipelines and tools for their + analysis. Accurately and swiftly determining the spectroscopic + parameters from the observational spectra of these objects is crucial + for understanding their atmospheric composition and guiding future + follow-up observations. TelescopeML is a Python + package developed to perform three main tasks: (i) Process the + synthetic astronomical datasets for training a CNN model and prepare + the observational dataset for later use for prediction; (ii) Train a + CNN model by implementing the optimal hyperparameters; and (iii) + Deploy the trained CNN models on the actual observational data to + derive the output spectroscopic parameters.

+

The implications and scientific outcomes from the trained CNN + models and this package are under revision for The Astrophysical + Journal under the title TelescopeML – II: Convolutional Neural + Networks for Predicting Brown Dwarf Atmospheric + Parameters.

+ + + Statement of Need +

We are on in a new era of space exploration, thanks to advancements + in ground- and space-based telescopes, such as the James Webb Space + Telescope (e.g., + Gardner + et al., 2023) and CRIRES. These remarkable instruments collect + high-resolution, high-signal-to-noise spectra from extrasolar planets + (e.g., + Alderson + et al., 2023), and brown dwarfs (e.g., + Miles + et al., 2023) atmospheres. Without accurate interpretation of + this data, the main objectives of space missions will not be fully + accomplished. Different analytical and statistical methods, such as + the chi-squared-test, Bayesian statistics as well as + radiative-transfer atmospheric modeling packages have been developed + (e.g., + Batalha + et al., 2019; + MacDonald, + 2023) to interpret the spectra. They utilize either forward- + and/or retrieval-radiative transfer modeling to analyze the spectra + and extract physical information, such as atmospheric temperature, + metallicity, carbon-to-oxygen ratio, and surface gravity + (Iyer + et al., 2023; + Line + et al., 2014; + Marley + & Robinson, 2015). These atmospheric models rely on + generating the physics and chemistry of these atmospheres for a wide + range of thermal structures and compositions. In addition to + Bayesian-based techniques, machine learning and deep learning methods + have been developed in recent years for various astronomical problems, + including confirming the classification of light curves for exoplanet + validation (e.g., + Valizadegan + et al., 2022), recognizing molecular features + (Zingales + & Waldmann, 2018) as well as interpreting brown dwarfs + spectra using Random Forest technique (e.g., + Lueber + et al., 2023). Here, we present one of the first applications + of deep learning and convolutional neural networks on the + interpretation of brown dwarf atmospheric datasets. The configuration + of a CNN and the key concepts can be found in + (Goodfellow + et al., 2016; + Kiranyaz + et al., 2021).

+

With the continuous observation of these objects and the increasing + amount of data, there is a critical need for a systematic pipeline to + quickly explore the datasets and extract important physical parameters + from them. In the future we can expand our pipeline to exoplanet + atmospheres, and use it to provide insights about the diversity of + exoplanets and brown dwarfs’ atmospheric compositions. Ultimately, + TelescopeML will help facilitate the long-term + analysis of this data in research. TelescopeML + is an ML Python package with Sphinx-ed user-friendly documentation + that provides both trained ML models and ML tools for interpreting + observational data captured by telescopes.

+ + + Functionality and Key Features +

TelescopeML is a Python package comprising a + series of modules, each equipped with specialized machine learning and + statistical capabilities for conducting Convolutional Neural Networks + (CNN) or Machine Learning (ML) training on datasets captured from the + atmospheres of extrasolar planets and brown dwarfs. The tasks executed + by the TelescopeML modules are outlined below + and visualized in the following Figure:

+ + +

DataMaster module: Performs various tasks to + process the datasets, including:

+ + +

Load the training dataset (i.e., atmospheric fluxes) in CSV + format

+ + +

Split the dataset into training, validation, and test sets + to pass it to the CNN model

+ + +

Scale/normalize the dataset column-wise or row-wise

+ + +

Visualize the training sets in each of the processing steps + for more insights

+ + +

Perform feature engineering by extracting the Min and Max + values from each flux to improve the ML training + performance

