2026 SURP PROJECTS

1. Numerical Simulations of Solids in Proto-planetary Disks

We will investigate the evolution of solids, the formation material of planets, as they orbit a new born star and embedded in a gaseous nebulae. The aim is to understand how these solid particles can accumulate and grow and, ultimately, leading to the building blocks of planets.

Preferred Skills: good physics/math skills

This project will be supervised by Prof. Yanqin Wu.

2. Instrumentation and Data Analysis for Radio Astronomy Applications

In this project, students will collaborate closely with teams from the University of Toronto Radio Astronomy and InstrumentatioN (UTRAIN) Lab and the Long Wavelength Lab (LWLab). They will actively engage in the development, characterization, and deployment of instrumentation tailored for radio astronomy applications. This includes radio antenna and analog receiver design, algorithmic development for digital correlators, and data analysis for instruments like the Canadian Hydrogen Intensity Mapping Experiment (CHIME), CHIME Fast Radio Burst (CHIME/FRB), the CHIME/FRB Outriggers program, the Canadian Hydrogen Observatory and Radio-transient Detector (CHORD), the Canadian-CHilean Array for Radio Transient Studies (CHARTS), and ongoing instrumentation initiatives at the Algonquin Radio Observatory (ARO).

Preferred Skills: Students should have a strong interest in astrophysics and instrumentation and a willingness to learn new skills. Students will get the most out of this research position if they have experience programming in Python or equivalent.

This project will be supervised by Profs. Juan Mena-Parra and Keith Vanderlinde.

3. Drone-based Calibration of Radio Telescopes

Radio telescopes are limited in their sensitivity by knowledge of the antenna beam response. Typical calibration methods for radio antennas have drawbacks at lower frequencies and in-situ calibration measurements are needed for high-precision science cases such as the observation of hydrogen from the early universe. Research into novel in-situ beam calibration techniques include using drones, GPS satellites, or cubesats as a known calibration source that allow for a high precision beam measurement. Drone-based experiments offer a platform that is entirely under experimenter control and are ideal for development and prototyping.

In this project, the student will design, build, and test a drone platform that is capable of flying a well-characterized calibration source in a predetermined pattern over an antenna under test (AUT). The student will gain experience in rapid prototyping techniques such as 3-D printing, data analysis techniques, and drone piloting, if time permits.

Preferred Skills: The student should have a strong interest in hands-on prototyping. Proficiency in Python would be helpful but is not required.

This project will be supervised by Dr. Amy Zhao and Prof. Keith Vanderlinde

4. Measuring High Precision Stellar Motions by Combining James Webb, Hubble, and Gaia

Motions of stars are vital to astrophysics; they allow us to understand how our Galaxy formed and evolved as well as put constraints on the nature of dark matter. For this project, we will focus on measuring extreme precision motions of stars across the plane of the sky: the so-called proper motion (PM). While the Gaia satellite has measured PMs for over a billion stars, faint stars (which are more likely to be at larger distances) have large PM uncertainties that make it difficult to constrain different dynamical properties of our Galaxy at large galactocentric radii. We will address this issue by combining data from Gaia with images from the James Webb Space Telescope (JWST) to measure significantly improved PMs for the faintest stars in Gaia. Recent work by our group has developed a new prototype tool, GaiaWebb, that is able to process data from one camera of JWST (i.e., NIRISS, ~3% of images), but this project will focus on expanding that tool to handle all JWST data (i.e., including images from the MIRI and NIRCam cameras), greatly improving its applicability and potential impact. Once completed, the project will then adapt another tool to combine JWST and Gaia with additional information from Hubble images, producing catalogues of the most precise PMs possible.

Preferred Skills: Experience in data analysis and scientific programming in python are desired.

This project will be supervised by Dr. Kevin McKinnon and Prof. Ting Li.

