arXiv subjects

Andrew J. Connolly

Publications and source records attributed to Andrew J. Connolly.

At least 19 recordsLinked to original sources

arXiv2026

Environmental Sculpting of Galaxy Structure at Fixed Stellar Mass: A Multi-Scale Analysis Across Cosmic Time using 3 Million HSC Galaxies

The extent to which galaxy structure is shaped by environment beyond the local universe, once stellar mass is controlled, remains an open question in galaxy evolution. We address this challenge using an unprecedentedly large sample of $\sim$3 million galaxies from the Hyper Suprime-Cam Subaru Strategic Program spanning $0.3 \leq z < 0.7$ with $\log(M/M_{\odot}) \geq 8.9$. We correlate a mass-independent bulge-to-total ratio statistic with large-scale overdensity maps and cluster catalogs, propagating structural parameter posteriors through a Monte Carlo framework to robustly assess significance. We confirm with $>5σ$ confidence that galaxy structure depends on environment at fixed stellar mass, but this dependence is secondary to stellar mass and varies with redshift, mass, and environmental scale. At $z < 0.5$, we detect no significant structural correlation with large-scale overdensity, but cluster galaxies show statistically significant bulge enhancement compared to mass-matched field galaxies, indicating cluster-specific processes such as ram-pressure stripping and cumulative tidal interactions dominate structural transformation at these epochs. At $z \geq 0.5$, massive galaxies exhibit bulge-enhancement across both cluster- and large-scale environments, while lower-mass systems show enhancement only in cluster environments. This indicates that environmental mechanisms operate across broader spatial scales at earlier cosmic epochs, and enhanced merger rates, group preprocessing, and cosmic web stripping augment cluster-specific processes. By separating into star-forming and quiescent subsamples, we find nearly flat trends within each, demonstrating that the observed environmental effects arise from coupled morphological and star formation transformations. These results collectively reveal the multi-scale, epoch-dependent nature of environmental effects on galaxy structure.

astro-ph.GA↗

arXiv2026

LLMoxie: Exploring Agentic AI for Scientific Software Development

In this paper, we describe LLMoxie, an institutional AI platform whose three-tiered architecture supports multi-cloud and on-premise inference, a LiteLLM/MLflow control plane for authentication, budgeting, PII masking, and observability, and an application augmentation layer for AI coding agents. Layered on top, an open-source RSE-Plugins ecosystem encodes accumulated RSE knowledge as a Plugin-Agent-Skill hierarchy spanning scientific Python practice, domain-specific knowledge, a six-phase research-and-implement workflow, and project lifecycle management. Scientific software is judged less by raw code quality than by whether it can be cited, audited, reproduced, and extended. Off-the-shelf AI coding agents, optimized against commercial software benchmarks, are poorly calibrated for this setting: they ignore the conventions of the scientific Python libraries they invoke, mishandle sensitive or embargoed data, and leave decision trails that are difficult to reconstruct after the fact. We report on twenty months of practice at a university-based research software engineering (RSE) center, where RSEs embedded across astronomy, earth and climate science, agriculture, and health projects worked to close this gap. We characterize the recurring infrastructure, governance, and process challenges of adopting Agentic AI inside a multi-domain RSE center, describe the platform and plugin design, and distill operational lessons from real scientific software deployments. Together, the platform and plugins shift AI coding agents from generic code generators into domain-aware collaborators that respect community norms and produce auditable provenance of technical reasoning.

cs.SE↗

arXiv2026

Laser-based metrology systems vs wavefront sensing techniques: a comparative overview between the Large Binocular Telescope and the Vera C. Rubin Observatory for the telescope alignment and collimation tracking

This work presents a comparative overview of the collimation and alignment strategies employed by two leading 8m-class facilities: the Large Binocular Telescope (LBT) and the Vera C. Rubin Observatory. While both telescopes share a challenging fast f-number of approximately f/1.2 (considering the LBT in its Prime Focus configuration), they have adopted reciprocal architectures for the initial optical alignment strategy and for maintaining collimation during the night. As an initial alignment strategy, the LBT relies on a Wavefront Sensing technique called Focal Plane Image Analysis. Conversely, the Vera C. Rubin Observatory baseline foresees the usage of a Laser Tracker system to establish the initial optical states. The strategies for preserving the optical alignment and maintaining the collimation against gravitational flexure and thermal drift during observations are instead reversed. Besides the use of open-loop corrections based on Look-Up Tables, common on both telescopes, the LBT utilizes a laser-based Telescope Metrology System to monitor the relative position of optics in real-time, applying the corrections between the exposures. In contrast, the Rubin Observatory employs a Curvature Wavefront Sensing technique, using dedicated detectors at the four corners of the focal plane. Rather than identifying a best strategy, this work aims to synthesize the strengths, limitations, and operational trade-offs of these complementary approaches, from the perspective of the next generation of Extremely Large Telescopes and their instruments.

