Orateur
Description
Extracting the full scientific potential of large photometric surveys demands richer data visualization than current methods provide. The Vera C. Rubin Observatory's LSST exemplifies this challenge: its filter-based sampling of the light spectrum is intentionally sparse, yet the subtle details encoded in filter spacing, overlap, and sensitivity profiles carry critical information that standard lightcurve visualisation discards. Recovering this information unlocks new opportunities across high-impact science cases such as supernova classification, asteroseismology, and exoplanet research.
I present LStein (Linking Series to envision information neatly), a novel, open-source, deterministic visualisation. LStein addresses this visualization challenge by including the missing information through projection: A set of lightcurves is placed in a combined angular coordinate system using azimuthal offsets to encode the sparse wavelength dimension. Using LStein one can gain intuitive insight into the relation between passbands while still showing all individual lightcurves in a comparable manner. This enables the researcher to include the wavelength information in interpretations of the physical nature of signals. I will demonstrate the capabilities of LStein by applying it to the first Rubin alerts. Examples include supernovae, and variable stars from Fink broker. I will conclude by outlining extensions to other fields such as radio astronomy and spectral analysis.