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Stellar population and stellar kinematic studies provide unique but complementary insights into how galaxies build-up their stellar mass and angular momentum 1 – 3 . A galaxy’s mean stellar age reveals when stars were formed, but provides little constraint on how the galaxy’s mass was assembled. Resolved stellar dynamics 4 trace the change in angular momentum due to mergers, but major mergers tend to obscure the effect of earlier interactions 5 . With the rise of large multi-object integral field spectroscopic surveys, such as SAMI 6 and MaNGA 7 , and single-object integral field spectroscopic surveys (for example, ATLAS 3D (ref. 8 ), CALIFA 9 , MASSIVE 10 ), it is now feasible to connect a galaxy′s star formation and merger history on the same resolved physical scales, over a large range in galaxy mass, morphology and environment 4 , 11 , 12 . Using the SAMI Galaxy Survey, here we present a combined study of spatially resolved stellar kinematics and global stellar populations. We find a strong correlation of stellar population age with location in the ( V / σ , ϵ e ) diagram that links the ratio of ordered rotation to random motions in a galaxy to its observed ellipticity. For the large majority of galaxies that are oblate rotating spheroids, we find that characteristic stellar age follows the intrinsic ellipticity of galaxies remarkably well. Α combined study of spatially resolved stellar kinematics and global stellar populations with the SAMI Galaxy Survey finds a strong correlation between the characteristic stellar population age of a galaxy and its intrinsic ellipticity.

The field of galaxy formation and evolution synthesizes the physics of baryons and dark matter to describe the origin of systems such as the Milky Way and the enormous diversity of the galaxy population. The broad variation in possible formation histories and the wide range of cosmic environments make large statistical samples of galaxies essential for identifying the important physical mechanisms that govern their formation. Starting in the early 2020s, the Large Synoptic Survey Telescope (LSST) will provide an unmatched dataset for galaxy evolution studies by observing the entire southern sky in ultraviolet, optical and near-infrared wavelengths, producing multi-epoch digital images over a 10-year nominal mission that when summed will provide the deepest, wide-angle view of our Universe ever assembled. Here, we discuss the importance of LSST for deepening our understanding of galaxy formation and evolution over cosmic time. We present some outstanding problems in the field that LSST will address, and we present a roadmap of some preparatory research efforts required to make effective use of the LSST dataset for galaxy formation science.The Large Synoptic Survey Telescope (LSST), an upcoming astronomical survey, will deeply observe the entire southern sky in a broad range of colours. We present the LSST science opportunities and technical challenges in the field of galaxy formation and evolution.

We present an overview of recent key results from the SAMI Galaxy Survey on the build-up of mass and angular momentum in galaxies across morphology and environment. The SAMI Galaxy survey is a multi-object integral field spectroscopic survey and provides a wealth of spatially-resolved, two-dimensional stellar and gas measurements for galaxies of all morphological types, with high-precision due the stable spectral resolution of the AAOmega spectrograph. The sample size of ~3000 galaxies allows for dividing the sample in bins of stellar mass, environment, and star-formation or morphology, whilst maintaining a statistical significant number of galaxies in each bin. By combining imaging, spatially resolved dynamics, and stellar population measurements, our result demonstrate the power of utilising integral field spectroscopy on a large sample of galaxies to further our understanding of physical processes involved in the build-up of stellar mass and angular momentum in galaxies.

We present the phase one report of the Bright Star Subtraction (BSS) pipeline for the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST). This pipeline is designed to create an extended PSF model by utilizing observed stars, followed by subtracting this model from the bright stars present in LSST data. Running the pipeline on Hyper Suprime-Cam (HSC) data shows a correlation between the shape of the extended PSF model and the position of the detector within the camera's focal plane. Specifically, detectors positioned closer to the focal plane's edge exhibit reduced circular symmetry in the extended PSF model. To mitigate this effect, we present an algorithm that enables users to account for the location dependency of the model. Our analysis also indicates that the choice of normalization annulus is crucial for modeling the extended PSF. Smaller annuli can exclude stars due to overlap with saturated regions, while larger annuli may compromise data quality because of lower signal-to-noise ratios. This makes finding the optimal annulus size a challenging but essential task for the BSS pipeline. Applying the BSS pipeline to HSC exposures allows for the subtraction of, on average, 100 to 700 stars brighter than 12th magnitude measured in g-band across a full exposure, with a full HSC exposure comprising ~100 detectors.