+ + + + +

DeepTrainer module: Utilizes different + methods/packages such as TensorFlow to:

+ + +

Load the processed dataset from the DataMaster + module

+ + +

Build Convolutional Neural Networks (CNNs) model using the + tuned hyperparameters

+ + +

Fit/train the CNN models given the epochs, learning rate, + and other parameters

+ + +

Visualize the loss and training history, as well as the + trained model’s performance

+ + + + +

Predictor module: Implements the following tasks + to predict atmospheric parameters:

+ + +

Perform Scale/normalize processes on the observational + fluxes

+ + +

Deploy the trained CNNs model

+ + +

Predict atmospheric parameters, i.e., effective + temperature, gravity, carbon-to-oxygen ratio, and + metallicity

+ + +

Visualize the processed observational dataset and the + uncertainty in the predicted results

+ + + + +

StatVisAnalyzer module: Provides a set of + functions to perform the following tasks:

+ + +

Explore and processes the synthetic datasets

+ + +

Perform the chi-square test to evaluate the similarity + between two datasets

+ + +

Calculate confidence intervals and standard errors

+ + + + + + + Details on the synthetic dataset +

The training dataset (or synthetic spectra) in this study is + computed using the open-source atmospheric radiative transfer Python + package, + PICASO + (e.g., + Batalha + et al., 2019), based on the + Sonora-Bobcat model grid generated for + cloudless brown dwarf atmospheres by + (Marley + et al., 2021). This set encompasses 30,888 synthetic spectra, + each including 104 wavelengths (i.e., 0.897, 0.906, …, 2.512 μm) and + their corresponding flux values. Each of these spectra has four output + variables attached to it: effective temperature, gravity, + carbon-to-oxygen ratio, and metallicity. These synthetic spectra are + utilized to interpret observational datasets and derive these four + atmospheric parameters. An example of the synthetic and observational + dataset is shown in the following figure.

+ + + Details on the CNN methodology for Multi-output Regression + problem +

Each row in the synthetic spectra has 104 input variables. The + order of these data points and their magnitude are crucial to + interpret the telescope data. For this purpose, we implemented a + Convolutional Neural Network (CNN) method with 1-D convolutional + layers. CNN is a powerful technique for this study because it extracts + the dominant features from these spectra and then passes them to the + fully connected hidden layers to learn the patterns. The output layer + predicts the four atmospheric targets. An example of the CNN + architecture is depicted in the following figure.

+ +

TelescopeML main modules to manipulate the training + example, build the ML model, train and tune it, and ultimately + extract the target features from the observational + data.

+ + + + + Documentation +

TelescopeML is available and being + maintained as a GitHub repository at + github.com/EhsanGharibNezhad/TelescopeML. + Online documentation is hosted with Sphinx using + ReadtheDocs tools and includes several instructions + and tutorials as follows:

+ + +

Main page: + ehsangharibnezhad.github.io/TelescopeML/

+ + +

Installation: + ehsangharibnezhad.github.io/TelescopeML/installation.html

+ + +

Tutorials and examples: + ehsangharibnezhad.github.io/TelescopeML/tutorials.html

+ + +

The code: + ehsangharibnezhad.github.io/TelescopeML/code.html

+ + + + + Users and Future Developments +

Astrophysicists with no prior machine learning knowledge can deploy + the TelescopeML package and download the + pre-trained ML or CNN models to interpret their observational data. In + this scenario, pre-trained ML models, as well as the PyPI package, can + be installed and deployed following the online instructions. Tutorials + in the Sphinx documentation include examples for testing the code and + also serve as a starting point. For this purpose, a basic knowledge of + Python programming is required to install the code, run the tutorials, + deploy the modules, and extract astronomical features from their + datasets. The necessary machine learning background and a detailed + guide for package installation, along with links to further Python + details, are provided to help understand the steps and outputs.