5. Using Gaia+Hubble stellar motions to constrain the local universe

The Local Group of galaxies — which contains the Milky Way, Andromeda, their satellites, and other dwarf galaxies — is a unique testbed for constraining our theories of galaxy formation and evolution. In particular, their relatively nearby distances mean that we can connect the properties of stars (e.g., chemistry, velocities) to the bulk properties of their hosts to test our models of the universe across many distance scales. This project will begin with building large catalogues of improved stellar motions by combining data from the Gaia satellite with Hubble Space Telescope (HST) images. We will target HST images around nearby dwarf galaxies, globular clusters, and stellar streams as particularly interesting astrophysical structures. We will then identify which systems are most impacted by our improved motion catalogues, and then investigate the dynamics of those systems. For dwarf galaxies, this means measuring bulk motions to understand their interaction histories within the Local Group as well as measuring internal velocity distributions to determine dark matter density profiles in their cores. For stellar streams, this means searching for kinematic evidence for possible perturbations from elusive dark matter subhalos. Ultimately, this project will leverage these high-precision measurements to provide powerful new constraints on the history of the Local Group and the fundamental nature of dark matter.

Preferred Skills: Experience in data analysis and scientific programming in python are desired. Understanding of dynamics in an astrophysics context is also valuable, but not required.

This project will be supervised by Dr. Kevin McKinnon and Prof. Ting Li.

6. DMD-based Multi-object Spectrograph Software Development and Data Analysis

Multi-object spectrographs play a crucial role in a variety of science cases in astronomy. The Digital Micromirror Device Multi-Object Spectrograph (DMD-MOS) is an innovative and elegant approach to multi-object spectroscopy that relies on micro-electromechanical (MEMS) mirrors to pick off light from selected targets in the acquisition field to channel into the spectroscopic arm of the instrument. This approach eliminates the need for fixed MOS masks, fiber positioning robots, and various other configurable slit technologies found in modern MOS spectrographs.
We have completed the system development and lab testing of the DMD-MOS and are now approaching integration with the 0.5-m telescope at the E.C. Carr Astronomical Observatory (CAO). The team is currently developing the instrument control software, observation software, and the data-reduction pipeline.

We are now hiring a student to join the Summer Research Program. The successful candidate will:
1. Contribute to finalizing the instrument control software and data-reduction pipeline.
2. Participate in on-sky testing at CAO, acquire high-quality observational data, and perform data processing and science analysis.

Data analysis and software development in this project will play a critical role in advancing DMD-MOS toward future ground-based and space-based multi-object spectroscopy.

Preferred Skills: 1. Strong background in optics, physics, or a related field. 2. Experience with data analysis. 3. Familiarity with optical instrumentation and observation. 4. Prior observation and data analysis experience is an asset.

This project will be supervised by Dr. Shaojie Chen.

7. Exploring Stellar Structure via Stellar Evolution Code MESA

Common envelope evolution, which describes a phase of binary stars where interaction and consequent engulfment result in a shared stellar envelope, is a field of increasing popularity, given that it is a primary mechanism for producing the close binaries observed via LIGO. Improvements in hydrodynamic modeling have led to several breakthroughs in our understanding of these events. Still, in an era of increased awareness around low-confidence “black box” modeling and computational inefficiency, it is more important than ever to test these results. The one-dimensional code MESA, or Modules for Experiments in Stellar Astrophysics, has proven to be a powerful and, in many cases, equal tool to three-dimensional software, which can complete models in a fraction of the time, allowing for greater exploration of physical phenomena. Here, we will test MESA’s various means of modeling convective processes in stars, a fundamental property of stars that 3D models often fail to encompass in their entirety, and see how it can affect the overall results of a common envelope event. The results would not only improve the field’s understanding of the importance of convection as a means of energy transport in modeling efforts but also serve as a critical starting point for identifying these events in transient surveys. As we are entering the era of the Vera C. Rubin Observatory’s contributions to transient detection, this will serve as a major step in the field between connecting models to real detections, a bridge that has yet to be built in this subfield.

