- Ongoing projects:
- Generative modelling with neural-network based Conditional Normalizing Flows to remove scattered light artefacts in NASA’s Transiting Exoplanet Survey Satellite’s Full-frame images
- Semi-supervised and self-supervised learning for modelling time-series data
- Anomaly detection in real-time
- Real-time prediction of the age of astrophysical transients
- Exoplanet light curve classification
Anomaly Detection in real-time for astronomical transients
- Using Bayesian recurrent neural networks to encode astrophysical transients. Predicting anomalies with predictive modelling or Isolation Forests
- Paper on “Real-Time Detection of Anomlaies in Large-Scale Transient Surveys” in the Monthly Notices of the Astronomical Society
- Paper in the Astronomy workshop at the Neurips 2021 “Real-time Detection of Anomalies in Multivariate Time Series of Astronomical Data“
- Developed App to track and compare COVID-19 across countries:
- Developed App with Nick Taylor (Plant Scientist PhD, University of Cambridge) to help public understanding of COVID-19 modeling and control measures
RAPID: Early classification of photometric transients for LSST and ZTF
- Paper on “RAPID: Early Classification of Explosive Transients using Deep Learning” (Muthukrishna et al., 2019b)
- Open source software: https://astrorapid.readthedocs.io
pip install astrorapidto get started!
- Finalist in the ongoing “Best Astrostatistics Student Paper Award” by the ASA Astrostatistics Interest Group.
Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC)
- Working with the PLAsTiCC collaboration to develop a community-wide transient classification challenge on Kaggle (kaggle.com/c/PLAsTiCC-2018).
- Paper on “Models and Simulations for the Photometric LSST Astronomical Time Series Classification Challenge (PLAsTiCC)” (Kessler, 2019b).
- Paper on “The Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC): Selection of a performance metric for classification probabilities balancing diverse science goals” (Malz et al. 2019).
- Note on “The Photometric LSST Astronomical Time-series Classification Challenge (PLAsTiCC): Data set” (The PLAsTiCC Team, 2018).
DASH: Deep learning for the automated classification of supernovae
- Paper on “DASH: Deep Learning for the Automated Spectral Classification of Supernovae and their Hosts” (Muthukrishna et al., 2019a)
pip install astrodashto get started!
- My thesis has won several prizes for its novel approach.
- Bok Prize Highly Commended (Awarded to the top three astronomy-related theses from all Honours/Masters students in Australia)
- IEEE Student Thesis Prize (Awarded to the top ranked Information Technology and Electrical Engineering thesis from all undergraduate students in Queensland)
- GBST Best Software Project (Awarded to the top ranked software-related thesis at the University of Queensland)
Dark Energy Survey
- Paper on “OzDES multifibre spectroscopy for the Dark Energy Survey: Three year results and first data release” (Childress et al., 2017)
- Paper on “First Cosmology Results using Type Ia Supernovae from the Dark Energy Survey: Constraints on Cosmological Parameters” (DES Collaboration, 2019)
- Paper on “First Cosmology Results Using Type Ia Supernovae From the Dark Energy Survey: Survey Overview and Supernova Spectroscopy” (D’Andrea, 2019)
- Paper on “First Cosmology Results using Type Ia Supernova from the Dark Energy Survey: Simulations to Correct Supernova Distance Biases” (Kessler, 2019a)
- Paper on “First Cosmology Results Using Type Ia Supernovae From the Dark Energy Survey: Analysis, Systematic Uncertainties, and Validation” (Brout, 2019)
SkyMapper Transient Survey
- Working with the SkyMapper team to hunt for new supernovae! We are using the 1.3m telescope at Siding Spring Observatory to study supernovae and other transient objects across ~1000 square degrees of the sky.
- We recently launched the citizen-science project to search for Planet 9 (launched on BBC’s Stargazing Live TV event). The supernova citizen-science search was also recently launched and is live here.
- The SkyMapper Team includes Brian Schmidt, Anais Möller, Brad Tucker, Ashley Ruiter, Ivo Seitenzahl, Seo-Won Chang, Bonnie Zhang, Fiona Panther, Daniel Muthukrishna, Natalia E. Sommer, Ryan Ridden-Harper, Patrick Armstrong, Nataliea Lowson, Richard Scalzo, Fang Yuan, Mike Childress, Chris Onken, Chris Wolf.
Model-independent cosmology with SNIa and BAO data
- Paper on “A cosmographic analysis of the transition to acceleration using SN-Ia and BAO” (Muthukrishna & Parkinson, 2016)
Modelling emission line profiles with multiple Gaussian components
Design of a portable digital oscilloscope
- With Benjamin Jarrett and Ovindu Manawaduge