ASTR 513 Syllabus

Course Number & Title: ASTR 513 Statistical and Computational Methods in Astrophysics
Course Website: https://ua-2025q3-astr501-513.github.io

Semester and Year: Fall 2025
Time: Monday & Wednesday 11:00-12:15pm
Location: Steward Observatory, Room 208

Description of Course¶

This course introduce basic computational methods for solving problems numerically in astrophysics and the foundations of modern statistical methods that are used in current research problems, with emphasis on big-data science. The topics will include basic scientific algorithms to solve integrals and simple differential equations frequently encountered in astrophysics, frequentist and Bayesian inference methods, non-linear regressions methods, modeling of data, Monte Carlo techniques, error estimation, and model selection.

Course Prerequisites or Co-requisites¶

This course is recommended in conjunction with ASTR 501 Introduction to Computing.

Instructor and Contact Information¶

Instructor: Chi-kwan Chan
Email: chanc@arizona.edu (please include “ASTR 513” in subjects of emails)
Office: Steward Observatory N332
Office Hours: TBD

Instructor: Shuo Kong
Email: shuokong@arizona.edu (please include “ASTR 513” in subjects of emails)
Office: Steward Observatory N328
Office Hours: TBD

Course Format and Teaching Methods¶

Live in person; lecture and lab combination.

Expected Learning Outcomes¶

Upon completion of this course, students will be able to:

• Understand the nature and application of statistical and computational methods in astrophysical research.

• Apply statistical and computational methods correctly, with an understanding of common pitfalls and limitations.

• Demonstrate a broad awareness of how statistical and computational methods are used in various astrophysical contexts.

• Develop the ability to self-learn new computational tools and methods relevant to astrophysical research.

• Critically analyze and interpret data, results, and scientific literature, including data presented in tables, graphs, and charts.

• Communicate scientific knowledge clearly and effectively, both in writing and orally.

• Appreciate computational complexity and develop a basic awareness of numerical errors and their impact on research outcomes.

Specifically, a recent core class homogenization effort suggested covering the following topics:

• Computational Methods:

  • Unix, C, and Python

  • Introduction to numerical analysis; errors, accuracy, stable and unstable computations

  • Root Finding: Bisection and Newton-Raphson

  • Numerical Integration

  • Ordinary Differential Equations (e.g., Runge-Kutta method)

• Statistical Methods:

  • Intro and Definitions: The Normal Distribution, Detection of Signal, Correlation, Data

  • Modeling, Sample Comparison

  • Random Numbers

  • Distribution Functions I; Exponential & Gaussian Distributions

  • Distribution Functions II; Bivariate Gaussians; Binomial; Poisson

  • Markov Chain Monte Carlo

  • Error Propagation - Transformation of Random Variables

  • Frequentist Statistics - Confidence Intervals

  • Frequentist and Bayesian Statistics

  • Frequentist Parameter Estimation; Pearson’s chi2 test

  • Bayesian parameter estimation for linear models

  • Inferring Distributions

  • Fast Fourier Transforms

Policies of Course¶

Absence and Class Participation Policy¶

The UA policy concerning Class Attendance and Participation is available at: https://catalog.arizona.edu/policy/courses-credit/courses/class-attendance-participation.

The UA policy regarding absences for any sincerely held religious belief, observance or practice will be accommodated where reasonable, http://policy.arizona.edu/human-resources/religious-accommodation-policy.

Absences pre-approved by the UA Dean of Students (or Dean Designee) will be honored. See: https://deanofstudents.arizona.edu/policies/attendance-policies-and-practices

Participating in the course and attending lectures and other course events are vital to the learning process. As such, attendance is required at all lectures and discussion section meetings. Absences may affect a student’s final course grade. If you anticipate being absent, are unexpectedly absent, or are unable to participate in class online activities, please contact me as soon as possible. To request a disability-related accommodation to this attendance policy, please contact the Disability Resource Center at (520) 621-3268 or disability@arizona.edu. If you are experiencing unexpected barriers to your success in your courses, the Dean of Students Office is a central support resource for all students and may be helpful. The Dean of Students Office is located in the Robert L. Nugent Building, room 100, or call 520-621-7057.

