Project Index
Click on a project title to link directly to the project description.
Project FA22a – The environments of luminous blue variables and the implications to type IIn supernovae – position filled
Project FA22b – Prospects for Atmospheric Characterization of Radial Velocity Exoplanets – position filled
Project FA22c – A Census of Blue Post-Asymptotic-Giant-Branch Stars in Galactic Globular Clusters – position filled
Project FA22d – Cross-referencing Supernovae with HETDEX Data – position filled
Project FA22e – Detecting Anomalies in Supernova Lightcurves – position filled
Project FA22a: The environments of luminous blue variables and the implications to type IIn supernovae
Researcher: Conor Ransome cbr5597@psu.edu
Application deadline: Open until filled
Application URL: https://sites.psu.edu/astronomyresearch/undergrads-apply-for-a-project/
Project and position description
Luminous blue variables (LBVs) are massive, evolved stars that undergo dramatic mass loss episodes (such as the great eruption of eta Car) on their way to becoming Wolf-Rayet stars. LBVs are also the only observed progenitors of the mysterious type IIn class supernovae (SNeIIn) with SN2005gl having the only confirmed progenitor detection in pre-explosion imaging. SNeIIn are a highly heterogenous in terms of their spectroscopic and photometric properties, ranging from the subluminous to the superluminous. However, environmental studies have shown that SNeIIn are not well associated with star formation regions in their host galaxies. This suggests that very high mass stars are not the sole progenitor of SNeIIn. This is an ongoing mystery, one that we can help solve with environmental analysis of host galaxies. Environmental analysis can be used to help constrain progenitor masses and ages of a stellar population. LBVs are massive (therefore young) so would be expected to be well associated with ongoing star formation as traced by H-alpha emission but this may not be the case as some LBVs are un-associated with an OB association. In terms of the H-alpha emission, where are these LBVs and how does this association compare to the association of SNeIIn and other SNe such as type Ic SNe that follow the H-alpha emission very well?
The student will use a novel pixel statistics technique (normalised cumulative ranking, NCR) along with archival data of host galaxies with LBV candidates to build up an NCR distribution that will be compared to the NCR distribution of SNeIIn and other SN types. The student will lead the study with weekly meetings with me discussing any aspect of the project such as the sample selection, methods, background, and code. The result will be a report with scope to submit as a peer-reviewed paper.
Desired qualifications
Insterested in astronomical research
Specific knowledge not required but general astronomy knowledge is beneficial
Knowledge of python desirable (preferably with access to Mac/Linux machines)
This will likely be a 2-3 credit project (90-135hrs of work)
Project FA22b: Prospects for Atmospheric Characterization of Radial Velocity Exoplanets
Researcher: Arvind Gupta arvind@psu.edu
Application deadline: June 30, 2022
Application URL: https://sites.psu.edu/astronomyresearch/undergrads-apply-for-a-project/
Project and position description
The NEID spectrograph is expected to facilitate the discovery of terrestrial-mass exoplanets in the habitable zones of nearby, Sun-like stars. Over the next 5 years, our team at Penn State seeks to realize these discoveries by conducting the NEID Earth Twin Survey (NETS) and observing 40 nearby stars. For this project, the student will explore the potential for atmospheric studies of the types of exoplanets we expect to find by the end of this 5-year survey. The student will assess the physical and orbital properties of these hypothetical exoplanets in the context of the detection of atmospheric biosignatures with a LUVOIR-style space telescope. The expected final product of this project is a written research report that will form the basis of a future co-authored or first-authored scientific paper. Upon successful completion of the project, there may also be an opportunity for the student to continue to do research with the mentor & the broader NEID/exoplanet team at Penn State beyond the Fall semester.
Desired qualifications
Completion of or concurrent enrollment in Astro 291 is beneficial but not expected or required, and Astro 320 or a general understanding of observational astronomy is helpful as well. While the project will require the use of Python, prior experience with scientific programming is not required. This project does not require an extensive background knowledge of exoplanets, but I’m looking for someone who is willing to spend time developing this foundation.
This is a 2-3 credit project (90-135 hours). In addition to the actual analysis work, this includes time spent reading relevant papers, developing scientific writing and programming skills, and writing a final report.