astro-ph.IM↗

arXiv2026

Hyrax: An Extensible Framework for Rapid ML Experimentation and Unsupervised Discovery in the Era of Rubin, Roman, and Euclid

The NSF-DOE Vera C. Rubin Observatory, Roman Space Telescope, Euclid, and other next-generation surveys will deliver imaging, spectroscopic, and time-domain data at scales that increasingly shift the bottleneck in astronomical machine learning (ML) projects from model design to infrastructure. We present Hyrax, an open-source, modular, GPU-enabled Python framework that supports the full ML lifecycle in astronomy: from data acquisition and training to inference and experiment comparison, with capabilities including multimodal dataset support, integrated vector databases for similarity search, and interactive two- and three-dimensional latent-space exploration for unsupervised discovery. We demonstrate Hyrax's versatility through five representative applications on real survey data: (i) unsupervised representation learning on $\sim 4\times10^5$ Rubin Legacy Survey of Space and Time (LSST) Data Preview 1 (DP1) galaxies, surfacing new merger and low-surface-brightness candidates missing from reference Euclid and Dark Energy Survey catalogs, while also isolating imaging artifacts -- all without labeled training data; (ii) hybrid density-based clustering for identifying cluster-scale gravitational lens candidates in DP1 data; (iii) multimodal early-time transient classification in the Zwicky Transient Facility leveraging light curves, spectra, images, and metadata; (iv) supervised false-positive filtering in shift-and-stack searches for distant solar system objects in the Dark Energy Camera Ecliptic Exploration Project survey; and (v) supervised detection of semi-resolved dwarf galaxies in Hyper Suprime-Cam and LSST-like imaging using synthetic source injection. Together, these results demonstrate that Hyrax provides astronomy-specific ML infrastructure that enables systematic discovery and rapid methodological iteration across next-generation astronomical surveys.

astro-ph.IM↗

arXiv2026

You Only Stack Once (YOSO): A Motion-Filtered, Deep-Learning Framework for Detecting Faint Moving Sources

We present You Only Stack Once (YOSO), an automated pipeline designed to detect faint, slow-moving Solar System objects in wide-field astronomical surveys. The pipeline integrates a novel Gaussian Motion Filter (GMoF) that operates at the pixel level to enhance signal-to-noise for objects exhibiting a range of apparent rates of motion. Unlike conventional shift-and-stack methods, which rely on discrete velocity trials, GMoF amplifies trails while suppressing random noise and static background features. Applied to a subset of DEEP observations from the Dark Energy Camera, YOSO recovered 45 out of 73 previously detected objects, as well as 11 new TNOs. It also discovered 216 objects in the near Solar System. Although alternative shift-and-stack methods are sensitive to objects about 0.88 magnitudes fainter, YOSO's false positive rate is extremely low, since it detects only sources that exhibit a trail and are consistent with a point source when shifted at the right rate. We show how this method can be deployed on large surveys like LSST, and adapted for other domains that require motion-based signal enhancement, including exoplanet imaging through Angular Differential Imaging (ADI), and near-Earth object (NEO) detection for missions like NEO Surveyor. YOSO thus provides a versatile, scalable approach for extracting faint, motion-dependent signals in the era of data-intensive astronomy.

astro-ph.EP↗

arXiv2026

WFST Supernovae in the First Year: I. Statistical Study of 16 Early-phase Type Ia Supernovae from the Pilot Survey