Makeup Policy for Students Who Register Late¶

Statement on whether students who register after the first class meeting may make up missed assignments/quizzes and the deadline for doing so.

Course Communications¶

Email is the official method to communicate with the instructor and teaching assistant outside scheduled classes and office hours.

Course Materials¶

Required Texts or Readings¶

Required Text: None

References:

• Statistics, Data Mining, and Machine Learning in Astronomy (SDMA), by Zeljko Ivezic, Andrew J. Connolly, Jacob T. VanderPlas, and Alexander Gray, UA Library available online

• Modern Statistical Methods for Astronomy (MSMA), by Eric D. Feigelson, G. Jogesh Babu, UA Library available online

• Numerical Recipes, 2nd Edition in C or 3rd Edition in C++, by William H. Press, Saul A. Teukolsky, William T. Vetterling, & Brian P. Flannery; online versions: https://numerical.recipes

Required or Special Materials¶

As a course on computational physics, students are excepted to have access to computers. Although not required, students will be encouraged to install popular development tools such as git, python, and JupyterLab to their computers.

Required Extracurricular Activities¶

The instructor will provide students additional online videos to broaden the students’ knowledge on computational physics. When bundled with assignments, students are required to watch them. When provided as references, the videos are optional.

Near the end of the semester, students are encouraged to join a field trip to UA’s Computer Center and see our supercomputers.

Assignments and Examinations¶

Schedule/Due Dates¶

This course includes 10 homework assignments. The due dates are listed in the schedule. We try to give at least one week of time for finishing each homework. Late homework will received reduced grades.

Writing Requirement¶

Although this is not a writing intensive course, good documentation is essential in communicating science and developing software, and will be used in evaluating homework and/or projects.

Project¶

Students are expected to work in groups of 3 to 6 for their projects. Each project is will be evaluated on four criteria: i) originality and clarity of the idea, ii) quality of the solution, iii) thoroughness of the documentation, and iv) effectiveness of the presentation. Presentations are scheduled during the week of October 12. The final project package, including presentation slides and any supporting materials, must be submitted on the day of the presentation.

Final Examination¶

A written final exam is scheduled on December 10th.

Grading Scale and Policies¶

The course includes 10 homework assignments, 1 project, and 1 final exam. Each homework is worth 5 points, the project is worth 20 points, and the final exam is worth 30 points. The total is 100 points.

This course provides regular letter grades (A-E), which are based on a simple point system:

• A: 90-100 points

• B: 80-89.9 points

• C: 70-79.9 points

• D: 60-69.9 points

• E: 0-59.9 points

No scaling will be applied. However, there are multiple opportunities to obtain extra credits.

Incomplete (I) or Withdrawal (W): Requests for incomplete (I) or withdrawal (W) must be made in accordance with University policy, which is available at https://catalog.arizona.edu/policy/courses-credit/grading/grading-system.

Dispute of Grade Policy: If a student disagrees on his or her grade on a homework assignment or a project, the student must send the instructor a formal request through email to re-evaluate the grade within a week from the time that the student receives the grade. Because no scaling will be applied in the final grade, the final grade cannot be re-evaluated. A student is expected to know his or her own performance throughout the course.

Scheduled Topics/Activities¶

Code of Conduct¶

Classroom Behavior Policy¶

To foster a positive learning environment, students and instructors have a shared responsibility. We want a safe, welcoming, and inclusive environment where all of us feel comfortable with each other and where we can challenge ourselves to succeed. To that end, our focus is on the tasks at hand and not on extraneous activities (e.g., texting, chatting, reading a newspaper, making phone calls, web surfing, etc.).

Threatening Behavior Policy¶

The UA Threatening Behavior by Students Policy prohibits threats of physical harm to any member of the University community, including to oneself. See http://policy.arizona.edu/education-and-student-affairs/threatening-behavior-students.

Accessibility and Accommodations¶

At the University of Arizona, we strive to make learning experiences as accessible as possible. If you anticipate or experience barriers based on disability or pregnancy, please contact the Disability Resource Center (520-621-3268, https://drc.arizona.edu) to establish reasonable accommodations.

