GoKawiilData
The HST Advanced Camera for Surveys (ACS) images use the Wide Field Channel (WFC) in the F555W and F814W filters, have an angular resolution of 0.1″ and reach exposure times of 2,406 s and 2,439 s, respectively. The observations were carried out in September 2022 under HST programme ID 16890 (ref. 52) and the resulting images of UGC 9050-Dw1 were presented in ref. 17. The images have been calibrated through the standard HST CALACS pipeline and are available on the STScI/MAST archive.
CFHT has observed the same region of the sky using MegaCam as part of its Legacy Survey in 2005 and UGC 9050-Dw1 was first identified and selected in a semi-automated search for diffuse dwarfs19. The area containing UGC 9050-Dw1 is visible in both the W3-1-3 and W3-2-3 fields of the survey, which contains bands u, g, r, i and z, so a total of ten CFHT images are available. Because of the signal-to-noise levels, we are not able to confidently identify the stream-like feature in the u-band or z-band images. Integration times, observation depth and seeing for the CFHT data are published in ref. 53. The CFHT data, which have been calibrated through the MegaPipe pipeline, are available through the Canadian Astronomy Data Centre.
For visual purposes, we have smoothed the HST and CFHT images using a Gaussian kernel (with σ = 3 and 1.5 pixels, respectively) and we plot the area of interest in all available bands in Extended Data Fig. 1. All data analysis is performed on unsmoothed data.
Data analysis
The feature was initially detected by eye, but when we apply the rolling Hough transform54 with a range of (hyper)parameters, the code consistently returns both the location and curvature of the stream candidate. We use this as a validation of the initial by-eye detection.
In Extended Data Fig. 2 (left), we rotate the images into a coordinate frame aligned with the stream, in which (ϕ 1 , ϕ 2 ) represents stream longitude and latitude55,56. We draw masks around the stream by following a polynomial fit to points selected manually in the F814W image, creating a curved shape with constant width, excluding the GC candidate. We duplicate these on both sides to mask the background areas. To measure the width, we sample slices perpendicular to the track curvature in steps of pixel size along this fit. We collapse the resulting area along the fitted track, calculating the mean brightness of the stream candidate and its surrounding areas. The right panels of Extended Data Fig. 2 depict the perpendicular brightness profile, consisting of the average brightnesses measured parallel to the curved stream track. This is done in six segments to check for any single dominating contaminant. We fit a Gaussian with a linear offset to the perpendicular brightness profile for each segment, as well as the whole stream. This way of sampling the surrounding area causes the inner part of the curve to be oversampled compared with the outer curve and thus, by construction, it has a lower error and dominates the fit. We therefore use the average of all standard deviations as uniform errors. The standard deviation of the Gaussian provides an estimate of the width of the stream, with uncertainties on the fitted parameter. Because σ describes the deviation from the mean, μ, the total width is twice σ. We denote this width, measured from μ − σ to μ + σ, as w ±σ . The width that includes 95% of the Gaussian area, corresponding to 2σ from the mean, we call w ±2σ . The physical width is affected by the instrument point spread function (PSF), which we account for by subtracting the observational dispersion in quadrature. We use the full width at half maximum for HST of 0.1″.
The seeing of the CFHT observation (0.88″) is comparable with the width of the feature in the CFHT images (w ±σ = 1.1″). Therefore, we use the HST width in subsequent analysis.
There is a brighter object located next to the track at around ϕ 1 = −4, which increases the measured width. If we only use the track up to that point, it yields a width of w ±σ = 52 pc instead. On the other hand, the faintness of the object means that we might only be sensitive to the brightest central part of our stream candidate, which would lead to an underestimated width. In Extended Data Fig. 6, we illustrate that the simulated GC streams show up with similar apparent width when injected into the data.
To quantify the prominence of the stream candidate, we sum the counts in the on-stream mask and in the off-stream background masks. We subtract the mean background from the signal and compare this with the standard deviation of the backgrounds: \(({f}_{{\rm{stream}}}-\,{\overline{f}}_{{\rm{background}}})/{\sigma }_{{\rm{background}}}\).
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