In this paper we present 16 early-phase type Ia supernovae (SNe Ia) discovered during the pilot survey of the 2.5-meter Wide Field Survey Telescope (WFST-PS) from March 4 to July 10, 2024, including three SNe Ia with early-excess emission features (EExSNe Ia). The discovery magnitude of the 16 WFST-PS early-phase SNe is at least 3 mag fainter than their peak brightness. A large scatter of color indices is found in approximately the first 10 days of supernova explosions, indicating diverse photometric behaviors in the early phase. Three EExSNe Ia show relatively brighter peak luminosities and longer rise time compared to those of non-EExSNe Ia. The results indicate that current theoretical models require further refinement to fully capture the early photometric evolution of SNe Ia. Based on the initial high-cadence ugr-band data from the WFST-PS survey, we emphasize that early near-ultraviolet (NUV) observations are indispensable for placing tight constraints on the explosion mechanisms and progenitor systems of SNe Ia.

astro-ph.HE↗

arXiv2026

WFST Supernovae in the First Year: II. SN 2024aedt: Systematical Study of a Transitional Type Ia Supernova

We present comprehensive photometric and spectroscopic observations of a transitional type Ia SN 2024aedt, discovered by the 2.5-meter Wide Field Survey Telescope (WFST) within one day of the explosion. Its light curve is characterized by a peak absolute magnitude of $M_B = -18.49 \pm 0.03$ mag and a decline rate of $Δm_{15}(B) = 1.53 \pm 0.36$ mag, placing the object on the $Δm_{15}(B)$--$M_B$ diagram in the transition region between normal and subluminous SNe Ia. Furthermore, the early-color evolution and host galaxy environment of SN 2024aedt underscore its transitional nature, sharing properties with both normal and 91bg-like SNe Ia. Light-curve modeling with MOSFiT yields a synthesized $^{56}\mathrm{Ni}$ mass of $0.414 \pm 0.042\,M_{\odot}$ and a total ejecta mass of $0.548 \pm 0.108\,M_{\odot}$. A comparison with theoretical models suggests that the evolutionary trend can be broadly explained by both delayed-detonation (DDT) and double-detonation (DDet) scenarios while possible early-excess emissions predicted by DDet cannot be identified given the limited detections soon after the SN explosion. Although the overall spectral evolution of SN 2024aedt is similar to that of other transitional SNe Ia, the spectroscopic comparison reveals diversity in the early-phase blue-end features, which becomes more homogeneous at later phases. The result indicates the importance of early-time observations in understanding the origin of SN Ia diversity.

astro-ph.HE↗

arXiv2026

WFST Supernovae in the First Year: III. Systematical Study of the Photometric Behavior of Early-phase Core-collapse Supernovae

We investigate the multiband photometric properties of seven supernovae (SNe) showing double-peaked light-curve evolution and prominent shock-cooling emission, observed by the Wide Field Survey Telescope (WFST) during its first year of operation. By jointly employing an analytic early shock-cooling model and the Arnett radioactive-diffusion model, we fit the bolometric light curves and infer ejecta masses in the range $1.1$-$2.6 M_\odot$, consistent with a transitional population between ultra-stripped supernovae (USSNe) and normal stripped-envelope supernovae (SESNe). The envelope masses are estimated to be $M_{\rm env}=0.1$-$0.4 M_\odot$, while the progenitors are constrained to be yellow or blue supergiants (YSGs/BSGs) with radii of $R=120$-$300 R_\odot$. Using empirical relations, we estimate progenitor luminosities of $L=10^{4.6}$-$10^{4.9} L_\odot$, corresponding to zero-age main-sequence (ZAMS) masses of $8$-$20 M_\odot$. Theoretical models suggest that such progenitors are more naturally produced through binary evolution channels, as single-star evolutionary pathways are unable to yield ejecta masses this low.

astro-ph.HE↗

arXiv2025

Quantifying the Impact of LSST $u$-band Survey Strategy on Photometric Redshift Estimation and the Detection of Lyman-break Galaxies

