arXiv subjects

Robert Yates

Publications and source records attributed to Robert Yates.

7 recordsLinked to original sources

arXiv2026

HRMOS: A High-Resolution Multi-Object Spectrograph for the VLT

This White Paper presents the scientific rationale and instrument concept for HRMOS (High-Resolution Multi-Object Spectrograph), a next-generation instrument proposed for the ESO Very Large Telescope within the VLT 2030 roadmap. Current and planned facilities offer either multi-object spectroscopy or ultra-high spectral resolution, but not both. HRMOS fills this gap by combining very high spectral resolution, multi-object capability, and radial-velocity stability, enabling transformative studies in Galactic and extragalactic astrophysics. The baseline design provides a resolving power of R = 80000, radial-velocity precision of 10 m s-1 (goal: 5 m s-1), simultaneous observations of 50-60 targets, and broad optical coverage down to 385 nm. These capabilities enable precise measurements of elemental abundances, isotopic ratios, line profiles, and radial velocities for large stellar samples, including crowded fields, star clusters, the Galactic bulge, and nearby dwarf galaxies. HRMOS will address key questions on the age of the oldest stellar populations through nucleocosmochronology, the formation and survival of planetary systems, the assembly history of the Milky Way and satellites, the origin of the heaviest elements, stellar evolution, and the chemical and dynamical properties of the interstellar and circumgalactic medium. It will bridge large spectroscopic surveys and the next generation of extremely large telescopes, with strong synergies with 4MOST, Gaia, TESS, PLATO, the proposed Haydn mission, and future ELT instruments. Building on VLT/FLAMES heritage, HRMOS represents a strategic investment for European astronomy in the 2030s.

astro-ph.IM↗

arXiv2026

Cosmology with Tully-Fisher HI Galaxy Surveys

The SKA Observatory will enable measurements of the Tully-Fisher relation for statistical samples of HI selected galaxies out to unprecedented depths and redshifts thanks to its unique combined spatial and spectral sensitivity. This chapter explores the transformative potential of such surveys for cosmology, in particular in the field of peculiar velocity measurements. We briefly review the present observational landscape for Tully-Fisher HI galaxy surveys and existing peculiar velocity datasets, and compare them with predictions for SKAO Tully-Fisher HI galaxy surveys with AA* and AA4 configurations of the SKA-Mid array. We discuss the extended range of cosmology science cases covered and enabled by such surveys.

astro-ph.CO↗

arXiv2026

Disentangling chemical evolution histories with phylogenetic trees

Chemical abundances encode the fossil record of galaxy evolution in a complex and diverse way that requires innovative approaches to reconstruct galactic histories. We investigate the power of using phylogenetic methods to disentangle different evolutionary pathways in analytical chemical evolution models. We ran 1024 one-zone chemical evolution models using flexCE. The resulting chemical abundances are combined with those of two fiducial models, mw-fid and dw-fid, and then used both to determine which combinations produce two-branched phylogenetic trees, as well as how purely these trees split the two input models. We used random forests and Shapley analysis to predict which model combinations return well-separated trees and explain which input parameters are most important for this. We also studied the abundance patterns, as well as star formation rates, mass accumulation, and branch lengths. We found that {\eta}, the mass-loading outflow parameter in flexCE, had the largest impact in separating models into separate branches, due to its importance in driving the chemical enrichment rates and total abundances. Star formation rates and mass accumulation had some impact on {\eta}, but no direct relation between these quantities and the abundances was found. We also found that branches connected through the most metal rich tips in our trees, which is opposite to how phylogenetic trees connect in biological systems. Phylogenetic trees help to reconstruct histories when there is information that is inherited between generations, which is the case of the chemical elements in galaxy evolution. Branch topologies can provide information about the rates of evolutionary change of the various populations, and the connection between branches also contains information about their shared history. This work brings us a step further understanding galaxy evolution through cross-disciplinary research.