+

Astrophysicists with machine learning expertise and data scientists + can also benefit from this package by developing and fine-tuning the + modules and pre-trained models to accommodate more complex datasets + from various telescopes. This effort could also involve the + utilization of new ML and deep learning algorithms, adding new + capabilities such as feature engineering methods, and further + optimization of hyperparameters using different and more efficient + statistical techniques. The ultimate outcome from these two groups + would be the creation of more advanced models with higher performance + and robustness, as well as the extension of the package to apply to a + wider range of telescope datasets.

+ + + Similar Tools +

The following open-source tools are available to either perform + forward modeling (χ²-based test) or retrievals (based on Bayesian + statistics and posterior distribution): + Starfish + (Czekala + et al., 2015), + petitRADTRANS + (Mollière + et al., 2019), + POSEIDON + (MacDonald, + 2023), + PLATON + (Zhang + et al., 2019), + CHIMERA + (Line + et al., 2013), + TauRex + (Waldmann + et al., 2015), + NEMESIS + (Irwin + et al., 2008), and + Pyrat Bay + (Cubillos + & Blecic, 2021).

+

In addition, the following package implements random forest to + predict the atmospheric parameters: + HELA + (Márquez-Neila + et al., 2018)

+ + + Utilized Underlying Packages +

For processing datasets and training ML models in + TelescopeML, the following software/packages + are employed: Scikit-learn + (Pedregosa + et al., 2011), TensorFlow + (Abadi + et al., 2015), AstroPy + (Astropy + Collaboration et al., 2022), SpectRes + (Carnall, + 2017), Pandas + (The + pandas development team, 2020), NumPy + (Harris + et al., 2020), SciPy + (Virtanen + et al., 2020), Matplotlib + (Hunter, + 2007), Seaborn + (Waskom, + 2021), Bokeh + (Bokeh + Development Team, 2018). Additionally, for generating training + astronomical datasets, Picaso + (Batalha + et al., 2019) is implemented.

+ + + Acknowledgements +

EGN and GN would like to thank OSTEM internships and funding + through the NASA with contract number 80NSSC22DA010. EGN acknowledges + ChatGPT 3.5 for proofreading some of the functions. EGN is grateful to + Olivier Parisot and Mike Walmsley for helpful referee reports, and to + the JOSS editorial staff, Paul La Plante and Dan Foreman-Mackey, for + their tireless efforts to encourage new people to join the open source + community in astronomy.