The Vera C. Rubin Observatory will conduct the Legacy Survey of Space and Time (LSST), promising to discover billions of galaxies out to redshift 7, using six photometric bands ($ugrizy$) spanning the near-ultraviolet to the near-infrared. The exact number of and quality of information about these galaxies will depend on survey depth in these six bands, which in turn depends on the LSST survey strategy: i.e., how often and how long to expose in each band. $u$-band depth is especially important for photometric redshift (photo-$z$) estimation and for detection of high-redshift Lyman-break galaxies (LBGs). In this paper we use a simulated galaxy catalog and an analytic model for the LBG population to study how recent updates and proposed changes to Rubin's $u$-band throughput and LSST survey strategy impact photo-$z$ accuracy and LBG detection. We find that proposed variations in $u$-band strategy have a small impact on photo-$z$ accuracy for $z < 1.5$ galaxies, but the outlier fraction, scatter, and bias for higher redshift galaxies varies by up to 50%, depending on the survey strategy considered. The number of $u$-band dropout LBGs at $z \sim 3$ is also highly sensitive to the $u$-band depth, varying by up to 500%, while the number of $griz$-band dropouts is only modestly affected. Under the new $u$-band strategy recommended by the Rubin Survey Cadence Optimization Committee, we predict $u$-band dropout number densities of $110$ deg$^{-2}$ (3200 deg$^{-2}$) in year 1 (10) of LSST. We discuss the implications of these results for LSST cosmology.

astro-ph.CO↗

arXiv2025

An Efficient Shift-and-Stack Algorithm Applied to Detection Catalogs

The boundary of solar system object discovery lies in detecting its faintest members. However, their discovery in detection catalogs from imaging surveys is fundamentally limited by the practice of thresholding detections at signal-to-noise (SNR) $\geq 5$ to maintain catalog purity. Faint moving objects can be recovered from survey images using the shift-and-stack algorithm, which coadds pixels from multi-epoch images along a candidate trajectory. Trajectories matching real objects accumulate signal coherently, enabling high-confidence detections of very faint moving objects. Applying shift-and-stack comes with high computational cost, which scales with target object velocity, typically limiting its use to searches for slow-moving objects in the outer solar system. This work introduces a modified shift-and-stack algorithm that trades sensitivity for speedup. Our algorithm stacks low SNR detection catalogs instead of pixels, the sparsity of which enables approximations that reduce the number of stacks required. Our algorithm achieves real-world speedups of $10$--$10^3 \times$ over image-based shift-and-stack while retaining the ability to find faint objects. We validate its performance by recovering synthetic inner and outer solar system objects injected into images from the DECam Ecliptic Exploration Project (DEEP). Exploring the sensitivity--compute time trade-off of this algorithm, we find that our method achieves a speedup of $\sim30\times$ with $88\%$ of the memory usage while sacrificing $0.25$ mag in depth compared to image-based shift-and-stack. These speedups enable the broad application of shift-and-stack to large-scale imaging surveys and searches for faint inner solar system objects. We provide a reference implementation via the find-asteroids Python package and this URL: https://github.com/stevenstetzler/find-asteroids.

astro-ph.IM↗

arXiv2025

ImageMM: Joint multi-frame image restoration and super-resolution

A key processing step in ground-based astronomy involves combining multiple noisy and blurry exposures to produce an image of the night sky with an improved signal-to-noise ratio. Typically, this is achieved via image coaddition, and can be undertaken such that the resulting night sky image has enhanced spatial resolution. Yet, this task remains a formidable challenge despite decades of advancements. In this paper, we introduce ImageMM: a new framework based on the majorization-minimization (MM) algorithm for joint multi-frame astronomical image restoration and super-resolution. ImageMM uses multiple registered astronomical exposures to produce a nonparametric latent image of the night sky, prior to the atmosphere's impact on the observed exposures. Our framework also features a novel variational approach to compute refined point-spread functions of arbitrary resolution for the restoration and super-resolution procedure. Our algorithms, implemented in TensorFlow, leverage graphics processing unit acceleration to produce latent images in near real time, even when processing high-resolution exposures. We tested ImageMM on Hyper Suprime-Cam (HSC) exposures, which are a precursor of the upcoming imaging data from the Rubin Observatory. The results are encouraging: ImageMM produces sharp latent images, in which spatial features of bright sources are revealed in unprecedented detail (e.g., showing the structure of spiral galaxies), and where faint sources that are usually indistinguishable from the noisy sky background also become discernible, thus pushing the detection limits. Moreover, aperture photometry performed on the HSC pipeline coadd and ImageMM's latent images yielded consistent source detection and flux measurements, thereby demonstrating ImageMM's suitability for cutting-edge photometric studies with state-of-the-art astronomical imaging data.

astro-ph.IM↗

arXiv2025

A Systematic Search for Main-Sequence Dipper Stars Using the Zwicky Transient Facility

