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Abstract We conducted an in-depth analysis of candidate member stars located in the peripheries of three ultra-faint dwarf (UFD) galaxy satellites of the Milky Way (MW): Boötes I (Boo1), Boötes II (Boo2), and Segue I (Seg1). Studying these peripheral stars has previously been difficult due to contamination from the MW foreground. We usedu-band photometry from the Dark Energy Camera (DECam) to derive metallicities to efficiently select UFD candidate member stars. This approach was validated on Boo1, where we identified both previously known and new candidate member stars beyond five half-light radii. We then applied a similar procedure to Boo2 and Seg1. Our findings hinted at evidence for tidal features in Boo1 and Seg1, with Boo1 having an elongation consistent with its proper motion and Seg1 showing some distant candidate stars, a few of which are along its elongation and proper motion. We find two Boo2 stars at large distances consistent with being candidate member stars. Using a foreground contamination rate derived from the Besançon Galaxy model, we ascribed purity estimates to each candidate member star. We recommend further spectroscopic studies on the newly identified high-purity members. Our technique offers promise for future endeavors to detect candidate member stars at large radii in other systems, leveraging metallicity-sensitive filters with the Legacy Survey of Space and Time and the new, narrowband Ca HK filter on DECam. 

The use of data and analytics in the field of research administration remains in explorations stages for many, if not most, higher education institutions. That is evidenced not only by the great demand and attendance seen for such sessions at NCURA’s Annual Meetings in 2022 and 2023, but also via prior research. This presentation will highlight the efforts implementing data-informed decision making based on research administration metrics, analytics, and dashboard examples. Emphasis will be placed on that you can’t manage what you can’t measure. The data analytics team at Emory supports all visions of our research administration leadership. Challenges, pain points, and lessons learned will be shared. Reasons for implementing collecting data and metrics will be shown. These include improving operational efficiencies, stakeholder satisfaction (e.g., faculty), as well as providing analytical insights to decision makers. The benefits of such initiatives will also be depicted with examples of successfully implemented metrics and analytics, including dashboard examples. 

This session is a basic introduction to descriptive statistics for research administration professionals. No worries! Excel will be used throughout, nothing will be calculated by hand. Averages (means), medians, standard deviations, correlations and other basic statistical concepts will be explained using only research administration data as context. A sample research administration data set will be provided and Excel will be used to analyze the data. Some sample charts and best practices in creating these charts will also be shown. Again using Excel. This is the perfect session for anyone in research administration whose roles include analyzing data using descriptive statistics. Whether you are new to statistics or whether you can't remember a statistics class you might have taken at some point. Basic knowledge of Excel would be helpful for this session. 

This session will present on how to develop basic research administration dashboards using Tableau. A sample research administration dataset will be provided in Excel for anyone who would like to work along. Using that data set the presenter will show step by step how to import the data into Tableau and how to create basic worksheets and dashboards. Calculations and filters will be shown also. Attendees should download a trial version of Tableau the same week as the conference if they would like to work along the presenter. 

We predict the sensitivity of the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) to faint, resolved Milky Way satellite galaxies and outer-halo star clusters. We characterize the expected sensitivity using simulated LSST data from the LSST Dark Energy Science Collaboration (DESC) Data Challenge 2 (DC2) accessed and analyzed with the Rubin Science Platform as part of the Rubin Early Science Program. We simulate resolved stellar populations of Milky Way satellite galaxies and outer-halo star clusters over a wide range of sizes, luminosities, and heliocentric distances, which are broadly consistent with expectations for the Milky Way satellite system. We inject simulated stars into the DC2 catalog with realistic photometric uncertainties and star/galaxy separation derived from the DC2 data itself. We assess the probability that each simulated system would be detected by LSST using a conventional isochrone matched-filter technique. We find that assuming perfect star/galaxy separation enables the detection of resolved stellar systems with $M_V$= 0 mag and $r_{1/2}$= 10 pc with >50% efficiency out to a heliocentric distance of ~250 kpc. Similar detection efficiency is possible with a simple star/galaxy separation criterion based on measured quantities, although the false positive rate is higher due to leakage of background galaxies into the stellar sample. When assuming perfect star/galaxy classification and a model for the galaxy-halo connection fit to current data, we predict that 89 +/- 20 Milky Way satellite galaxies will be detectable with a simple matched-filter algorithm applied to the LSST wide-fast-deep data set. Different assumptions about the performance of star/galaxy classification efficiency can decrease this estimate by ~7-25%, which emphasizes the importance of high-quality star/galaxy separation for studies of the Milky Way satellite population with LSST. 