+ + + + + + + + + KiranyazSerkan + AvciOnur + AbdeljaberOsama + InceTurker + GabboujMoncef + InmanDaniel J. + + 1D convolutional neural networks and applications: A survey + Mechanical Systems and Signal Processing + 2021 + 151 + 0888-3270 + https://www.sciencedirect.com/science/article/pii/S0888327020307846 + 10.1016/j.ymssp.2020.107398 + 107398 + + + + + + + GoodfellowIan + BengioYoshua + CourvilleAaron + + Deep learning + MIT Press + 2016 + + + + + + ZingalesTiziano + WaldmannIngo P. + + ExoGAN: Retrieving Exoplanetary Atmospheres Using Deep Convolutional Generative Adversarial Networks + The Astronomical Journal + 201812 + 156 + 6 + https://arxiv.org/abs/1806.02906 + 10.3847/1538-3881/aae77c + 268 + + + + + + + MilesBrittany E. + BillerBeth A. + PatapisPolychronis + WorthenKadin + RickmanEmily + HochKielan K. W. + SkemerAndrew + PerrinMarshall D. + WhitefordNiall + ChenChristine H. + SargentB. + MukherjeeSagnick + MorleyCaroline V. + MoranSarah E. + BonnefoyMickael + PetrusSimon + CarterAarynn L. + ChoquetElodie + HinkleySasha + Ward-DuongKimberly + LeisenringJarron M. + Millar-BlanchaerMaxwell A. + PueyoLaurent + RayShrishmoy + SallumSteph + StapelfeldtKarl R. + StoneJordan M. + WangJason J. + AbsilOlivier + BalmerWilliam O. + BoccalettiAnthony + BonavitaMariangela + BoothMark + BowlerBrendan P. + ChauvinGael + ChristiaensValentin + CurrieThayne + DanielskiCamilla + FortneyJonathan J. + GirardJulien H. + GradyCarol A. + GreenbaumAlexandra Z. + HenningThomas + HinesDean C. + JansonMarkus + KalasPaul + KammererJens + KennedyGrant M. + KenworthyMatthew A. + KervellaPierre + LagagePierre-Olivier + LewBen W. P. + LiuMichael C. + MacintoshBruce + MarinoSebastian + MarleyMark S. + MaroisChristian + MatthewsElisabeth C. + MatthewsBrenda C. + MawetDimitri + McElwainMichael W. + MetchevStanimir + MeyerMichael R. + MollierePaul + PantinEric + QuirrenbachAndreas + RebollidoIsabel + RenBin B. + SchneiderGlenn + VasistMalavika + WyattMark C. + ZhouYifan + BriesemeisterZackery W. + BryanMarta L. + CalissendorffPer + CantalloubeFaustine + CugnoGabriele + De FurioMatthew + DupuyTrent J. + FactorSamuel M. + FahertyJacqueline K. + FitzgeraldMichael P. + FransonKyle + GonzalesEileen C. + HoodCallie E. + HoweAlex R. + KrausAdam L. + KuzuharaMasayuki + LagrangeAnne-Marie + LawsonKellen + LazzoniCecilia + LiuPengyu + Llop-SaysonJorge + LloydJames P. + MartinezRaquel A. + MazoyerJohan + QuanzSascha P. + RedaiJea Adams + SamlandMatthias + SchliederJoshua E. + TamuraMotohide + TanXianyu + UyamaTaichi + ViganArthur + VosJohanna M. + WagnerKevin + WolffSchuyler G. + YgoufMarie + ZhangXi + ZhangKeming + ZhangZhoujian + + The JWST Early-release Science Program for Direct Observations of Exoplanetary Systems II: A 1 to 20 \mum Spectrum of the Planetary-mass Companion VHS 1256-1257 b + The Astrophysical Journal Letters + 202303 + 946 + 1 + https://arxiv.org/abs/2209.00620 + 10.3847/2041-8213/acb04a + L6 + + + + + + + AldersonLili + WakefordHannah R. + AlamMunazza K. + BatalhaNatasha E. + LothringerJoshua D. + Adams RedaiJea + BaratSaugata + BrandeJonathan + DamianoMario + DaylanTansu + EspinozaNéstor + FlaggLaura + GoyalJayesh M. + GrantDavid + HuRenyu + InglisJulie + LeeElspeth K. H. + Mikal-EvansThomas + Ramos-RosadoLakeisha + RoyPierre-Alexis + WallackNicole L. + BatalhaNatalie M. + BeanJacob L. + BennekeBjörn + Berta-ThompsonZachory K. + CarterAarynn L. + ChangeatQuentin + ColónKnicole D. + CrossfieldIan J. M. + DésertJean-Michel + Foreman-MackeyDaniel + GibsonNeale P. + KreidbergLaura + LineMichael R. + López-MoralesMercedes + MolaverdikhaniKaran + MoranSarah E. + MorelloGiuseppe + MosesJulianne I. + MukherjeeSagnick + SchlawinEverett + SingDavid K. + StevensonKevin B. + TaylorJake + AggarwalKeshav + AhrerEva-Maria + AllenNatalie H. + BarstowJoanna K. + BellTaylor J. + BlecicJasmina + CasewellSarah L. + ChubbKaty L. + CrouzetNicolas + CubillosPatricio E. + DecinLeen + FeinsteinAdina D. + FortneyJoanthan J. + HarringtonJoseph + HengKevin + IroNicolas + KemptonEliza M. -R. + KirkJames + KnutsonHeather A. + KrickJessica + LeconteJérémy + LendlMonika + MacDonaldRyan J. + ManciniLuigi + MansfieldMegan + MayErin M. + MayneNathan J. + MiguelYamila + NikolovNikolay K. + OhnoKazumasa + PalleEnric + ParmentierVivien + Petit dit de la RocheDominique J. M. + PiauletCaroline + PowellDiana + RackhamBenjamin V. + RedfieldSeth + RogersLaura K. + RustamkulovZafar + TanXianyu + TremblinP. + TsaiShang-Min + TurnerJake D. + de Val-BorroMiguel + VenotOlivia + WelbanksLuis + WheatleyPeter J. + ZhangXi + + Early Release Science of the exoplanet WASP-39b with JWST NIRSpec G395H + Nature + 202302 + 614 + 7949 + https://arxiv.org/abs/2211.10488 + 10.1038/s41586-022-05591-3 + 664 + 669 + + + + + + GardnerJonathan P. + MatherJohn C. + AbbottRandy + AbellJames S. + AbernathyMark + AbneyFaith E. + AbrahamJohn G. + AbrahamRoberto + Abul-HudaYasin M. + ActonScott + al. + + The James Webb Space Telescope Mission + Publications of the Astronomical Society of the Pacific + 202306 + 135 + 1048 + https://arxiv.org/abs/2304.04869 + 10.1088/1538-3873/acd1b5 + 068001 + + + + + + + CarnallA. C. + + SpectRes: A Fast Spectral Resampling Tool in Python + arXiv e-prints + 201705 + https://arxiv.org/abs/1705.05165 + 10.48550/arXiv.1705.05165 + arXiv:1705.05165 + + + + + + + VirtanenPauli + GommersRalf + OliphantTravis E. + HaberlandMatt + ReddyTyler + CournapeauDavid + BurovskiEvgeni + PetersonPearu + WeckesserWarren + BrightJonathan + van der WaltStéfan J. + BrettMatthew + WilsonJoshua + MillmanK. Jarrod + MayorovNikolay + NelsonAndrew R. J. + JonesEric + KernRobert + LarsonEric + CareyC J + Polatİlhan + FengYu + MooreEric W. + VanderPlasJake + LaxaldeDenis + PerktoldJosef + CimrmanRobert + HenriksenIan + QuinteroE. A. + HarrisCharles R. + ArchibaldAnne M. + RibeiroAntônio H. + PedregosaFabian + van MulbregtPaul + SciPy 1.0 Contributors + + SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python + Nature Methods + 2020 + 17 + 10.1038/s41592-019-0686-2 + 261 + 272 + + + + + + Bokeh Development Team + + Bokeh: Python library for interactive visualization + 2018 + https://bokeh.pydata.org/en/latest/ + + + + + + WaskomMichael L. + + Seaborn: Statistical data visualization + Journal of Open Source Software + The Open Journal + 2021 + 6 + 60 + https://doi.org/10.21105/joss.03021 + 10.21105/joss.03021 + 3021 + + + + + + + HunterJ. D. + + Matplotlib: A 2D graphics environment + Computing in Science & Engineering + IEEE