Data Compression for the Tomo-e Gozen with Low-rank Matrix Approximation

TL;DR

This paper proposes a low-rank matrix approximation method for data compression in high-cadence optical surveys, effectively reducing data size while preserving transient astronomical signals.

Contribution

It introduces a novel low-rank matrix approximation technique tailored for astronomical data compression, demonstrating its effectiveness on Tomo-e Gozen prototype data.

Findings

Achieved about 10-fold data compression without losing transient signals

Recovered point source intensities accurately from compressed data

Processing speed is sufficient for real-time application

Abstract

Optical wide-field surveys with a high cadence are expected to create a new field of astronomy, so-called "movie astronomy," in the near future. The amount of data of the observations will be huge, and hence efficient data compression will be indispensable. Here we propose a low-rank matrix approximation with sparse matrix decomposition as a promising solution to reduce the data size effectively, while preserving sufficient scientific information. We apply one of the methods to the movie data obtained with the prototype model of the Tomo-e Gozen mounted on the 1.0-m Schmidt telescope of Kiso Observatory. Once the full-scale observation of the Tomo-e Gozen commences, it will generate ~30 TB of data per night. We demonstrate that the data are compressed by a factor of about 10 in size without losing transient events like optical short transient point-sources and meteors. The intensity of point sources can be recovered from the compressed data. The processing runs sufficiently fast, compared with the expected data-acquisition rate in the actual observing runs.