Low surface brightness substructures around galaxies, known as tidal features, are a valuable tool in the detection of past or ongoing galaxy mergers, and their properties can answer questions about the progenitor galaxies involved in the interactions. The assembly of current tidal feature samples is primarily achieved using visual classification, making it difficult to construct large samples and draw accurate and statistically robust conclusions about the galaxy evolution process. With upcoming large optical imaging surveys such as the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST), predicted to observe billions of galaxies, it is imperative that we refine our methods of detecting and classifying samples of merging galaxies. This paper presents promising results from a self-supervised machine learning model, trained on data from the Ultradeep layer of the Hyper Suprime-Cam Subaru Strategic Program optical imaging survey, designed to automate the detection of tidal features. We find that self-supervised models are capable of detecting tidal features, and that our model outperforms previous automated tidal feature detection methods, including a fully supervised model. An earlier method applied to real galaxy images achieved 76% completeness for 22% contamination, while our model achieves considerably higher (96%) completeness for the same level of contamination. We emphasise a number of advantages of self-supervised models over fully supervised models including maintaining excellent performance when using only 50 labelled examples for training, and the ability to perform similarity searches using a single example of a galaxy with tidal features.

We use machine learning to measure the intracluster light (ICL) fractions of 177 galaxy groups and clusters identified from Hyper Suprime-Cam Subaru Strategic Program imaging to explore how the ICL varies with the properties of its host cluster. We study the variation in ICL fraction with host cluster redshift, halo mass, and magnitude gap to investigate how the ICL develops over time, in various cluster environments, and with cluster relaxation. We find that there is a decreasing correlation with redshift (Spearman correlation \\(r_S=-0.604\\), p-value \\(=9\\times10^{-10}\\)), however this can be plausibly accounted for by including the effects of cosmological surface brightness dimming and the passive aging of stellar populations. There is a weak negative correlation with halo mass (\\(r_S=-0.330\\), p-value \\(=8\\times 10^{-5}\\)) where ICL fractions are higher in lower halo mass groups than higher halo mass clusters. We also find that there is a marginal positive correlation with magnitude gap (\\(r_S=0.226\\), p-value = 0.01), indicating that relaxed clusters are more likely to host higher ICL fractions. These results are consistent with a scenario where the dominant formation mechanism of the ICL is galaxy-galaxy interactions such as tidal stripping, and demonstrates the capability of the method to easily construct large samples and study large-scale trends in the ICL fraction.

Low surface brightness substructures around galaxies, known as tidal features, are a valuable tool in the detection of past or ongoing galaxy mergers. Their properties can answer questions about the progenitor galaxies involved in the interactions. This paper presents promising results from a self-supervised machine learning model, trained on data from the Ultradeep layer of the Hyper Suprime-Cam Subaru Strategic Program optical imaging survey, designed to automate the detection of tidal features. We find that self-supervised models are capable of detecting tidal features and that our model outperforms previous automated tidal feature detection methods, including a fully supervised model. The previous state of the art method achieved 76% completeness for 22% contamination, while our model achieves considerably higher (96%) completeness for the same level of contamination.