This project will be supervised by Dr. Nikki Noughani and Prof. Maria Drout.

8. Nature or Nurture: Cosmic Explosions, Collisions and their Environments

This project will be supervised by Dr. James Leung and Prof. Maria Drout.

9. Development of an Imaging Fourier Transform Spectrograph

We are a newly established optics laboratory led by Professor Laurie Rousseau-Nepton. We are focusing on developing innovative optical instrumentation. We are seeking highly motivated senior undergraduate students and early-stage graduate students to join our team for a summer research project.

This project aims to develop a next-generation Imaging Fourier Transform Spectrometer (IFTS) that integrates two emerging technologies:
(1) Microwave Kinetic Inductance Detector (MKID)
(2) Metasurface-based image slicer

During this SURP project, our goal is to complete the core system design and initial testing of the IFTS, enabling the team to gain hands-on experience with SCMOS-based detection, instrument alignment, and data analysis workflows.

Research Responsibilities
Students will have opportunities to participate in, and potentially lead, the following tasks:
Understand the principle of the IFTS system.
Work with state-of-the-art optical, optoelectronic, and optomechanical components.
Conduct precision alignment, characterization, and testing of optical subsystems.
Build and evaluate an IFTS prototype in the laboratory.
Develop a deep understanding of the physical principles and data products associated with IFTS.

Preferred Skills: Background in optics, physics, or a related field. Experience with data analysis (e.g., Python/Matlab). Familiarity with optical instrumentation and laboratory practices. Prior hands-on optical lab experience is an asset.

This project will be supervised by: Prof. Laurie Rousseau-Nepton.

10. Exploring Local Star Formation Processes through Stellar Populations

Young stellar populations provide a fossil record of the star formation event that formed them, preserving the history of the parent cloud. The Gaia spacecraft has made this stellar record more accessible than ever before, providing positions, velocities, and luminosities for nearly 2 billion stars in the Milky Way. This data is the basis for the SPYGLASS (Stars with Photometrically Young Stellar Populations Around the Solar System) Program, a project to identify new young stellar structures in the solar neighbourhood and use them to reveal the patterns guiding local star formation.

Students will use data from the SPYGLASS program alongside observations from ground-based observatories to explore one of several unexplained features of the local star formation record. Potential projects range from the characterization of newly discovered young stellar populations and uncovering their star formation histories, to studying larger-scale patterns like feedback-driven expanding bubbles, to dynamical studies using using new gas observations of star-forming regions. The exact nature of the project will be subject to the specific interests of the student hired.

The student chosen will gain experience in scientific python coding and science communication, with the goal of eventually publishing the result. Depending on the project, there may also be opportunities for running and interpreting simulations, as well as planning and participating in remote observing runs at ground-based observatories.

Preferred Skills: Experience with python programming, basic understanding of star formation

This project will be supervised by: Dr. Ronan Kerr.

11. alphaUniverse: The AI Virtual Observatory

Modern astronomical sky surveys produce large volumes of data across various modalities like images, spectra and time series observations that are hard to synthesize together using traditional data analysis techniques. To solve this, we plan to build alphaUniverse, a platform that uses AI foundation models that enables an “embedding-first” approach to astronomical discoveries. Embeddings are compressed versions of observed data produced by AI foundation models that are expected to encode physical properties of astrophysical objects.
During the summer the student will contribute towards building the core infrastructure of this AI virtual observatory. This will include, creating a web based platform to distribute embeddings for various astronomical data sets to users, and leverage the generative capabilities of foundation models to serve some value added products. These may include predictions of physical properties of objects like redshift, mass, chemical composition along with predictions of what an optical spectrum may be given imaging data.
We are looking for students with a strong background in coding with proficiency in at least one programming language (preferably Python). An interest to learn about things like web development frameworks, cloud computing, deep learning, astronomical sky surveys and using AI agents or coding tools to accomplish software engineering tasks will be valued.

