Mentor Areas
Physics, astronomy, data science, machine learning
Description:
Although the night sky appears static to the naked eye, it is, in reality, highly dynamic, with countless objects constantly changing in brightness and position. Based on the nature of their variability, transient astronomical sources can be broadly classified as periodic sources, which exhibit regular changes in brightness over fixed periods; stochastic sources, characterized by irregular but continuous or repeated variability over time; and transients, which display abrupt changes in brightness lasting for a limited duration, typically without repetition. Transients are often associated with catastrophic astrophysical events, such as supernovae (SNe) or mergers of compact objects, resulting in kilonovae (KNe). The Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) sensitivity to these transients provides unique opportunities to explore the final stages of stellar evolution and test theories of stellar structure, nuclear physics, and general relativity under extreme conditions. Rare or anomalous transients with unusual properties are of particular interest because they may reveal new physical phenomena or lead to the discovery of exotic, poorly understood objects, potentially opening new avenues for research.
Many of the million a night transients that Rubin detects may not be novel and interesting. Since all the information about a million transients a night would be unmanageable, current approaches heavily filter these based on prior expectations of what supernovae and kilonovae look like. However, this may cause us to miss truly novel discoveries. We will explore instead releasing all million alerts in a compressed format, where the compression is informed by the latent space of existing classifiers used by Rubin alert algorithms. The undergraduate student will explore a variety of data compression techniques in collaboration with postdoctoral scholars and graduate students in the Madhavacheril group.
Preferred Qualifications
Experience with Python programming and data science techniques.
Project Website
Learn more about the researcher and/or the project here. https://msyriac.github.io/
Details:
Preferred Student Year
Second-Year, Junior, Senior
Academic Term
Fall, Spring, Summer
I prefer to have students start during the above term(s).Volunteer
No
Yes indicates that faculty are open to volunteers.Paid
Yes
Yes indicates that faculty are open to paying students they engage in their research, regardless of their work-study eligibility.Work Study
Yes
Yes indicates that faculty are open to hiring work-study-eligible students.