The study of low surface brightness light in large, deep imaging surveys is still uncharted territory as automated data reduction pipelines over-subtract or eliminate this light. Using archival data of the Abell 85 cluster of galaxies taken with Hyper Suprime-Cam on the Subaru Telescope, we show that using careful data processing can unveil the diffuse light within the cluster, the intracluster light. We reach surface brightness limits of \\(\\mu_{g}^{limit}\\)(3\\(\\sigma\\), 10\"x10\") = 30.9 mag/arcsec\\(^2\\), and \\(\\mu_{i}^{limit}\\)(3\\(\\sigma\\), 10\"x10\") = 29.7 mag/arcsec\\(^2\\). We measured the radial surface brightness profiles of the brightest cluster galaxy out to the intracluster light (radius \\(\\sim215\\) kpc), for the g and i bands. We found that both the surface brightness and the color profiles become shallower beyond \\(\\sim75\\) kpc suggesting that a distinct component, the intracluster light, starts to dominate at that radius. The color of the profile at \\(\\sim100\\) kpc suggests that the buildup of the intracluster light of Abell 85 occurs by the stripping of massive (\\(\\sim10^{10}M_{\\odot}\\)) satellites. The measured fraction of this light ranges from 8% to 30% in g, depending on the definition of intracluster light chosen.

The intracluster light (ICL) is an important tracer of a galaxy cluster's history and past interactions. However, only small samples have been studied to date due to its very low surface brightness and the heavy manual involvement required for the majority of measurement algorithms. Upcoming large imaging surveys such as the Vera C. Rubin Observatory's Legacy Survey of Space and Time are expected to vastly expand available samples of deep cluster images. However, to process this increased amount of data, we need faster, fully automated methods to streamline the measurement process. This paper presents a machine learning model designed to automatically measure the ICL fraction in large samples of images, with no manual preprocessing required. We train the fully supervised model on a training dataset of 50,000 images with injected artificial ICL profiles. We then transfer its learning onto real data by fine-tuning with a sample of 101 real clusters with their ICL fraction measured manually using the surface brightness threshold method. With this process, the model is able to effectively learn the task and then adapt its learning to real cluster images. Our model can be directly applied to Hyper Suprime-Cam images, processing up to 500 images in a matter of seconds on a single GPU, or fine-tuned for other imaging surveys such as LSST, with the fine-tuning process taking just 3 minutes. The model could also be retrained to match other ICL measurement methods. Our model and the code for training it is made available on GitHub.

We conduct a comparison of the merging galaxy populations detected by a sample of visual identification of tidal features around galaxies as well as spectroscopically-detected close pairs of galaxies to determine whether our method of selecting merging galaxies biases our understanding of galaxy interactions. Our volume-limited parent sample consists of 852 galaxies from the Galaxy And Mass Assembly (GAMA) survey in the redshift range \\(0.04 \\leq z \\leq 0.20\\) and stellar mass range \\(9.50 \\leq\\) log\\(_{10}(M_{\\star}/\\rm{M}_{\\odot})\\leq 11.0\\). We conduct our comparison using images from the Ultradeep layer of the Hyper Suprime-Cam Subaru Strategic Program (HSC-SSP) to visually-classify galaxies with tidal features and compare these to the galaxies in the GAMA spectroscopic close-pair sample. We identify 198 galaxies possessing tidal features, resulting in a tidal feature fraction \\(f_{\\rm{tidal}}\\) = 0.23 \\(\\pm\\) 0.02. We also identify 80 galaxies involved in close pairs, resulting in a close pair fraction \\(f_{\\rm{pair}}\\) = 0.09 \\(\\pm\\) 0.01. Upon comparison of our tidal feature and close pair samples we identify 42 galaxies that are present in both samples, yielding a fraction \\(f_{\\rm{both}}\\) = 0.05 \\(\\pm\\) 0.01. We find evidence to suggest that the sample of close pairs of galaxies is more likely to detect early-stage mergers, where two separate galaxies are still visible, and the tidal feature sample detects later-stage mergers, where only one galaxy nucleus remains visible. The overlap of the close pair and tidal feature samples likely detect intermediate-stage mergers. Our results are in good agreement with the predictions of cosmological hydrodynamical simulations regarding the populations of merging galaxies detected by close pair and tidal feature samples.