Main-sequence dipper stars, characterized by irregular and often aperiodic luminosity dimming events, offer a unique opportunity to explore the variability of circumstellar material and its potential links to planet formation, debris disks, and broadly star-planet interactions. The advent of all-sky time-domain surveys has enabled the rapid discovery of these unique systems. We present the results of a large systematic search for main-sequence dipper stars, conducted across a sample of 63 million FGK main-sequence stars using data from Gaia eDR3 and the Zwicky Transient Facility (ZTF) survey. Using a novel light curve scoring algorithm and a scalable workflow tailored for analyzing millions of light curves, we have identified 81 new dipper star candidates. Our sample reveals a diverse phenomenology of light curve dimming shapes, such as skewed and symmetric dimmings with timescales spanning days to years, some of which closely resemble exaggerated versions of KIC 8462852. Our sample reveals no clear periodicity patterns sensitive to ZTF in many of these dippers and no infrared excess or irregular variability. Using archival data collated for this study, we thoroughly investigate several classification scenarios and hypothesize that the mechanisms of such dimming events are either driven by circumstellar clumps or occultations by stellar/sub-stellar companions with disks. Our study marks a significant step forward in understanding main-sequence dipper stars.

astro-ph.SR↗

arXiv2025

A Common Origin of Normal Type Ia Supernovae Suggested by the Photometric Diversity

In recent years, with an increasing number of type Ia supernovae (SNe Ia) discovered soon after their explosions, a non-negligible fraction of SNe Ia with early-excess emissions (EExSNe Ia) have been confirmed. In this letter, we present a total of \textbf{67} early-phase normal SNe Ia from published papers and ongoing transient survey projects to systematically investigate their photometric behaviors from very early time. We found that EExSNe Ia in our sample have longer rise and brighter peak luminosities compared to those of non-EExSNe Ia. Moreover, EExSNe Ia commonly have ``red-bump" features in the early $B-V$ color while non-EExSNe Ia show blueward evolution from the very beginning. Here, we propose that the thin-helium double-detonation scenario can phenomenologically explain the photometric diversities of normal SNe Ia considering different white dwarf-He-shell mass combinations and the viewing-angle effect, implying a unified explosion mechanism of normal-type SNe Ia. To further testify the possible common origin of normal SNe Ia, systematical studies of multiband photometric and spectral properties of early-phase SNe Ia through the new generation wide-field time-domain survey facilities and global real-time follow-up networks are highly demanded.

astro-ph.HE↗

arXiv2025

DESC DC2 Data Release Note

In preparation for cosmological analyses of the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST), the LSST Dark Energy Science Collaboration (LSST DESC) has created a 300 deg$^2$ simulated survey as part of an effort called Data Challenge 2 (DC2). The DC2 simulated sky survey, in six optical bands with observations following a reference LSST observing cadence, was processed with the LSST Science Pipelines (19.0.0). In this Note, we describe the public data release of the resulting object catalogs for the coadded images of five years of simulated observations along with associated truth catalogs. We include a brief description of the major features of the available data sets. To enable convenient access to the data products, we have developed a web portal connected to Globus data services. We describe how to access the data and provide example Jupyter Notebooks in Python to aid first interactions with the data. We welcome feedback and questions about the data release via a GitHub repository.

astro-ph.CO↗

arXiv2025

Estimates of Rotation Periods for Jupiter Trojans with the Zwicky Transient Facility Photometric Lightcurves

We present new rotational period estimates for 216 Jupiter Trojans using photometric data from the Zwicky Transient Facility (ZTF), including 80 Trojans with previously unknown periods. Our analysis reveals rotation periods ranging from 4.6 hours to 447.8 hours. These results support the existence of a spin barrier for Trojans larger than 10 km, with periods clustering between 4 and 4.8 hours. This spin barrier is roughly twice as long as that observed for main-belt asteroids, suggesting that Jupiter Trojans have significantly lower bulk densities, likely due to a higher fraction of ices and volatile materials in their composition. We identify three new Trojans with reliable rotation periods near the spin barrier, doubling the number of known Trojans in this critical period range. Using these results, we estimate a mean density of approximately 0.52 g/cm^3 for rubble-pile Trojans. Our findings support the growing evidence that many Trojans are rubble-pile bodies with distinct physical properties compared to main-belt asteroids. Looking forward, we anticipate that data from the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) will provide rotational period estimates for several hundred thousand Trojans, down to objects as small as 1 km, enabling a more detailed investigation of their rotational properties and internal structure.

