LStein: A new approach to visualizing sparse 2.5-dimensional data

Abstract

Visualization of high-dimensional data is crucial to retrieve all the knowledge that is contained within a dataset. Effective and informative presentation of three-dimensional data via a two-dimensional medium is challenging, especially if the dataset more closely resembles a 2.5-dimensional (2.5D) entity due to sparse sampling. We present LStein (Linking Series to envision information neatly), a novel visualization approach implemented in Python, in an attempt to solve this challenge. Inspired by the astrophysical application of displaying photometric timeseries in multiple passbands with minimal loss of information, we compare our method to traditional approaches. While astronomy -- specifically multi-passband visualization for lightcurves obtained with the Rubin Observatory -- serves as the principal driver for the design, we demonstrate that LStein can be used in any context with 2.5D datasets from radio astronomy to machine learning hyperparameter search visualization. LStein provides a complementary visualization to traditional techniques. LStein can be installed from GitHub (https://github.com/TheRedElement/LStein).

20 pages, 16 figures, accepted in Astronomy and Computing

References in corpus (12)

• LSST: from Science Drivers to Reference Design and Anticipated Data Products
• SuperNNova: an open-source framework for Bayesian, Neural Network based supernova classification
• Supernova Simulations and Strategies For the Dark Energy Survey
• The MeerKAT Telescope as a Pulsar Facility: System verification and early science results from MeerTime
• Fink, a new generation of broker for the LSST community
• The Thousand-Pulsar-Array programme on MeerKAT I: Science objectives and first results
• Mode identification and ensemble asteroseismology of 119 $\beta$ Cep stars detected by Gaia light curves and monitored by TESS
• Transient Classifiers for Fink: Benchmarks for LSST
• Validation of up to seven TESS planet candidates through multi-colour transit photometry using MuSCAT2 data
• Interchanging Interactive 3-d Graphics for Astronomy
• Early Identification of Optical Tidal Disruption Events: A science module for the Fink broker
• Real-Time Active Learning for optimised spectroscopic follow-up: Enhancing early SN Ia classification with the Fink broker