Preferred Skills: Strong coding skills in atleast one language, preferably in Python

This project will be supervised by Prof. Josh Speagle, Dr. Biprateep Dey, and Nolan Koblischke.

12. Studying Supermassive Black Holes with Cosmic Microwave Background Observations

Cosmic microwave background (CMB) telescopes designed for studying the early Universe and its subsequent evolution are also excellent instruments for probing astrophysics with millimetre-wave light. In particular, the Atacama Cosmology Telescope (ACT; 2008–2022) observed around 28,000 radio galaxies, the majority of them blazars, i.e., active galactic nuclei (AGN) emitting jets oriented close to our line of sight. The brand-new Simons Observatory (SO) is forecasted to observe about 96,000 such objects. By studying their time variability, we can probe the physics of their powerful relativistic jets that play an enormous role in ejecting gas from galaxies and regulating star formation. Moreover, a few AGN have recently been discovered with a sinusoidal variations in brightness, making them compelling candidates for supermassive black hole (SMBH) binaries. The successful SURP candidate for this project will work with Prof. Hincks and his group to study the treasure trove of AGN data from ACT and/or SO data. Possible aspects of this project include reducing raw data to light curves of AGN and creating simulated AGN observations to characterise the significance of SMBH binary detections.

Preferred Skills: All candidates should have some experience coding in Python, with demonstrated strong coding skills being an asset. Experience working with time-series data and doing Fourier analysis are plusses.

This project will be supervised by Prof. Adam Hincks.

13. What can marked power spectrum tell us about neutrino mass and beyond-LCDM physics?

Massive cosmological surveys such as the Dark Energy Spectroscopic Instrument (DESI), the Legacy Survey of Space and Time (LSST), and the Simons Observatory promises to constrain fundamental cosmological parameters to never-seen-before precisions. However, extracting maximum information about parameters such as the sum of neutrino masses requires us to use complicated statistics such as measuring the “most typical triangles” (also known as the three-point correlation function or the bispectrum), which can be computationally expensive and intractable at times. A promising alternative is to use the “marked power spectrum” technique which re-weights the cosmological datasets to enhance signals of neutrino masses. In this project, the student will study how different choices of this re-weighting technique can enhance signals of neutrino masses and modified gravity. The student will receive training to use cosmological simulations to study these techniques. If the student is interested to continue further, they may use their findings to apply on the actual DESI DR2 dataset.

Skills: Familiarity with python, basic statistics. Preferred: familiarity with NumPy

This project will be supervised by Dr. Tanveer Karim.

14. Fiber-Based Integral Field Spectrograph (IFS) Development

This summer SURP project focuses on the development and testing of a fiber-based integral field spectrograph (IFS) as part of a broader effort to advance innovative optical instrumentation. The work builds on established expertise in high-sensitivity detectors, multi-object spectrographs (MOS), and weak-signal fluorescence detection, and will give students direct, hands-on experience with the technologies that enable modern spectroscopic instrumentation.
Students accepted into this program will gain a deep understanding of the working principles behind fiber-based IFS, perform characterization and basic performance testing of fiber bundles, and take part in system assembly, optical alignment, and laboratory validation. Students will also analyze test results, identify system issues, and iteratively refine the setup to improve overall data quality, providing valuable training in both experimental optics and instrument development.

Preferred Skills:  Strong background in optics, physics, or a related field. Familiarity with optical instrumentation and laboratory practices. Experience in using or testing spectrographs is strongly preferred. Prior experience working in an optics laboratory is an asset. Strong communication skills and the ability to work effectively in a collaborative team environment.

This project will be supervised by Prof. Ting Li and Dr. Shaojie Chen.

