Hello, I'm Adrian Crawford!​ (they/she)

Current Work

My most recent work--Peaky Finders, https://arxiv.org/abs/2503.03735--created the first ever early-time population statistics that described the double-peaked light curves of Type IIb supernovae (SNe IIb). These light curves hold more clues into the progenitor system than typical single-peaked light curves. The partially stripped natured of SNe IIb make them an interesting probe into mass loss in massive stars. We plan expand this work into other multi-peaked light curves of transients that we can find in our night sky as we anticipate the nightly deluge that the Vera C. Rubin Legacy Survey of Space and Time (Rubin LSST) will bring later in 2024. 

It's necessary for Rubin LSST to quickly and reliably identify a millions of transient events each night, so that astronomers know what objects to focus their time and energy on and where to send our limited follow-up resources. Currently, most training data sets for LSST on supernovae use simulated data, but theory doesn't always match reality, so it's important to create data-driven training sets that consist of real observations of supernovae. 
I am currently a 4th year Ph.D. candidate at the University of Virginia. I am working with Maryam Modjaz as part of the Modjaz Explosions and Transients Astronomy Lab (METAL). I've done work on the very small (white dwarfs), to the very largest (galaxy clusters), and now (some of) the most energetic (supernovae). I have experience using theoretical, analytic, and computational methods in my research.

​I graduated from UT Austin in May 2021 with a B.S. in Astronomy and a B.S. in Physics. I earned my M.S. in May 2023 on the way towards my Ph.D. 

Previous Research

My first PhD project was studying the effects of mixing during galaxy cluster mergers and its effect on the entropy. To do this, I created galaxy clusters models and analytical descriptions and predictions of their entropy during merger events. In this research I used C++ to create the cluster models and Python to script the models and perform data analysis and visualizations. This work was in performed in collaboration with Dr. Craig Sarazin and is currently in draft for publication.
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My previous undergraduate work, on galaxy cluster shapes, was published in the Monthly Notices of the Royal Astronomical Society (MNRAS) under the title "Brightest Cluster Galaxies Trace Weak Lensing Mass Bias and Halo Triaxiality in The Three Hundred Project". In this project, I utilized Python (Pandas, NumPy, SciPy, sk-learn, & matplotlib) to:
  • Clean the dataset by identifying messy and unusable images through a system of flags developed on a subset of the full data set
  • Assess the accuracy of three different shape measurements by computing offsets from true values and creating flags to tag inaccurate measurements
  • Compute the Spearman Correlation Coefficient of multiple pairings of shape measurements with bootstrapping techniques
  • Develop various data visualizations showcasing the shape measurements and correlation strengths using matplotlib
In addition to our published paper, I also won an award for my poster presentation at the American Astronomical Society's 237th meeting. This research was part of an NSF REU at the University of Michigan working with Dr. Camille Avestruz. 
Research Areas
Current: Fast Transients: Supernovae; Population Statistics; Machine Learning
Past: Galaxy Clusters: modeling, entropy, simulations; White Dwarfs: pulsations, modeling
Technical Skills
Languages: Python, C++, Unix/Bash, SQL, Mathematica, LaTeX
Software and Libraries: Pandas, NumPy, SciPy, Matplotlib, Sci-Kit Learn, Excel, Google Sheets