Processing GOTO data with the Rubin Observatory LSST Science Pipelines I: Production of coadded frames

Abstract

The past few decades have seen the burgeoning of wide-field, high-cadence surveys, the most formidable of which will be the Legacy Survey of Space and Time (LSST) to be conducted by the Vera C. Rubin Observatory. So new is the field of systematic time-domain survey astronomy; however, that major scientific insights will continue to be obtained using smaller, more flexible systems than the LSST. One such example is the Gravitational-wave Optical Transient Observer (GOTO) whose primary science objective is the optical follow-up of gravitational wave events. The amount and rate of data production by GOTO and other wide-area, high-cadence surveys presents a significant challenge to data processing pipelines which need to operate in near-real time to fully exploit the time domain. In this study, we adapt the Rubin Observatory LSST Science Pipelines to process GOTO data, thereby exploring the feasibility of using this ‘off-the-shelf’ pipeline to process data from other wide-area, high-cadence surveys. In this paper, we describe how we use the LSST Science Pipelines to process raw GOTO frames to ultimately produce calibrated coadded images and photometric source catalogues. After comparing the measured astrometry and photometry to those of matched sources from PanSTARRS DR1, we find that measured source positions are typically accurate to subpixel levels, and that measured L-band photometries are accurate to $\sim50$ mmag at $m_L\sim16$ and $\sim200$ mmag at $m_L\sim18$ . These values compare favourably to those obtained using GOTO’s primary, in-house pipeline, gotophoto, in spite of both pipelines having undergone further development and improvement beyond the implementations used in this study. Finally, we release a generic ‘obs package’ that others can build upon, should they wish to use the LSST Science Pipelines to process data from other facilities.

Processing GOTO survey data with the Rubin Observatory LSST Science Pipelines II: Forced Photometry and lightcurves

“…Included in the LSST stack is a suite of software that is capable of conducting both image differencing and forced photometry. This includes the production of a set of reference images and catalogues (see Mullaney et al 2021) and, as we describe next, the tasks required to perform forced photometry based on the positions of sources in the aforementioned reference catalogue. The LSST stack also includes software capable of conducting image differencing, although this is beyond the scope of this study.…”

“…The data presented in this paper are the result of the nightly processing of raw images observed between the 24 February and the 25 October 2019. Coadded images, from which reference catalogues are constructed, were produced by combining frames from dates spanning the 24 February to 12 March 2019 (see Mullaney et al 2021). This selection resulted in reference images and catalogues spanning 2h RA 20h and −20deg Dec. 90deg, which represents roughly 50% of the sky observable from GOTO's location on La Palma and avoids the densest parts of the Galactic plane (see Figure 1).…”

“…On coadding these three exposure, we achieve an L-band 5σ magnitude limit of ∼ 20mag (see Section 4). While we describe the processing of GOTO images with the LSST software in detail in Mullaney et al (2021), we feel it is important to highlight some of the key processing steps using the LSST stack here in order to provide some context. First, the raw data are ingested into a database using the header information and master calibration frames that are produced by combining the individual bias, dark, and flat frames.…”

“…However, on visual inspection of the charge-coupled device (CCD) images, it is clear that bad pixels represent far fewer than one in ten thousand pixels, and so we are confident that the impact of not including bad pixel masks has a minimal impact on our results. In this paper, we present the results from forced photometry using, as references, catalogues generated by running the LSST stack's detection algorithm on the coadded frames, and adopting a 5σ detection threshold (Mullaney et al 2021). In the following subsection, we present the method we used to perform forced photometry on GOTO images using the LSST stack.…”

“…However, while GOTO's survey depth is shallower (∼ 20 mag limit for a 3 min exposure in dark time) than the LSST, both have a similar single-pointing field-of-views and cadences, meaning that the LSST stack is a viable alternative pipeline for non-primary science data products. In order to process GOTO data using the LSST stack, we have developed our own 'obs package', obs_goto, which is described in more detail in Mullaney et al (2021).…”