minor update on JOSS from main

publications/joss/paper.md

@@ -1,6 +1,6 @@
 ---
-title: '`TelescopeML` -- I. Convolutional Neural Networks and Machine Learning Python Package for Analyzing  
-Stellar and Exoplanetary Telescope Spectra'
+title: '`TelescopeML` -- I. An End-to-End Python Package for Interpreting Telescope Datasets through Training 
+Machine Learning Models, Generating Statistical Reports, and Visualizing Results'
 tags:
   - Python
   - Astronomy
@@ -51,17 +51,19 @@ aas-journal: The Astrophysical Journal
 # Statement of Need
 
 We are on the verge of a revolutionary era in space exploration, thanks to advancements in ground- and space-based 
-telescopes, such as the James Webb. These remarkable instruments collect an enormous amount of data from extrasolar 
-atmospheres [e.g., @bean2018transiting]. Without an accurate interpretation of this data, the main objectives 
+telescopes, such as the James Webb and CRIRES. These remarkable instruments collect an enormous amount of data 
+from extrasolar atmospheres [e.g., @bean2018transiting]. Without an 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-test and 
-Bayesian statistics, and packages have been developed [e.g., @batalha2017pandexo; @batalha2019picaso; @MacDonald2023POSEIDON]
-and are utilized in the context of forward- and retrieval-radiative transfer modeling to interpret these datasets and 
+Bayesian statistics as well as radiative-transfer atmospheric modeling packages have been developed 
+[e.g., @batalha2017pandexo; @batalha2019picaso; @MacDonald2023POSEIDON]
+and are utilized in the context of forward- and retrieval-radiative transfer modeling to analyze these datasets and 
 extract crucial information, such as atmospheric temperature, metallicity, carbon-to-oxygen ratio, and surface gravity 
 [@line2014systematic; @Iyer2023Sphinx; @Marley2015]. These atmospheric models rely on the synthetic training 
 datasets generated by simulating the physics and chemistry of these atmospheres for a wide range of thermal structures 
-and compositions. In addition, different machine learning and deep learning methods have been developed in recent years 
-to perform various tasks, such as confirming the detection of exoplanets [e.g., @Valizadegan2021 by implementing CNN 
-techniques] and brown dwarfs [e.g., @Lueber2023RandomForesr_BDs using Random Forest ensembles]. However, to the best of
+and compositions. In addition, 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., @Valizadegan2021] as well as interpreting brown dwarfs spectra using Random Forest technique 
+[e.g., @Lueber2023RandomForesr_BDs]. However, to the best of
 our knowledge, this is the first time, deep learning and convolutional neural networks is implemented on brown dwarf 
 atmospheric datasets to predict parameters such as temperature and gravity simultaneously.  
 
@@ -131,6 +133,7 @@ the package to apply to a wider range of telescope datasets.
 # Acknowledgements
 Ask the team if they want to add any grant!!!! 
 Ask Natasha for the Grant!!
-Ask Janet for Gopal's summer support from NASA!!
+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 and providing helpful suggestions. 
 
 # References