15. Quest for the First Galaxies and Black Holes with JWST — Harnessing the Power of Gravitational Lensing

The first generations of stars and black holes mark the cosmic dawn, shaping the earliest stages of galaxy formation and chemical enrichment. Despite decades of theoretical predictions, direct evidence for these “Population III” systems or their black hole remnants remains elusive. The unparalleled sensitivity of JWST, combined with gravitational lensing magnification by massive galaxy clusters, now enables us to probe faint galaxies deep into the epoch of reionization.

In this project, the student will analyze deep JWST/NIRCam imaging data from existing lensing cluster programs, including the VENUS survey — the largest JWST lensing survey initiated in 2025. Using these datasets, the student will search for galaxies that show the distinct photometric features predicted for metal-free stellar populations, such as strong Balmer lines and weak metal emission lines. The study will also explore unusually bright systems with similar colors that may indicate early black hole formation. Students will quantify the abundance and physical properties of these candidates and compare their findings to theoretical models of early galaxy and black hole evolution.

For advanced students, the project can expand to include spectroscopic searches using JWST/NIRSpec IFU datacubes, examining pixel-by-pixel spectra for metal-free or broad-line signatures. If the student has prior experience with AI or machine learning, these methods can be incorporated to enhance detection and classification performance.

Preferred Skills: Students who have astrophysical and data science-related backgrounds will be a better fit for this position.

This project will be supervised Prof. Seiji Fujimoto, Dr. Yoshihisa Asada, and Dr. Qinyue Fei

16. Searching through Rubin Alerts to find Pre-SN variability

The Vera Rubin Observatory is an exceptional new facility that will start the Legacy Survey of Space and Time (LSST) early 2026. While some of its data will remain proprietary for a while, it will publish world-public alerts whenever it detects a new change in the sky. This project will explore some of the first alerts that are produced by Rubin LSST to look for pre-SN variability. This is an exploratory project, and its direction will change based on findings and on how Rubin LSST itself evolves. There are a number of possible projects that we can do. For example, we can see if any interesting targets registered by the Transient Name Server (TNS) show up in Rubin LSST alerts, and analyze them more carefully. Alternatively, we are also planning to launch a citizen science project that shows volunteers some of these alerts. This project can analyze some of the first results from this citizen science project. This project will likely make use of software recently developed (called “DETECT”) to analyze alerts in greater detail.

Preferred Skills: Python Programming

This project will be supervised by Dr. Tobias Géron and Prof. Maria Drout.

17. Studying the kinematics of barred galaxies using IFUs

Recent studies highlight the crucial role of bars in galaxy evolution. For example, bars (like the one in NGC1300) can drive gas inflows, potentially triggering central starbursts and eventual quenching of its host. In this project, the student will use the Mapping Nearby Galaxies at APO (MaNGA) IFU survey to study the kinematics of barred galaxies. This is an exploratory project, and its direction will evolve based on findings. There are a number of projects that students can do. For example, they can investigate whether the strength of the bar affects the inflow of gas. Another potential direction is looking whether barred galaxies have an offset on the Tully-Fisher relationship.

Preferred Skills: Python Programming

This project will be supervised by Dr. Tobias Géron and Prof. Maria Drout.

18. Study of Infant Supernovae and Unusual Optical Transients

Supernovae studies have been central in moving modern astronomy forward,
which is best described as “seeding the elements and measuring the Universe.” Infant supernovae that are detected within a few hours from explosion are of particular interest and importance since they have crucial natal information for how supernovae explode. They are also potential targets for neutrino and/or gravitational wave detection. Using the new KMTNet facility, which provides 24-hour continuous sky coverage with three wide-field telescopes in southern hemisphere, we are now detecting elusive infant supernovae as well as other unusual optical transients previously unidentified. This project is to study those young supernovae and optical transients to understand their origin and evolution.

Preferred Skills: Python Coding

This project will be supervised by Prof. Dae-Sik Moon.

