Research
I am an optical survey astronomer working at the intersection of time-domain astronomy and data science with an aim towards improving our understanding of stellar evolution. My recent efforts have focused on coupling machine learning methods with results from citizen science projects to streamline the discovery and classification of electromagnetic transients. This has enabled a special research program to study supernovae in the hours after explosion, which provides unique insight into the explosion mechanism and progenitor systems.
Working with data from the SDSS, PanSTARRS1, Gaia, PTF, ZTF, and ASAS surveys, I have: (i) built machine learning algorithms to classify stellar sources and infer their fundamental physical properties, (ii) probed the progenitor systems of Type Ia supernovae, (iii) observed super luminous supernovae to understand the death of the most massive stars, (iv) discovered outbursts from young stellar objects to examine accretion processes during star formation, and (v) identified previously overlooked, bright R Cor Bor stars to probe the late stages of low-mass stellar evolution.
For more information view my CV.
Working with data from the SDSS, PanSTARRS1, Gaia, PTF, ZTF, and ASAS surveys, I have: (i) built machine learning algorithms to classify stellar sources and infer their fundamental physical properties, (ii) probed the progenitor systems of Type Ia supernovae, (iii) observed super luminous supernovae to understand the death of the most massive stars, (iv) discovered outbursts from young stellar objects to examine accretion processes during star formation, and (v) identified previously overlooked, bright R Cor Bor stars to probe the late stages of low-mass stellar evolution.
For more information view my CV.
Publications
- The Exceptionally Luminous Type II-Linear Supernova 2008es. (2009) A.A. Miller et al.
- The Spectacular Ultraviolet Flash from the Peculiar Type Ia Supernova 2019yvq. (2020) A.A. Miller et al.
- Early Observations of the Type Ia Supernova iPTF 16abc: A Case of Interaction with Nearby, Unbound Material and/ or Strong Ejecta Mixing. (2018) A.A. Miller et al.
- A Machine-learning Method to Infer Fundamental Stellar Parameters from Photometric Light Curves. (2015) A.A. Miller et al.
- Preparing for Advanced LIGO: A Star-Galaxy Separation Catalog for the Palomar Transient Factory. (2017) A.A. Miller et al.
- Evidence for an FU Orionis-like Outburst from a Classical T Tauri Star. (2011) A.A. Miller et al.
For a complete list of publications visit NASA ADS.
Education & Outreach
I am the Program Director for the LSSTC Data Science Fellowship Program (DSFP). The DSFP is a two year training program, designed to teach skills required to analyze data collected by the Vera C. Rubin Observatory. The program consists of three, one-week schools per year over a two year period, during which fellows receive instruction and hands-on training in several data science topics. The DSFP supplements the traditional graduate curriculum, while building a fostering environment that emphasizes community building within the program.
Beyond the DSFP, I am the PI of two citizen science projects focused on time-domain astrophysics: In addition to engaging students and other members of the public, these projects are laying the foundation for citizen science in the era of the Rubin Observatory.
Beyond the DSFP, I am the PI of two citizen science projects focused on time-domain astrophysics: In addition to engaging students and other members of the public, these projects are laying the foundation for citizen science in the era of the Rubin Observatory.
Press
On a few occasions my work has been written up in the popular press:
- The spectacular UV flash of SN 2019yvq (CNN, Mashable, The Independent, Cosmos, Medium)
- DSFP trains astronomers to code so they don't get left behind (WIRED)
- Machines teach astronomers about stars (astronomy.com, JPL)
- Discovered of the "naked eye" GRB 080319b (astronomy.com, ESPN)