RaderonLab Astronomy Department

The incredible advancement in computer power, the availability of large amounts of data, and the ability to process them, combined with a theoretical understanding of techniques like machine learning and, more broadly, data mining, have allowed AI to advance at a breakneck pace. AI is adopted for a wide variety of tasks and is becoming increasingly popular in astronomy. Machine Learning (ML) uses various analysis methods under supervised, and unsupervised conditions in order to provide streaming information, such as interpretation, classification, regression, prediction, and more, which now have a real impact on all areas of astronomy. Scientists have employed machine learning in a variety of fields, including galaxy morphology, exoplanet discoveries, transient objects, solar activity prediction, quasars, and more. Data analysis must become more automated and efficient to a considerable extent, particularly using AI. And this is exactly what is going on.

Events

A cooperation agreement was signed between the Astronomy Department of Raderon Laboratory and Erciyes University in Turkye (Project number FBA-2022-11737). This scientific collaboration has been related to the Contact Binary Stars project. Based on the results of this project, five papers were published in prestigious journals with the following titles:

Global Parameters of Eight W UMa-type Binary Systems
The First Multiband Photometric Light Curve Solutions of the V Gru Binary System from the Southern Hemisphere
First Multiband Photometric Light Curve Analysis of Southern Hemisphere Contact Binary System: DM Circinus
Photometric Light Curve Analysis of Two Contact Binary Systems: LS Del and V997 Cyg
First Photometric Light Curve Analysis of Two W-type Contact Binary Systems: OP Boo and V0511 Cam

AI Models

Investigation of the orbital period and mass relations for W UMa-type contact systems

Based on an idea, we have introduced a new relationship for the W UMa systems. If there is photometric data, it is feasible to estimate the mass of the primary component with good accuracy using this relationship. This is a three-parameter (P-T1-M1) relationship modeled using the Artificial Neural Network (ANN) method. This model shows that there is a significant interaction between these three parameters simultaneously. Therefore, M1 can be estimated if one knows both the orbital period and the temperature of the primary component. For further information, see the following published article: https://doi.org/10.1093/mnras/stab3775
To use this model to estimate M1, please enter the required values in its place. Units: Orbital period (day); temperature (Kelvin).

AI Models

Projects and Publications

A model by ANN method for contact binary stars

W Ursae Majoris (W UMa)-type binary systems are important astrophysical tools for studying star formation, stellar structure, and evolution in astrophysics. In contact binary systems, determining the mass of the primary star without using an expensive spectroscopic approach has always been a challenge. In this project, we attempted for the first time to present a method for trying to solve this problem utilizing creative and AI methods. We have obtained a model between the orbital period, the mass of the primary component, and temperature for these types of binary systems using the artificial neural network (ANN) method. We also had a comparison between our ANN model results and previous spectroscopy-based investigations for the primary component masses; the results were very close. The results of this study have been published in the prestigious Monthly Notices of the Royal Astronomical Society (MNRAS) journal. This paper is available in this link: https://doi.org/10.1093/mnras/stab3775.

Title: Investigation of the Orbital Period and Mass Relations for W UMa-type Contact Systems
Journal: Monthly Notices of the Royal Astronomical Society (MNRAS)
Publication Year: 2022
Cite: A Poro, S Sarabi, S Zamanpour, S Fotouhi, F Davoudi, S Khakpash, et al., Investigation of the orbital period and mass relations for W UMa-type contact systems, Monthly Notices of the Royal Astronomical Society, Volume 510, Issue 4, March 2022, Pages 5315–5329.
AI Part: A 3-parameter model by ANN method for contact binary systems.
Doi: https://doi.org/10.1093/mnras/stab3775
Journal link: https://academic.oup.com/mnras/article-abstract/510/4/5315/6486452
Bibcode (Adsabs): https://ui.adsabs.harvard.edu/#abs/2022MNRAS.510.5315P/abstract
ArXiv: https://arxiv.org/abs/2112.13276

Machine Learning Classification for Delta-Scuti Stars

A project in the field of Delta-Scuti variable stars was carried out in collaboration with researchers from four countries. In August 2021, the results of the study were published as a peer-reviewed publication in the Publications of the Astronomical Society of the Pacific (PASP) journal. The P-L relationship was investigated in this project, and measurement accuracy was improved. We applied machine learning classification and fitted lines to the fundamental and overtone modes areas to recognize and classify data. As a result, we provide a new P-L relation for Scuti variables' fundamental and overtone modes. This paper is available in this link: https://doi.org/10.1088/1538-3873/ac12dc The department's team came up with the idea of employing a learning machine, carried out the entire procedure, wrote the content for this section, and presented new relationships.

Title: Observational and Theoretical Studies of 27 δ Scuti Stars with Investigation of the Period–Luminosity Relation
Journal: Publications of the Astronomical Society of the Pacific (PASP)
Publication Year: 2021
Cite: Poro, A., Paki, E., Mazhari, G., Sarabi, S., Alicavus, F.K., et al., 2021. Observational and theoretical studies of 27 δ Scuti stars with investigation of the period–luminosity relation. Publications of the Astronomical Society of the Pacific, 133(1026), p.084201.
AI Part: Period-Luminosity relations for Delta-Scuti stars by using Machine Learning Classification.
Doi: https://doi.org/10.1088/1538-3873/ac12dc
Journal link: https://iopscience.iop.org/article/10.1088/1538-3873/ac12dc/meta
Bibcode (Adsabs): https://ui.adsabs.harvard.edu/#abs/2021PASP..133h4201P/abstract
ArXiv: https://arxiv.org/abs/2102.10136

About Us

The department was established with the goal of developing new approaches and addressing challenges in astronomy and cosmology. Using Artificial Intelligence (AI), expert personnel, and relevant technologies, the department strives to assist in numerous research initiatives in all aspects of astronomy. We have been involved in various research projects. Our team is ready to assist from consulting to project participation.

Contact Info

Postal Address: 900-2025 Willingdon Avenue, Burnaby, British Columbia, V5C 0J3 Canada
Phone: +1 616 920 1190

E-mail: [email protected]