astro-ph.EP↗

arXiv2024

Denser Environments Cultivate Larger Galaxies: A Comprehensive Study beyond the Local Universe with 3 Million Hyper Suprime-Cam Galaxies

The relationship between galaxy size and environment has remained enigmatic, with over a decade of conflicting results. We present one of the first comprehensive studies of the variation of galaxy radius with environment beyond the local Universe and demonstrate that large-scale environmental density is correlated with galaxy radius independent of stellar mass and galaxy morphology. We confirm with $>5σ$ confidence that galaxies in denser environments are up to $\sim25\%$ larger than their equally massive counterparts with similar morphology in less dense regions of the Universe. We achieve this result by correlating projected two-dimensional densities over $\sim360$ deg$^2$ with the structural parameters of $\sim3$ million Hyper Suprime-Cam galaxies at $0.3 \leq z < 0.7$ with $\log M/M_{\odot} \geq 8.9$. Compared to most previous studies, this sample is $\sim100-10,000$ times larger and goes $\sim1$ dex deeper in mass-completeness. We demonstrate that past conflicting results have been driven by small sample sizes and a lack of robust measurement uncertainties. We verify the presence of the above correlation separately for disk-dominated, bulge-dominated, star-forming, and quiescent subpopulations. We find the strength of the correlation to be dependent on redshift, stellar mass, and morphology. The correlation is strongest at lower redshifts and systematically weakens or disappears beyond $z \geq 0.5$. At $z\geq0.5$, more massive galaxies still display a statistically significant correlation. Although some existing theoretical frameworks can be selectively invoked to explain some of the observed correlations, our work demonstrates the need for more comprehensive theoretical investigations of the correlation between galaxy size and environment.

astro-ph.GA↗

arXiv2024

Probabilistic Forward Modeling of Galaxy Catalogs with Normalizing Flows

Evaluating the accuracy and calibration of the redshift posteriors produced by photometric redshift (photo-z) estimators is vital for enabling precision cosmology and extragalactic astrophysics with modern wide-field photometric surveys. Evaluating photo-z posteriors on a per-galaxy basis is difficult, however, as real galaxies have a true redshift but not a true redshift posterior. We introduce PZFlow, a Python package for the probabilistic forward modeling of galaxy catalogs with normalizing flows. For catalogs simulated with PZFlow, there is a natural notion of "true" redshift posteriors that can be used for photo-z validation. We use PZFlow to simulate a photometric galaxy catalog where each galaxy has a redshift, noisy photometry, shape information, and a true redshift posterior. We also demonstrate the use of an ensemble of normalizing flows for photo-z estimation. We discuss how PZFlow will be used to validate the photo-z estimation pipeline of the Dark Energy Science Collaboration (DESC), and the wider applicability of PZFlow for statistical modeling of any tabular data.

astro-ph.IM↗

arXiv2024

Using AI for Wavefront Estimation with the Rubin Observatory Active Optics System

The Vera C. Rubin Observatory will, over a period of 10 years, repeatedly survey the southern sky. To ensure that images generated by Rubin meet the quality requirements for precision science, the observatory will use an Active Optics System (AOS) to correct for alignment and mirror surface perturbations introduced by gravity and temperature gradients in the optical system. To accomplish this Rubin will use out-of-focus images from sensors located at the edge of the focal plane to learn and correct for perturbations to the wavefront. We have designed and integrated a deep learning model for wavefront estimation into the AOS pipeline. In this paper, we compare the performance of this deep learning approach to Rubin's baseline algorithm when applied to images from two different simulations of the Rubin optical system. We show the deep learning approach is faster and more accurate, achieving the atmospheric error floor both for high-quality images, and low-quality images with heavy blending and vignetting. Compared to the baseline algorithm, the deep learning model is 40x faster, the median error 2x better under ideal conditions, 5x better in the presence of vignetting by the Rubin camera, and 14x better in the presence of blending in crowded fields. In addition, the deep learning model surpasses the required optical quality in simulations of the AOS closed loop. This system promises to increase the survey area useful for precision science by up to 8%. We discuss how this system might be deployed when commissioning and operating Rubin.

astro-ph.IM↗