Code of Academic Integrity¶

Students are encouraged to share intellectual views and discuss freely the principles and applications of course materials. However, graded work/exercises must be the product of independent effort unless otherwise instructed. Students are expected to adhere to the UA Code of Academic Integrity as described in the UA General Catalog. See: https://deanofstudents.arizona.edu/student-rights-responsibilities/academic-integrity.

The University Libraries have some excellent tips for avoiding plagiarism, available at https://lib.arizona.edu/research/citing/plagiarism.

Selling class notes and/or other course materials to other students or to a third party for resale is not permitted without the instructor’s express written consent. Violations to this and other course rules are subject to the Code of Academic Integrity and may result in course sanctions. Additionally, students who use D2L or UA e-mail to sell or buy these copyrighted materials are subject to Code of Conduct Violations for misuse of student e-mail addresses. This conduct may also constitute copyright infringement.

Nondiscrimination and Anti-harassment Policy¶

The University of Arizona is committed to creating and maintaining an environment free of discrimination. In support of this commitment, the University prohibits discrimination, including harassment and retaliation, based on a protected classification, including race, color, religion, sex (including pregnancy), national origin, age, disability, veteran status, sexual orientation, gender identity, or genetic information. For more information, including how to report a concern, please see https://policy.arizona.edu/employment-human-resources/nondiscrimination-and-anti-harassment-policy

Our classroom is a place where everyone is encouraged to express well-formed opinions and their reasons for those opinions. We also want to create a tolerant and open environment where such opinions can be expressed without resorting to bullying or discrimination of others.

Usage of Generative AI¶

Homework, projects, and exams in this course are designed to help students apply class concepts, test their understanding, and develop skills in software development and scientific communication. Generative AI tools such as ChatGPT, Google Gemini, and GitHub Copilot can be valuable for brainstorming, exploring alternative approaches, clarifying confusing concepts, and debugging code. Students may also use these tools to clarify difficult concepts and to generate examples that aid their learning.

However, students must write their own code, take full responsibility for their work, and demonstrate a clear understanding of the underlying concepts. While AI tools can support learning, they may produce inaccurate, incomplete, or biased results. Students are responsible for verifying facts, testing code, and critically assessing all submitted material.

Any use of generative AI must be acknowledged or cited (see guidelines from UA library). Failure to disclose such use, or submitting work that is not original, will be considered a violation of academic integrity.

For questions, contact your instructor.

Additional Resources for Students¶

UA Academic policies and procedures are available at http://catalog.arizona.edu/policies

Campus Health¶

http://www.health.arizona.edu/
Campus Health provides quality medical and mental health care services through virtual and in-person care.
Phone: 520-621-9202

Counseling and Psych Services (CAPS)¶

https://health.arizona.edu/counseling-psych-services
CAPS provides mental health care, including short-term counseling services.
Phone: 520-621-3334

The Dean of Students Office’s Student Assistance Program¶

https://deanofstudents.arizona.edu/support/student-assistance
Student Assistance helps students manage crises, life traumas, and other barriers that impede success. The staff addresses the needs of students who experience issues related to social adjustment, academic challenges, psychological health, physical health, victimization, and relationship issues, through a variety of interventions, referrals, and follow up services.
Email: DOS-deanofstudents@arizona.edu
Phone: 520-621-7057

Survivor Advocacy Program¶

https://survivoradvocacy.arizona.edu/
The Survivor Advocacy Program provides confidential support and advocacy services to student survivors of sexual and gender-based violence. The Program can also advise students about relevant non-UA resources available within the local community for support.
Email: survivoradvocacy@arizona.edu
Phone: 520-621-5767

Safety on Campus and in the Classroom¶

For a list of emergency procedures for all types of incidents, please visit the website of the Critical Incident Response Team (CIRT): https://cirt.arizona.edu/case-emergency/overview

Also watch the video available at https://arizona.sabacloud.com/Saba/Web_spf/NA7P1PRD161/common/learningeventdetail/crtfy000000000003560

Confidentiality of Student Records¶

http://www.registrar.arizona.edu/ferpa

Subject to Change Statement¶

Information contained in the course syllabus, other than the grade and absence policy, may be subject to change with advance notice, as deemed appropriate by the instructor.