Project FA22c: A Census of Blue Post-Asymptotic-Giant-Branch Stars in Galactic Globular Clusters
Researcher: Gautam Nagaraj gxn75@psu.edu
Application deadline: July 31, 2022
Application URL: https://sites.psu.edu/astronomyresearch/undergrads-apply-for-a-project/
Project and position description
After thermal pulses gradually disperse the envelopes of asymptotic giant branch (AGB) stars, the high luminosity sources move across the Hertzsprung-Russell (HR) diagram at nearly constant luminosity as they get hotter. The luminosity a given AGB star reaches depends mainly on its initial mass. Higher mass stars are brighter and evolve much faster over the post-AGB (pAGB) phase (thus making them rare). Lower mass stars take much longer to cross the HR diagram, allowing more observations. Given the finite age of stars and galaxies in the universe, there is a minimum-mass cutoff for evolved stars, which will manifest itself as a lower bound to the pAGB branch at a fixed, known luminosity. Therefore, we can use the lowest luminosity pAGB stars as a standard candle for distances.
In this project, the student will identify blue pAGB stars in up to 109 globular clusters (GCs) in the Milky Way and the Magellanic Clouds using U, B, V, and I photometry. As GCs contain some of the oldest stars in galaxies, we will be able to identify the lowest-luminosity pAGB stars to construct the standard candle. In order to ensure a sample of stars belonging to the GCs, the student will use software developed by former grad student Brian Davis to match stars to the GAIA database for precise astrometric measurements and eliminate foreground interlopers. The student will then measure properties of all blue pAGB candidates and compare to the literature when information is available. We expect about 20% of stars found would have never been catalogued (or associated with GCs) before this project.
If time permits, the student will then help assemble the information needed to create the standard candle, namely determining the minimum absolute brightness (along with uncertainties) of pAGB stars from the created catalog: the blue AGB stars from this project with the yellow AGB stars identified by Davis et al. 2022. As this process uses unrelated distance measurements from previous works, we will be able to calibrate the standard candle.
From this project, we expect to create one or two peer-reviewed articles. Depending on their contributions, the student could be lead author on one or both papers. At the least, the student should get one co-author paper from this project.
Desired qualifications
While there are no specific qualifications, some basic astronomy knowledge would be helpful (such as the solid grounding from ASTRO 291/292). In particular, a good understanding of the stellar evolution process of lower-to-intermediate-mass stars (0.7-8 solar masses initially) would be great. And some experience in Python (or another programming language) would be greatly appreciated.
As this project follows the work of Davis et al. 2022, it would be very helpful to read it beforehand: https://iopscience.iop.org/article/10.3847/1538-4357/ac4224/pdf
This will likely be a 2-3 credit project (90-135 hours of work).
Project FA22d: Cross-referencing Supernovae with HETDEX Data
Researcher: Karthik Yadavalli sky5265@psu.edu
Application deadline: Open until filled
Application URL: https://sites.psu.edu/astronomyresearch/undergrads-apply-for-a-project/
Project and position description
In this project, the student will use host galaxy information to better understand dozens of supernovae. We will use HETDEX data, a dark energy survey conducted using the Hobby Eberly Telescope (HET) at McDonald Observatory. HETDEX takes spectroscopic observations of one million galaxies, allowing us to measure the Hubble-flow redshifts of many nearby galaxies. The student will cross-reference supernovae (discovered through multiple ground-based observatories) with HETDEX galaxies in order to estimate the distances and properties of these galaxies from Earth
Desired qualifications
Some basic Astronomy knowledge would be helpful, along with some experience in Python or another programming language would be appreciated.
This will come with about 2-3 course credits (90-135 hours of work)
Project FA22e: Detecting Anomalies in Supernova Lightcurves
Researcher: Kaylee de Soto kmd6851@psu.edu
Application deadline: Open until filled
Application URL: https://sites.psu.edu/astronomyresearch/undergrads-apply-for-a-project/
Project and position description
Automated analysis and classification of supernova lightcurves is necessary for processing the huge amounts of photometric data that the Vera Rubin Observatory will provide. One aspect of this lightcurve processing is anomaly detection, which allows the isolation of “unique” lightcurves that arise from either errors in data processing or different underlying physics. This project will aim to add an anomaly score to our current supernova classification pipeline, using either traditional Gaussian statistics and/or isolation forests. This project can be expanded during the semester if time allows.
Desired qualifications
Experience with Python is recommended. Completion of an introductory statistics course at the high school or undergraduate level is preferred. Knowledge of machine learning techniques is a plus.
This is expected to be a 2-3 credit project (90-135 hours), which will be set based on the student’s expected course load. Work will consist of programming in Python, reading about relevant statistical techniques, and summarizing the semester’s work in a final report.