arXiv subjects

Laura Toribio San Cipriano

Publications and source records attributed to Laura Toribio San Cipriano.

3 recordsLinked to original sources

arXiv2026

Rubin M1M3 Dynamic performance : stability and actuation during operations

The Vera C. Rubin Observatory is preparing to commence the Legacy Survey of Space and Time with the fully integrated Simonyi Survey Telescope. To verify that the primary/tertiary (M1M3) mirror system is ready to meet the demanding survey requirements, dynamic tests of the 8.4 m, 53 ton M1M3 system were conducted to assess safety, stability, and image quality under realistic operating conditions. The M1M3 is supported by 156 pneumatic force actuators and positioned, relative to its mirror cell, by six hardpoint actuators that together must counteract gravitational and inertial loads during rapid telescope motion. The Rubin Observatory telescope mount is capable of moving at a rate that meets its nominal motion requirements, and can approach it maximum allowable values that are 50 percent higher. Even at just 20 % of its operational speed, it is an exceptionally fast motion for such a large structure. After slewing, the system must stabilize and dampen vibrations within 5 seconds to ensure image quality during observations. Achieving this rapid settling requires precise control of 156 force actuators, which must adjust dynamically with changes in telescope elevation to compensate for gravity effects. We present results for M1M3 from a comprehensive series of TMA dynamical tests spanning the operational envelope of slew velocities and accelerations. The analysis evaluates elevation axis balancing and lookup table updating as we install the M1M3 mirror; slew-and-settle behavior, force response and stability of the pneumatic actuator system across telescope attitudes including responses to the earthquake. The results demonstrate the readiness of the M1M3 subsystem for routine survey operations and provide validation data for ongoing performance modeling.

astro-ph.IM↗

arXiv2026

Redshift Assessment Infrastructure Layers (RAIL): Rubin-era photometric redshift stress-testing and at-scale production

Virtually all extragalactic use cases of the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) require the use of galaxy redshift information, yet the vast majority of its sample of tens of billions of galaxies will lack high-fidelity spectroscopic measurements thereof, instead relying on photometric redshifts (photo-$z$) subject to systematic imprecision and inaccuracy best encapsulated by photo-$z$ probability density functions (PDFs). We present the version 1 release of Redshift Assessment Infrastructure Layers (RAIL), an open source Python library for at-scale probabilistic photo-$z$ estimation, initiated by the LSST Dark Energy Science Collaboration (DESC) with contributions from the LSST Interdisciplinary Network for Collaboration and Computing (LINCC) Frameworks team. RAIL's three subpackages provide modular tools for end-to-end stress-testing, including a forward modeling suite to generate realistically complex photometry, a unified API for estimating per-galaxy and ensemble redshift PDFs by an extensible set of algorithms, and built-in metrics of both photo-$z$ PDFs and point estimates. RAIL serves as a flexible toolkit enabling the derivation and optimization of photo-$z$ data products at scale for a variety of science goals and is not specific to LSST data. We thus describe to the extragalactic science community, including and beyond Rubin the design and functionality of the RAIL software library so that any researcher may have access to its wide array of photo-$z$ characterization and assessment tools.

astro-ph.IM↗

arXiv2026

Opportunities in AI/ML for the Rubin LSST Dark Energy Science Collaboration

The Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) will produce unprecedented volumes of heterogeneous astronomical data (images, catalogs, and alerts) that challenge traditional analysis pipelines. The LSST Dark Energy Science Collaboration (DESC) aims to derive robust constraints on dark energy and dark matter from these data, requiring methods that are statistically powerful, scalable, and operationally reliable. Artificial intelligence and machine learning (AI/ML) are already embedded across DESC science workflows, from photometric redshifts and transient classification to weak lensing inference and cosmological simulations. Yet their utility for precision cosmology hinges on trustworthy uncertainty quantification, robustness to covariate shift and model misspecification, and reproducible integration within scientific pipelines. This white paper surveys the current landscape of AI/ML across DESC's primary cosmological probes and cross-cutting analyses, revealing that the same core methodologies and fundamental challenges recur across disparate science cases. Since progress on these cross-cutting challenges would benefit multiple probes simultaneously, we identify key methodological research priorities, including Bayesian inference at scale, physics-informed methods, validation frameworks, and active learning for discovery. With an eye on emerging techniques, we also explore the potential of the latest foundation model methodologies and LLM-driven agentic AI systems to reshape DESC workflows, provided their deployment is coupled with rigorous evaluation and governance. Finally, we discuss critical software, computing, data infrastructure, and human capital requirements for the successful deployment of these new methodologies, and consider associated risks and opportunities for broader coordination with external actors.

astro-ph.IM↗