Physics 444, Fall 2021

Course Information:

Prof. Matthew Buckley (office: Serin W329, mbuckley@physics.rutgers.edu)

Lectures: Wednesdays 9:00-10:20, Fridays 3:00-4:20, BE 213 (Livingston)

Office Hours: TBA (Serin W329 and via zoom)

Course Description

This course is an advanced undergraduate-level study of the origins and evolution of the Universe. We will cover the 13.8 billion year history of our Cosmos, as we understand it today. The course covers the expansion of the Universe, our understanding of that expansion in the context of General Relativity, the energy content of the Universe, and epochs of particularly interesting and important physics, including the formation of the Cosmic Microwave Background, Big Bang Nucleosynthesis, the formation of large-scale structure, and cosmic inflation. 

The textbook for the course is Introduction to Cosmology by Barbara Ryden (2nd Edition, 2003, Addison Wesley ISBN 0-8053-8912-1). Please note that the 2nd Edition is the version used in this class; the 1st Edition was published prior to major cosmological discoveries in the 2000’s and thus lacks important content on the Cosmic Microwave Background.

The Canvas website for the class is here, where enrolled students can find and submit homework.

Grading

  • Grades will be based on weekly problem sets (70% of final grade), an in-class midterm (15%), and a final exam (15%).

  • Barring special circumstances announced in advance, weekly homework will be assigned on Friday in class and due on the following Friday in class.

  • Collaboration with other students is strongly encouraged, but your write-up of the solutions must be your own. You must write down the names of your collaborators on your write-up. You must also cite any external sources you use (other than the textbook). You may not refer to notes, assignments, or solutions from previous years of Physics 444.

  • Always show your work. You will not receive full credit if you do not show your work. I am always looking for the reasoning behind the answer.

  • Some homework problems will ask you to use a numeric equation solver, and plot your results. See the bottom of this page for a discussion of possible programs to use and some example documents to get you started. If the homework asks for numeric solutions, you must submit the code you used on Canvas. The code must include the names of all people you worked with.

  • You may hand in homework in person or submit online. In either case, the homework must be written legibly. If submitting online, please organize your written submission into a single file (code may be submitted separately).

  • In general, late homework will automatically receive a maximum of half points. If possible, seek arrangement with me at least 24 hours in advance if you think you have a legitimate excuse for late work. Due to the ongoing public health emergency, I understand that people’s situations can change rapidly and unexpectedly, and so I will be as generous as possible with granting extensions and accepting late homework. After I have graded and handed back an assignment, I will not accept that late submissions.

COVID-19

I am excited to be able to return to in-person teaching. The last 18 months have been very difficult for all of us, though everyone’s individual experience has been different. I know that remote learning has been hard for you all. Both your health and maintaining in-person instruction in a safe manner are top priorities for me this semester. With that in mind, and in accordance with University policy, masks are required in the classroom. This includes me as the instructor.

Please contact me if you feel there are steps that I can take to improve your ability to safely attend class and/or improve your learning experience.

Maintaining safe in-person instruction requires us all to work together to keep the community rate of COVID-19 infection low. If you feel sick, please stay home until you have obtained a negative test or the quarantine period has passed, as per public health recommendations. I will do everything I can to ensure you are not academically punished for missed lectures. If you need more time for homework due to sickness or disruptions from the ongoing health emergency, please let me know and I will grant an extension (you do not need to share personal details in making this request).

If I, as the instructor, am forced to quarantine during a class, I will unfortunately have to resort to online lecture, using zoom. I will contact you via Canvas as far ahead of time as possible in this event.

Regarding the midterm and final exams: it is my hope that the number of students who find they must miss lecture due to COVID-19 quarantines or illness at any one time is small. Should this be the case, students who miss the exams due to illness will have an opportunity to take a make-up exam, as would be the normal procedure in any other academic year. If, due to widespread COVID-19 cases, we are not allowed to give in-person exams, I will not be demanding students take an “in-person” exam at home with online monitoring. Instead, I will construct an alternative exam format; the exact form of which will be determined and communicated to you as students as far in advance as possible. My preferred solution will be a open-book exam, which implies a significantly different question structure than in previous years, but depending on the timing of any (hypothetical) change in exam structure this may not be possible.