19. Bayesian Supernova Cosmology with the Vera C. Rubin Observatory

Type Ia Supernovae are calibrated standard light beacons that enable us to measure distances across cosmic time. These distances encode the expansion history of the Universe; however, one of the biggest challenges is finding a “pure” sample of these supernovae, given that many things explode in the night sky, and only some of those are useful cosmological probes. The Vera C Rubin Observatory is a telescope that takes images of the sky and will find hundreds of thousands of these objects, contaminated by other light sources. Our group is working on a fully Bayesian supernova cosmology analysis pipeline to process the incoming Rubin data. There are many aspects to this analysis, including parametrizing supernova rates over time, modelling supernova spectra, and more practical considerations such as optimizing the analytic and numerical runtime, and performing coverage tests. Depending on your interests and strengths, your tasks could include developing statistical tests to determine the accuracy of the Bayesian model, using conformal prediction or similar methods to improve quantified uncertainties, performing an independent analysis on an alternate supernova dataset, or optimizing the code for accuracy or performance.

This project will be supervised by Prof. Renee Hlozek.

20. Deblending time-dependent images (from the sky and the brain) to identify transient signals

As telescopes become more powerful, separating the light from distant galaxies and nearby objects becomes increasingly difficult. Finding signals associated with particular interesting (often transient) objects is key to learning new physics from these explosions in the sky, or about neurodegeneration in the brain. This interdisciplinary project will develop new tools for modelling signals from multiple objects where there are also transients present. This will enable us to ‘deblend’ astronomical (or biological) images. The project will be working within a team of astronomers and biologists who have different needs for similar statistical tools.

This project will be supervised by Prof. Renee Hlozek.

21. Modeling the impact of the Large Magellanic Cloud on the Milky Way’s dark matter

In recent years it has become clear that the Milky Way’s largest satellite, the Large Magellanic Cloud, is more massive than previously thought and contains about 1/10 of the Milky Way’s mass at only 50 kpc from the Milky Way’s center. As such, the Large Magellanic Cloud has a big impact on the dynamics of objects in the Milky Way’s stellar halo (other satellites, globular cluster, stellar streams, and general stellar-halo stars). Properly accounting for the Large Magellanic Cloud’s various dynamical effects is therefore essential in the coming area of ‘precision near-field cosmology’, where we use Milky Way observations to constrain the nature of dark matter.

The Large Magellanic Cloud has essentially three gravitational effects on the orbits of objects in the Milky Way’s halo: (i) it’s direct gravitational pull on the object, (ii) it’s pull on the Milky Way’s center which causes the entire Milky Way to accelerate towards the Large Magellanic Cloud, and (iii) a deformation of the Milky Way’s dark matter halo that changes its gravitational effect on objects. We currently have good models for (i) and (ii), but not for (iii). The goal of this project is to test simple methods for computing models of (iii) that could be incorporated into the galpy Galactic Dynamics code for future high-precision measurements of the Milky Way’s dark matter content.

Preferred Skills: Python Programming

This project will be supervised by Prof. Jo Bovy.

22. Limit Testing Correlation Calibration

Correlation Calibration, or CorrCal, is a new technique for calibrating data from radio interferometric arrays that was designed to meet the stringent calibration requirements for precision 21cm cosmology. CorrCal has been demonstrated to be robust to a variety of expected modeling errors, and preliminary tests show reasonable performance on data from the Hydrogen Epoch of Reionization Array. While these tests established strong foundational support for CorrCal’s ability to meet the demands of precision 21cm cosmology, they do not provide a holistic view of CorrCal’s performance in 21cm data analysis. This project will be focused on developing a more complete understanding of how CorrCal performs in real data analysis applications. The specific approach for this project will depend on the student’s interest and whether they would prefer to take a simulation-based approach or work with observational data. The student will learn fundamentals of observational 21cm cosmology and radio interferometry and will gain experience analyzing large, high-dimensional data.

Essential Skills: proficient in Python, comfortable with linear algebra, multivariate calculus, and electromagnetism; Preferred: numerical optimization, high performance computing, shell scripting, data visualization

This project will be supervised by Dr. Bobby Pascua.

