| Univ. Belgrade Prof. A. Kovačević | Computational Astrobiology (S2, compulsory, 6 ECTS) |
| Learning Outcomes: | This course will address following learning priorities: Communication, Critical Thinking, Information Literacy, Self-Directed Learning, and Technology Use. Upon completion of this course, s Student will obtain modern, up-to-date knowledge and skills in both astrobiology and computational science. Student will develop the ability to understand, analyse and translate a great variety of astrobiological problems to computer models. Presumably for mapping of environments within, and outside our solar system, where there might exist preconditions for the existence of life; to apply a perspective from biology, in describing how life has developed on Earth, and in studying its limits, e.g., the most extreme environments that terrestrial life can endure. |
| Knowledge and Understanding: | Upon completion of the course, the student should have acquired skills to apply a perspective from astronomy and geosciences, in describing methods for mapping of environments within, and outside our solar system, where there might exist preconditions for the existence of life; to apply a perspective from biology, in describing how life has developed on Earth, and in studying its limits, e.g., the most extreme environments that terrestrial life can endure. |
| Applying Knowledge and Understanding: | Students will design algorithms with mathematical models that seek to explain exoplanetary environments. Students will also create virtual life-forms that reside in the computer and which can mimic life’s processes. Students will demonstrate fluency in coding with Python in the Jupyter Notebook/Lab environment. |
| Prerequisites | Experience with Anaconda Python, SciPy, NumPy, as well as GitHub would be ideal, but is not required |
| Program | Introduction to computational astrobiology. Overview of astrobiological databases and tools. Formation of planetary systems. Simulation of vortices within accretion disk.Solar system and life origins. Exoplanets detection methods. A software tool for exoplanets characterization from radial velocity and transit data. Time Series Analysis for Exoplanets. Application of Gaussian process to transit light curves in order tu detect exoplanets. Inferring the properties of the underlying planet population from incomplete and biased samples from a range of surveys. Planetary signals in sparse datasets.Circumstellar habitable zone modeling. Relationship between genetic code and human designed codes. Simple rules and algorithms for reading DNA sequence. Maximum entropic principle and definition of intelligence. Maximum entropic principle and existence of observes within Universe. |
| Description of how the course is conducted | Classes will be roughly evenly divided between lecture and hands-on practice. Students will be working through real problems during the course of the lecture. Contributions of students real-time solutions will contribute to enhancement of their class participation score. There will be a programming/reading assignment for homework in order to to help synthesize the topics covered in lectures. Three or one person groups will research/conduct a project. Students will write a 6+ page paper (not including figures [which are strongly suggested] and references [required]) and give a 20-minute presentation to the class. Presentations are strongly recommended to be in the form of a Jupyter notebook. Groups should clearly delineate the roles of each student in the project. |
| Description of the didactic methods | Please refer on the point above |
| Description of the evaluation methods | The final exam will be orally and practically defended term project which is defined at the beginning of the semester. |
| Adopted Textbooks | Relevant scientific papers |
| Recommended readings | Statistics, Data Mining, and Machine Learning in Astronomy: A Practical Python Guide for the Analysis of Survey Databy Ivezic, Connolly, VanderPlas, and Gray (ISBN: 9780691151687). Other Info: The purpose of reading the primary source (relevant scientific paper) is to get the original data, not someone else’s interpretation of the data (a secondary source). Books, encyclopedias, dictionaries, review articles, and textbooks are all secondary sources because they have already processed the information in the primary sources for the reader. Through this student will develop critical scientific thinking. |