I remind you all that cheating is a serious academic infraction that can have serious consequences for your university career, and is forbidden by the Rutgers student honor code. It is also disrespectful to me and your fellow students. I choose to believe you are all adults taking this course because of your interest in the material and out of a desire to learn. I would rather risk that trust being misplaced than subject you all to draconian anti-cheating methods.

Again, I know how difficult these last few semesters have been. Your health and well-being are important to me as your professor. Please continue to take the necessary measures to protect yourself and others; I look forward to a safe and enjoyable semester with you all.

Student Accommodations

If you require special accommodation in the course, please speak with me as early in the semester as possible. Visit this link for information on Rutgers policies.

Course Schedule

  • Week 1 (Sept. 1, 3, 10). Chapters 1 & 2. Introduction, Hubble's Law NOTE, NO CLASS ON WEDNESDAY SEPT. 8 (Monday schedule of classes this day)

  • Week 2 (Sept. 15, 17) Chapter 3. Relativity, Space-time Metric

  • Week 3 (Sept. 22, 24) Chapter 4. Friedmann, Fluid, Acceleration Equations

  • Week 4 (Sept. 29, Oct. 1) Chapter 5. Single-Component Universes

  • Week 5 (Oct. 4 8) Chapter 5 & 6. Single-Component Universes. Multiple Component Universes

  • Week 6 (Oct. 13, 15) Chapter 6 & 7. The Benchmark Model. Cosmological Distances

  • Week 7 (Oct. 20, 22) Chapter 8. Dark Matter

  • Week 8 (Oct. 27, 29) In Class Midterm (Oct. 29)

  • Week 9 (Nov. 3, 5) Chapter 9. The Cosmic Microwave Background

  • Week 10 (Nov. 10, 12) Chapter 9 & 10. The Cosmic Microwave Background. Big Bang Nucleosynthesis

  • Week 11 (Nov. 17, 19) Chapter 10 & 11. Big Bang Nucleosynthesis. Inflation

  • Thanksgiving (No Class)

  • Week 12 (Nov. 29, Dec. 1, 3) Chapter 11 & 12. Inflation. Structure Formation NOTE, CLASS ON MONDAY NOV 29 (Wednesday schedule of classes this day)

  • Week 13 (Dec. 8, 10) Chapter 12. Structure Formation

Numeric Computation

This course will ask students to numerically computer properties of the Universe in the homework.

There are many tools available to allow one to solve differential equations and other numeric manipulations. I will recommend two of them (these are the two I'm most familiar with, so these are the two I will provide support for in office hours). These are Mathematica (current version is 12) and Python3 (Python2.7 is very similar, but has some annoying non-compatibilities).

Mathematica is an extremely powerful mathematical tool. It is very flexible, very useful, and individual licenses are very expensive. 

DO NOT PURCHASE A MATHEMATICA LICENSE FOR THIS COURSE.

As Rutgers students, you should be able to download a free Mathematica license from the Rutgers network. Keep in mind it will require yearly renewal, so when you leave Rutgers, you will not have necessarily have Mathematica available. If you continue in academia, or work in a job that requires it, you will have to get a license elsewhere.

Python is a programming language with many open-source free packages that allow sophisticated mathematical manipulations. In particular the packages NumPy and SciPy are incredibly useful for scientists working with statistics and mathematical problems. You will have to download and install these, along with Python itself. It should be available on all operating systems. In recent years, a useful manager for Python and associated packages is the Anaconda (or Miniconda) management system. It should be available for all operating systems. Using Conda will allow you to use Jupyter notebooks, which allow for interactive python coding (i.e., you can execute code line by line, see if it works, and edit and update as you go).

Additionally, to make plots, I recommend the MatPlotLib package.

The learning curve for Python is probably a bit steeper than for Mathematica, especially if you are not familiar with programming languages. However, it will be available to you where ever you go, and can do many tasks with greater speed and flexibility than Mathematica.

This is not a course on programming, and I will not be asking you to do extremely complicated programming tasks. However, modern science is increasingly computational, and these tools will be very useful. If you are thinking of a career in science or computing, I would recommend taking the plunge and learning Python (I would especially recommend Jupyter notebooks for ease-of-use).

An example documents for Mathematica is here, and covers most of the type of tasks you will be asked to do in the course. The equivalent for Python is here. A Jupyter notebook (identical in most respects to the Python code, but needs to be opened in Jupyter) is here.