COMPUTER SOC + 2007 + 9 + 3 + 10.1109/MCSE.2007.55 + 90 + 95 + + + + + + HarrisCharles R. + MillmanK. Jarrod + WaltStéfan J. van der + GommersRalf + VirtanenPauli + CournapeauDavid + WieserEric + TaylorJulian + BergSebastian + SmithNathaniel J. + KernRobert + PicusMatti + HoyerStephan + KerkwijkMarten H. van + BrettMatthew + HaldaneAllan + RíoJaime Fernández del + WiebeMark + PetersonPearu + Gérard-MarchantPierre + SheppardKevin + ReddyTyler + WeckesserWarren + AbbasiHameer + GohlkeChristoph + OliphantTravis E. + + Array programming with NumPy + Nature + Springer Science; Business Media LLC + 202009 + 585 + 7825 + https://doi.org/10.1038/s41586-020-2649-2 + 10.1038/s41586-020-2649-2 + 357 + 362 + + + + + + The pandas development team + + Pandas-dev/pandas: pandas + Zenodo + 202002 + https://doi.org/10.5281/zenodo.3509134 + 10.5281/zenodo.3509134 + + + + + + Astropy Collaboration + Price-WhelanAdrian M. + LimPey Lian + EarlNicholas + StarkmanNathaniel + BradleyLarry + ShupeDavid L. + PatilAarya A. + CorralesLia + BrasseurC. E. + N"otheMaximilian + DonathAxel + TollerudErik + MorrisBrett M. + GinsburgAdam + VaherEero + WeaverBenjamin A. + TocknellJames + JamiesonWilliam + van KerkwijkMarten H. + RobitailleThomas P. + MerryBruce + BachettiMatteo + G"untherH. Moritz + AldcroftThomas L. + Alvarado-MontesJaime A. + ArchibaldAnne M. + B’odiAttila + BapatShreyas + BarentsenGeert + Baz’anJuanjo + BiswasManish + BoquienM’ed’eric + BurkeD. J. + CaraDaria + CaraMihai + ConroyKyle E. + ConseilSimon + CraigMatthew W. + CrossRobert M. + CruzKelle L. + D’EugenioFrancesco + DenchevaNadia + DevillepoixHadrien A. R. + DietrichJ"org P. + EigenbrotArthur Davis + ErbenThomas + FerreiraLeonardo + Foreman-MackeyDaniel + FoxRyan + FreijNabil + GargSuyog + GedaRobel + GlattlyLauren + GondhalekarYash + GordonKarl D. + GrantDavid + GreenfieldPerry + GroenerAusten M. + GuestSteve + GurovichSebastian + HandbergRasmus + HartAkeem + Hatfield-DoddsZac + HomeierDerek + HosseinzadehGriffin + JennessTim + JonesCraig K. + JosephPrajwel + KalmbachJ. Bryce + KaramehmetogluEmir + Kaluszy’nskiMikolaj + KelleyMichael S. P. + KernNicholas + KerzendorfWolfgang E. + KochEric W. + KulumaniShankar + LeeAntony + LyChun + MaZhiyuan + MacBrideConor + MaljaarsJakob M. + MunaDemitri + MurphyN. A. + NormanHenrik + O’SteenRichard + OmanKyle A. + PacificiCamilla + PascualSergio + Pascual-GranadoJ. + PatilRohit R. + PerrenGabriel I. + PickeringTimothy E. + RastogiTanuj + RoulstonBenjamin R. + RyanDaniel F. + RykoffEli S. + SabaterJose + SakurikarParikshit + SalgadoJes’us + SanghiAniket + SaundersNicholas + SavchenkoVolodymyr + SchwardtLudwig + Seifert-EckertMichael + ShihAlbert Y. + JainAnany Shrey + ShuklaGyanendra + SickJonathan + SimpsonChris + SinganamallaSudheesh + SingerLeo P. + SinghalJaladh + SinhaManodeep + SipHoczBrigitta M. + SpitlerLee R. + StansbyDavid + StreicherOle + SumakJani + SwinbankJohn D. + TaranuDan S. + TewaryNikita + TremblayGrant R. + Val-BorroMiguel de + Van KootenSamuel J. + Vasovi’cZlatan + VermaShresth + de Miranda CardosoJos’e Vin’icius + WilliamsPeter K. G. + WilsonTom J. + WinkelBenjamin + Wood-VaseyW. M. + XueRui + YoachimPeter + ZhangChen + ZoncaAndrea + Astropy Project Contributors + + The Astropy Project: Sustaining and Growing a Community-oriented Open-source Project and the Latest Major Release (v5.0) of the Core Package + The Astrophysical Journal + 202208 + 935 + 2 + https://arxiv.org/abs/2206.14220 + 10.3847/1538-4357/ac7c74 + 167 + + + + + + + AbadiMartín + AgarwalAshish + BarhamPaul + BrevdoEugene + ChenZhifeng + CitroCraig + CorradoGreg S. + DavisAndy + DeanJeffrey + DevinMatthieu + GhemawatSanjay + GoodfellowIan + HarpAndrew + IrvingGeoffrey + IsardMichael + JiaYangqing + JozefowiczRafal + KaiserLukasz + KudlurManjunath + LevenbergJosh + ManéDandelion + MongaRajat + MooreSherry + MurrayDerek + OlahChris + SchusterMike + ShlensJonathon + SteinerBenoit + SutskeverIlya + TalwarKunal + TuckerPaul + VanhouckeVincent + VasudevanVijay + ViégasFernanda + VinyalsOriol + WardenPete + WattenbergMartin + WickeMartin + YuYuan + ZhengXiaoqiang + + TensorFlow: Large-scale machine learning on heterogeneous systems + 2015 + https://www.tensorflow.org/ + + + + + + PedregosaF. + VaroquauxG. + GramfortA. + MichelV. + ThirionB. + GriselO. + BlondelM. + PrettenhoferP. + WeissR. + DubourgV. + VanderplasJ. + PassosA. + CournapeauD. + BrucherM. + PerrotM. + DuchesnayE. + + Scikit-learn: Machine learning in Python + Journal of Machine Learning Research + 2011 + 12 + 2825 + 2830 + + + + + + IrwinP. G. J. + TeanbyN. A. + de KokR. + FletcherL. N. + HowettC. J. A. + TsangC. C. C. + WilsonC. F. + CalcuttS. B. + NixonC. A. + ParrishP. D. + + The NEMESIS planetary atmosphere radiative transfer and retrieval tool + Journal of Quantitative Spectroscopy and Radiative Transfer + 200804 + 109 + 10.1016/j.jqsrt.2007.11.006 + 1136 + 1150 + + + + + + CubillosPatricio E. + BlecicJasmina + + The PYRAT BAY framework for exoplanet atmospheric modelling: a population study of Hubble/WFC3 transmission spectra + Monthly Notices of the Royal Astronomical Society + 202108 + 505 + 2 + https://arxiv.org/abs/2105.05598 + 10.1093/mnras/stab1405 + 2675 + 2702 + + + + + + WaldmannI. P. + TinettiG. + RocchettoM. + BartonE. J. + YurchenkoS. N. + TennysonJ. + + Tau-REx I: A Next Generation Retrieval Code for Exoplanetary Atmospheres + The Astrophysical Journal + 201504 + 802 + 2 + https://arxiv.org/abs/1409.2312 + 10.1088/0004-637X/802/2/107 + 107 + + + + + + + ZhangMichael + ChachanYayaati + KemptonEliza M. -R. + KnutsonHeather A. + + Forward Modeling and Retrievals with PLATON, a Fast Open-source Tool + Publications of the Astronomical Society of the Pacific + 201903 + 131 + 997 + https://arxiv.org/abs/1811.11761 + 10.1088/1538-3873/aaf5ad + 034501 + + + + + + + MollièreP. + WardenierJ. P. + van BoekelR. + HenningTh. + MolaverdikhaniK. + SnellenI. A. G. + + petitRADTRANS. A Python radiative transfer package for exoplanet characterization and retrieval + Astronomy & Astrophysics + 201907 + 627 + https://arxiv.org/abs/1904.11504 + 10.1051/0004-6361/201935470 + A67 + + + + + + + LineMichael R. + WolfAaron S. + ZhangXi + KnutsonHeather + KammerJoshua A. + EllisonElias + DerooPieter + CrispDave + YungYuk L. + + A Systematic Retrieval Analysis of Secondary Eclipse Spectra. I. A Comparison of Atmospheric Retrieval Techniques + The Astrophysical Journal + 201310 + 775 + 2 + https://arxiv.org/abs/1304.5561 + 10.1088/0004-637X/775/2/137 + 137 + + + + + + + MacDonaldRyan J. + + POSEIDON: A multidimensional atmospheric retrieval code for exoplanet spectra + Journal of Open Source Software + The Open Journal + 2023 + 8 + 81 + https://doi.org/10.21105/joss.04873 + 10.21105/joss.04873 + 4873 + + + + + + + Márquez-NeilaPablo + FisherChloe + SznitmanRaphael + HengKevin + + Supervised machine learning for analysing spectra of exoplanetary atmospheres + Nature Astronomy + 201806 + 2 + https://arxiv.org/abs/1806.03944 + 10.1038/s41550-018-0504-2 + 719 + 724 + + + + + + CzekalaIan + AndrewsSean M. + MandelKaisey S. + HoggDavid W. + GreenGregory M. + + Constructing a Flexible Likelihood Function for Spectroscopic Inference + The Astrophysical Journal + 201510 + 812 + 2 + https://arxiv.org/abs/1412.5177 + 10.1088/0004-637X/812/2/128 + 128 + + + + + + + MarleyMark S. + SaumonDidier + VisscherChannon + LupuRoxana + FreedmanRichard + MorleyCaroline + FortneyJonathan J + SeayChristopher + SmithAdam JRW + TealDJ + others + + The sonora brown dwarf atmosphere and evolution models. I. Model description and application to cloudless atmospheres in rainout chemical equilibrium + The Astrophysical Journal + IOP Publishing + 2021 + 920 + 2 + 10.3847/1538-4357/ac141d + 85 + + + + + + + MarleyMark S. + RobinsonT. D. + + On the Cool Side: Modeling the Atmospheres of Brown Dwarfs and Giant Planets + Annual Review of Astronomy and Astrophysics + 201508 + 53 + https://arxiv.org/abs/1410.6512 + 10.1146/annurev-astro-082214-122522 + 279 + 323 + + + + + + LineMichael R. + KnutsonHeather + WolfAaron S + YungYuk L + + A systematic retrieval analysis of secondary eclipse spectra. II. A uniform analysis of nine planets and their c to o ratios + The Astrophysical Journal + IOP Publishing + 2014 + 783 + 2 + 10.1088/0004-637x/783/2/70 + 70 + + + + + + + IyerAishwarya R. + LineMichael R. + MuirheadPhilip S. + FortneyJonathan J. + Gharib-NezhadEhsan + + The SPHINX M-dwarf Spectral Grid. I. Benchmarking New Model Atmospheres to Derive Fundamental M-dwarf Properties + The Astrophysical Journal + 202302 + 944 + 1 + https://arxiv.org/abs/2206.12010 + 10.3847/1538-4357/acabc2 + 41 + + + + + + + BatalhaNatasha E + MarleyMark S + LewisNikole K + FortneyJonathan J + + Exoplanet reflected-light spectroscopy with PICASO + The Astrophysical Journal + IOP Publishing + 2019 + 878 + 1 + 10.3847/1538-4357/ab1b51 + 70 + + + + + + + ValizadeganHamed + MartinhoMiguel J. S. + WilkensLaurent S. + JenkinsJon M. + SmithJeffrey C. + CaldwellDouglas A. + TwickenJoseph D. + GerumPedro C. L. + WaliaNikash + HausknechtKaylie + LubinNoa Y. + BrysonStephen T. + OzaNikunj C. + + ExoMiner: A Highly Accurate and Explainable Deep Learning Classifier That Validates 301 New Exoplanets + The Astrophysical Journal + 202202 + 926 + 2 + https://arxiv.org/abs/2111.10009 + 10.3847/1538-4357/ac4399 + 120 + + + + + + + LueberAnna + KitzmannDaniel + FisherChloe E. + BowlerBrendan P. + BurgasserAdam J. + MarleyMark + HengKevin + + Intercomparison of Brown Dwarf Model Grids and Atmospheric Retrieval Using Machine Learning + The Astrophysical Journal + 202309 + 954 + 1 + https://arxiv.org/abs/2305.07719 + 10.3847/1538-4357/ace530 + 22 + + + + + +
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