Research Interests
I am a Royal Society Newton International Fellow working in astrostatistics and astroinformatics. I am part of Prof. Stephen Smartt’s group, focusing on end-to-end processing for wide-field, time-domain optical surveys.
I develop methods and tools that turn raw astronomical images into science-ready products— from calibration and astrometry through reliable object detection, classification, and cross-matching to alerting and spectral analysis. I work primarily with data from , , , and , and I contribute to the broker, which will provide community access to alerts from the Rubin Observatory’s Legacy Survey of Space and Time (LSST).
Current Projects
Satellite trails in survey imaging (ASTA).
I lead ASTA, an automated system that detects and characterises satellite trails in wide-field optical images. Trained on annotated MeerLICHT data and refined with a Probabilistic Hough Transform, ASTA produces pixel-level masks that flag trail-affected regions so source detection, alerting and photometry avoid spurious measurements. We have run ASTA on about 200,000 full-field MeerLICHT images and are analysing five years of MeerLICHT/BlackGEM observations to quantify how often satellites pass over the telescopes and when and where they affect survey operations.
Spectral analysis at scale ().
After an astronomical transient (e.g. a supernova) is detected in survey imaging, the next critical step is spectroscopy to determine its type and redshift. To 91̽»¨ that stage, I develop and maintain SNID-SAGE, a modern Python toolkit for spectral analysis built on the classic SNID cross-correlation method, extending it with an interactive UI, clustering-aided classification suggestions and LLM-assisted workflows.
Large language models for survey automation.
I’m studying how multimodal LLMs can replace or complement CNN-based stages in survey pipelines—real–bogus filtering in difference imaging, light curve analysis, and spectra characterisation—by producing human-readable textual outputs and structured suggestions rather than only binary scores. The aim is interpretable, auditable outputs that plug into alert brokers and follow-up workflows, improving classification and documentation across surveys.