Explore the transformative potential of generative AI in university-research administration. As universities strive to enhance research capabilities and support a culture of innovation, the need for efficient and effective management of sponsored programs has become paramount. This presentation will share lessons from deploying a sponsored programs’ Large Language Model Chatbot and how it optimizes research administration operations and unlocks opportunities. By harnessing the power of GenAI, a university office of sponsored programs chatbot can develop training materials, policies and SOPs. It can offer immediate support and guidance by analyzing queries and providing real-time responses, empowering staff members to overcome challenges and reducing time on tasks. It can enhance productivity and job satisfaction. An OSP Chat will allow research administrators to focus on higher-value activities, such as strategic planning, relationship building and facilitating research collaboration resulting in improved operational effectiveness and increased capacity to support research excellence. This improves the quality of research administration services. This presentation will highlight the collaboration of AI experts, research administrators and stakeholders to tailor LLM to the research administration's needs, maximizing staff benefits and optimizing research support. 

Abstract We report the results of the deepest search to date for dwarf galaxies around NGC 3109, a barred spiral galaxy with a mass similar to that of the Small Magellanic Cloud (SMC), using a semiautomated search method. Using the Dark Energy Camera, we survey a region covering a projected distance of ∼70 kpc of NGC 3109 (D= 1.3 Mpc,R_vir∼ 90 kpc,M∼ 10⁸M_*) as part of the MADCASH and DELVE-DEEP programs. We introduce a newly developed semiresolved search method, used alongside a resolved search, to identify crowded dwarf galaxies around NGC 3109. Using both approaches, we successfully recover the known satellites Antlia and Antlia B. We identified a promising candidate, which was later confirmed to be a background dwarf through deep follow-up observations. Our detection limits are well defined, with the sample ∼80% complete down toM_V∼ −8.0, and include detections of dwarf galaxies as faint asM_V∼ −6.0. This is the first comprehensive study of a satellite system through resolved stars around an SMC mass host. Our results show that NGC 3109 has more bright (M_V∼ −9.0) satellites than the mean predictions from cold dark matter models, but well within the host-to-host scatter. A larger sample of LMC/SMC-mass hosts is needed to test whether or not the observations are consistent with current model expectations. 

Abstract We present spectroscopy of the ultra-faint Milky Way satellites Eridanus III (Eri III) and DELVE 1. We identify eight member stars in each satellite and place nonconstraining upper limits on their velocity and metallicity dispersions. The brightest star in each object is very metal poor, at [Fe/H] = −3.1 for Eri III and [Fe/H] = −2.8 for DELVE 1. Both of these stars exhibit large overabundances of carbon and very low abundances of the neutron-capture elements Ba and Sr, and we classify them as CEMP-no stars. Because their metallicities are well below those of the Milky Way globular cluster population, and because no CEMP-no stars have been identified in globular clusters, these chemical abundances could suggest that Eri III and DELVE 1 are dwarf galaxies. On the other hand, the two systems have half-light radii of 8 pc and 6 pc, respectively, which are more compact than any known ultra-faint dwarfs. We conclude that Eri III and DELVE 1 are either the smallest dwarf galaxies yet discovered, or they are representatives of a new class of star clusters that underwent chemical evolution distinct from that of ordinary globular clusters. In the latter scenario, such objects are likely the most primordial star clusters surviving today. These possibilities can be distinguished by future measurements of carbon and/or iron abundances for larger samples of stars or improved stellar kinematics for the two systems. 

Abstract We present deep optical observations of the stellar halo of NGC 300, an LMC-mass galaxy, acquired with the DEEP subcomponent of the DECam Local Volume Exploration survey using the 4 m Blanco Telescope. Our resolved star analysis reveals a large, low surface brightness stellar stream (M_V ∼ −8.5; [Fe/H] = −1.4 ± 0.15) extending more than 40 kpc north from the galaxy’s center. We also find other halo structures, including potentially an additional stream wrap to the south, which may be associated with the main stream. The morphology and derived low metallicities of the streams and shells discovered surrounding NGC 300 are highly suggestive of a past accretion event. Assuming a single progenitor, the accreted system is approximately Fornax-like in luminosity, with an inferred mass ratio to NGC 300 of approximately 1:15. We also present the discovery of a metal-poor globular cluster (GC) (R_proj = 23.3 kpc;M_V = −8.99 ± 0.16; [Fe/H] ≈ −1.6 ± 0.6) in the halo of NGC 300, the furthest identified GC associated with NGC 300. The stellar structures around NGC 300 represent the richest features observed in a Magellanic Cloud analog to date, strongly supporting the idea that accretion and subsequent disruption is an important mechanism in the assembly of dwarf galaxy stellar halos.