We present a spectroscopic study of the tidal tails and core of the Milky Way satellite Tucana III, collectively referred to as the Tucana III stream, using the 2dF+AAOmega spectrograph on the Anglo-Australian Telescope and the IMACS spectrograph on the Magellan/Baade Telescope. In addition to recovering the brightest 9 previously known member stars in the Tucana III core, we identify 22 2 Li et al.members in the tidal tails. We observe strong evidence for a velocity gradient of 8.0±0.4 km s −1 deg −1 over at least 3 • on the sky. Based on the continuity in velocity we confirm that the Tucana III tails are real tidal extensions of Tucana III. The large velocity gradient of the stream implies that Tucana III is likely on a radial orbit. We successfully obtain metallicities for 4 members in the core and 12 members in the tails. We find that members close to the ends of the stream tend to be more metalpoor than members in the core, indicating a possible metallicity gradient between the center of the progenitor halo and its edge. The spread in metallicity suggests that the progenitor of the Tucana III stream is likely a dwarf galaxy rather than a star cluster. Furthermore, we find that with the precise photometry of the Dark Energy Survey data, there is a discernible color offset between metal-rich disk stars and metal-poor stream members. This metallicity-dependent color offers a more efficient method to recognize metal-poor targets and will increase the selection efficiency of stream members for future spectroscopic follow-up programs on stellar streams.

We perform consistent reductions and measurements for three ultra-faint dwarf galaxies (UFDs): Boötes I, Leo IV and Leo V. Using the public archival data from the GIRAFFE spectrograph on the Very Large Telescope (VLT), we locate new members and provide refined measurements of physical parameters for these dwarf galaxies. We identify nine new Leo IV members and four new Leo V members, and perform a comparative analysis of previously discovered members. Additionally, we identify one new binary star in both Leo IV and Leo V. After removing binary stars, we recalculate the velocity dispersions of Boötes I and Leo IV to be 5.1 +0.7 −0.8 and 3.4 +1.3 −0.9 km s −1 , respectively; We do not resolve the Leo V velocity dispersion. We identify a weak velocity gradient in Leo V that is ∼4× smaller than the previously calculated gradient and that has a corresponding position angle which differs from the literature value by ∼120 deg. Combining the VLT data with previous literature, we re-analyze the Boötes I metallicity distribution function and find that a model including infall of pristine gas while Boötes I was forming stars best fits the data. Our analysis of Leo IV, Leo V and other UFDs will enhance our understanding of these enigmatic stellar populations and contribute to future dark matter studies. This is the first in a series of papers examining thirteen UDFs observed with VLT/GIRAFFE between 2009 and 2017. Similar analyses of the remaining ten UFDs will be presented in forthcoming papers.

In astronomy, neural networks are often trained on simulation data with the prospect of being used on telescope observations. Unfortunately, training a model on simulation data and then applying it to instrument data leads to a substantial and potentially even detrimental decrease in model accuracy on the new target dataset. Simulated and instrument data represent different data domains, and for an algorithm to work in both, domain-invariant learning is necessary. Here we employ domain adaptation techniques— Maximum Mean Discrepancy (MMD) as an additional transfer loss and Domain Adversarial Neural Networks (DANNs)— and demonstrate their viability to extract domain-invariant features within the astronomical context of classifying merging and non-merging galaxies. Additionally, we explore the use of Fisher loss and entropy minimization to enforce better in-domain class discriminability. We show that the addition of each domain adaptation technique improves the performance of a classifier when compared to conventional deep learning algorithms. We demonstrate this on two examples: between two Illustris-1 simulated datasets of distant merging galaxies, and between Illustris-1 simulated data of nearby merging galaxies and observed data from the Sloan Digital Sky Survey. The use of domain adaptation techniques in our experiments leads to an increase of target domain classification accuracy of up to ${\sim }20\%$. With further development, these techniques will allow astronomers to successfully implement neural network models trained on simulation data to efficiently detect and study astrophysical objects in current and future large-scale astronomical surveys.

Wide-field astronomical surveys are often affected by the presence of undesirable reflections (often known as "ghosting artifacts" or "ghosts") and scattered-light artifacts. The identification and mitigation of these artifacts is important for rigorous astronomical analyses of faint and low-surface-brightness systems. However, the identification of ghosts and scattered-light artifacts is challenging due to a) the complex morphology of these features and b) the large data volume of current and near-future surveys. In this work, we use images from the Dark Energy Survey (DES) to train, validate, and test a deep neural network (Mask R-CNN) to detect and localize ghosts and scattered-light artifacts. We find that the ability of the Mask R-CNN model to identify affected regions is superior to that of conventional algorithms and traditional convolutional neural networks methods. We propose that a multi-step pipeline combining Mask R-CNN segmentation with a classical CNN classifier provides a powerful technique for the automated detection of ghosting and scattered-light artifacts in current and near-future surveys.