23. Galactic Archaeology in Nearby Dwarf Galaxy Relics

The absorption features imprinted on a star’s spectrum encode its physical structure, chemical composition, and relative motion, which in turn provide a fossil record of its host galaxy’s chemical and dynamical evolution across cosmic time. Accordingly, galactic archaeology—the study of a galaxy’s resolved stars—provides a powerful lens through which to test theories of galaxy evolution and probe the nature of dark matter. Dwarf galaxies, as some of the smallest, oldest, and most dark matter-dominated galaxies in the local Universe, represent particularly valuable sites for galactic archaeology. This has motivated large spectroscopic surveys, including the Southern Stellar Streams Spectroscopic Survey (S5) [1] and the Dark Energy Spectroscopic Instrument (DESI) [2] survey to collect spectra for thousands of stars in nearby dwarf galaxies.

In this research project, the SURP student will have the opportunity to apply galactic chemical evolution and dynamical models as well as a variety of statistical techniques to large stellar datasets collected by spectroscopic surveys like S5 and DESI. Potential directions of this research include: i) identification and characterization of stellar populations in dwarf galaxies, ii) searches for spatially extended stellar populations around dwarf galaxies (e.g., stellar halos and tidally stripped stars), and iii) investigation of the chemical and dynamical evolution of dwarf galaxies.

Preferred Skills: Experience with numerical Python. A familiarity with statistical inference is preferred but not required.

This project will be supervised by Dr. Nathan Sandford and Prof. Ting Li.

24. Unraveling the Formation History of the Andromeda Galaxy

As the nearest Milky Way-like spiral galaxy, the Andromeda Galaxy (M31) offers a unique external astrophysical laboratory to study the formation and evolution of our own Galaxy and spiral galaxies more generally. Importantly, M31 is near enough that we can acquire spectroscopy of individually resolved stars in its disk and stellar halo, which encodes critical chemical and dynamical information through which M31’s evolution can be investigated. As a result, M31 has been the subject of several recent observing programs, including the first deep JWST [1] spectroscopy to probe M31’s crowded inner disk and complementary wide-field DESI [2] spectroscopy, which is mapping M31’s outer stellar halo. Together, these observations are providing a transformative new perspective on our nearest neighboring spiral galaxy.

This project offers several opportunities depending on student interest and data availability to make direct contributions to our understanding of M31 and its formation history. The project may involve one or more of the following undertakings: i) characterizing the chemical and dynamical properties of M31’s inner stellar disk with new JWST observations, ii) developing novel machine learning methods for measuring stellar chemistry from JWST spectroscopy, and/or iii) exploring stellar substructure in M31’s outer stellar halo with new DESI observations.

This project will be supervised by Dr. Nathan Sandford, Prof. Ting Li, and Prof. Jo Bovy.

25. Simulating Stellar Collisions and Transients

We will explore one or more of the most dramatic events that can happen to stars: colliding with another star, being torn up by the gravity of a black hole, crashing through a gas disk that orbits a giant black hole, collapsing into a new-formed central black hole, or being destroyed from within by an explosion or by a jet. Whichever we choose, we will make a clear statement of the project and explore the literature, and consider the options before setting up our own simulations.

Preferred Skills: curiosity and passion for exploration

This project will be supervised by Prof. Chris Matzner.

