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Finding Bumps in the Night: How Scientists Discover the Unknown
FYSEMR 54K

Course Information

Description

The universe is full of “bumps in the night”: cosmic explosions and mysterious signals that appear without warning. When scientists find unexpected results, how do we determine its origin, especially something never seen before? In this seminar, we will explore how raw observations of our universe turn into scientific discoveries using “transient phenomena” (like the explosive death of stars) as a case study.

The seminar emphasizes science as a human and interdisciplinary endeavor. Students will explore how interpretation, judgment, and debate shape what we believe, especially when encountering new anomalies in our data. The seminar will have a strong focus on topics within observational astrophysics, computational methods and modern AI within research. The seminar will also include visits to Harvard’s Collection of Historical Scientific Instruments and the Harvard-Smithsonian Center for Astrophysics engineering lab to explore past, present and future astronomical instruments.

Class Notes

First-Year Seminars are available only to first-year students. You may apply to both Fall 2026 and Spring 2027 First-Year Seminars via the FYS lottery between July 6 and August 6, 2026 at 11:59PM-midnight.

You may apply to as many seminars each term as you would like, but we recommend you apply to at least six in fall and three in spring.

As part of your application, you must provide a brief statement on why you are interested in each seminar. You will be notified of lottery results for both fall and spring seminars at 5 pm on August 11. If you are unsuccessful in the lottery, you may still join any seminar with open seats. A list of open seminars and instructions on next steps will be available on the First-Year Seminar Program website August 11 at 12 Noon.

School Faculty of Arts & Sciences
Credits 4
Cross Reg

Not Available for Cross Registration

Course Component Seminar
Grading Basis FAS Satisfactory/Unsatisfactory
General Education N/A
Quantitative Reasoning with Data N/A
Divisional Distribution None
Course Level Primarily for Undergraduate Students