26. Statistical Signatures in Polarimetric Light Curves from Black Hole Accretion

The supermassive black hole Sagittarius A* (Sgr A*) has a dynamic environment where the magnetized accretion flow produces variable high-energy flares observed in X-ray, infrared, and radio wavelengths. While recent Event Horizon Telescope (EHT) imaging has confirmed the ring-like structure of the black hole shadow, the physical nature of the time-variable emission remains unknown. Leading hypotheses attribute these flares to orbiting compact “hotspots” or flux tubes formed near the event horizon, yet simulations suggest that stochastic fluctuations within a turbulent disk can produce similar transient features. Distinguishing between discrete coherent structures and continuous turbulence requires a rigorous statistical study that connects disk properties to observables. This project addresses this challenge by forward-modeling the polarimetric signatures of turbulent accretion flows. The student will develop a Python-based pipeline to simulate synthetic observations of the Galactic Center. First, the student will generate 3D, time-evolving Gaussian Random Fields (GRFs) which capture the inhomogeneous and anisotropic nature of accretion turbulence, incorporating differential rotation (shear) consistent with Keplerian orbital dynamics (using a python library pynoisy). Second, these 3D scalar fields will be ray-traced to integrate emissivity along the geodesics in the curved spacetime of a Kerr black hole (using a deep learning based JAX – python library bhnerf and kgeo). By varying correlation length, anisotropy, magnetic field configurations, (e.g., vertical vs. toroidal), the student will generate synthetic polarimetric light curves (Stokes I, Q, U ). The analysis will focus on two key statistical diagnostics: (1) the slope of the power spectral density (PSD), which theoretically encodes the correlation length of the underlying turbulent eddies, and (2) the topological morphology of loops in the Stokes Q-U plane. Ultimately, this work aims to determine if purely stochastic turbulence can mimic the observed coherent “hotspot” signatures, thereby providing new constraints on the physics of black hole accretion.

Skills: Essential: Strong proficiency in Python (NumPy, Pandas, Matplotlib) and a solid foundation in linear algebra, calculus, and statistics. Preferred: Familiarity with JAX/PyTorch , Fourier analysis , or introductory concepts in General Relativity.

This project will be supervised by Dr. Rohan Dahale and Prof. Aviad Levis

27. Adapting radio readout systems for Optical Single Photon detectors

Most optical photon detectors convert photons into nanosecond electrical pulses that need low-noise amplification, fast analog-to-digital conversion (ADC), precise time tagging, and fast data handling. Radio-astronomy backends already provide this stack – ultra-low noise amplifiers, analog filters, RF ADCs at a few Gsps, Field Programmable Gate Arrays (FPGA) data processing and output on high speed ethernet. In this project, we aim to build prototype single-photon detectors head that interface with radio electronics, and perform sample photon time tagging with GPS and/or atomic clock systems. In-lab detector characterization and on-sky test will follows if time and resource allow.

Preferred Skills: high frequency electronic design and/or FPGA knowledge

This project will be supervised by Dr. Albert Wai Kit Lau.

28. Developing Instrumentation to Study Atmospheric Turbulence

The performance of ground based telescopes is fundamentally limited by the effects of atmospheric turbulence and how it distorts the incoming light before it is received by the telescope. Characterizing and understanding this turbulence is key to developing new instruments and techniques for mitigating these effects and improving the performance of our observatories. In this project we would work on the design of and/or software needed to operate an instrument known as “SCIDAR” (SCIntillation Detection And Ranging). Eventually this SCIDAR will be deployed at the summit of Maunakea in Hawaii where it will regularly study the vertical structure of the atmosphere above the site.

Preferred Skills: Python programming, optics, familiarity with machine learning a plus but not required

This project will be supervised by Dr. Ryan Dungee

29. Data Processing Pipeline for MKID detectors combined with a Fourier Transform Imaging Spectrograph

Micro-Kinetic Inductance Detectors are quantum arrays of resonant circuits including superconductors which allows the detection of single photons with time sampling of the order of micro-seconds. These detectors also provide low resolution energy measurements for each photons. Raw data from these detectors consist in a large file containing 64 bits sequences for each photons encrypting their position, timing, and energy. The High-Res IFTS lab will combine one of these detectors with an Imaging Fourier Transform Spectrograph. The goal of this project is to prepare tools to handle the MKIDS dataset and produce data products for the analysis process.

This project will be supervised by: Prof. Laurie Rousseau-Nepton.