astro-ph.GA↗

arXiv2026

Simulations of the 21cm emission line for upcoming large-scale HI galaxy surveys

Upcoming galaxy surveys with the SKA Observatory will detect neutral hydrogen (HI) across unprecedented volumes, and their scientific return will crucially depend on predictive models for HI observables. In this work, we present a framework to simulate the neutral hydrogen 21cm emission line in such large-scale HI galaxy surveys. This framework is developed as a modular layer that builds on semi-analytical models. In particular we use as bases the Galaxy Evolution and Assembly (GAEA) and L-Galaxies semi-analytical models, coupled to merger trees from the Millennium Simulation suite. We validate our framework against local Universe observations, demonstrating consistency with velocity functions, and generalised Tully-Fisher relations. Predictions based on GAEA and L-Galaxies exhibit mutual consistency despite the distinct underlying physical prescriptions. We construct mock galaxy catalogues that incorporate forward-modelled selection functions, inclination effects, and redshift broadening, reproducing the statistical distributions of HI-selected galaxies in the ALFALFA survey. Finally, we present redshift distribution forecasts for future SKA Observatory HI galaxy surveys. This framework offers a flexible tool for interpreting upcoming large-scale radio surveys and studying HI line observables as cosmological probes.

astro-ph.CO↗

arXiv2023

Optimal metallicity diagnostics for MUSE observations of low-z galaxies

The relatively red wavelength range (4800-9300{\AA}) of the VLT Multi Unit Spectroscopic Explorer (MUSE) limits which metallicity diagnostics can be used; in particular excluding those requiring the [O ii]{\lambda}{\lambda}3726,29 doublet. We assess various strong line diagnostics by comparing to sulphur Te-based metallicity measurements for a sample of 671 HII regions from 36 nearby galaxies from the MUSE Atlas of Disks (MAD) survey. We find that the O3N2 and N2 diagnostics return a narrower range of metallicities which lie up to ~0.3 dex below Te-based measurements, with a clear dependence on both metallicity and ionisation parameter. The N2S2H{\alpha} diagnostic shows a near-linear relation with the Te-based metallicities, although with a systematic downward offset of ~0.2 dex, but no clear dependence on ionisation parameter. These results imply that the N2S2H{\alpha} diagnostic produces the most reliable results when studying the distribution of metals within galaxies with MUSE. On sub-HII region scales, the O3N2 and N2 diagnostics measure metallicity decreasing towards the centres of HII regions, contrary to expectations. The S-calibration and N2S2H{\alpha} diagnostics show no evidence of this, and show a positive relationship between ionisation parameter and metallicity at 12 + log(O/H)> 8.4, implying the relationship between ionisation parameter and metallicity differs on local and global scales. We also present HIIdentify, a python tool developed to identify HII regions within galaxies from H{\alpha} emission maps. All segmentation maps and measured emission line strengths for the 4408 HII regions identified within the MAD sample are available to download.

astro-ph.GA↗

arXiv2023

Assembling a high-precision abundance catalogue of solar twins in GALAH for phylogenetic studies

Stellar chemical abundances have proved themselves a key source of information for understanding the evolution of the Milky Way, and the scale of major stellar surveys such as GALAH have massively increased the amount of chemical data available. However, progress is hampered by the level of precision in chemical abundance data as well as the visualization methods for comparing the multidimensional outputs of chemical evolution models to stellar abundance data. Machine learning methods have greatly improved the former; while the application of tree-building or phylogenetic methods borrowed from biology are beginning to show promise with the latter. Here we analyse a sample of GALAH solar twins to address these issues. We apply The Cannon algorithm to generate a catalogue of about 40,000 solar twins with 14 high precision abundances which we use to perform a phylogenetic analysis on a selection of stars that have two different ranges of eccentricities. From our analyses we are able to find a group with mostly stars on circular orbits and some old stars with eccentric orbits whose age-[Y/Mg] relation agrees remarkably well with the chemical clocks published by previous high precision abundance studies. Our results show the power of combining survey data with machine learning and phylogenetics to reconstruct the history of the Milky Way.

astro-ph.GA↗

arXiv2018

Bootstrapping Conversational Agents With Weak Supervision

Many conversational agents in the market today follow a standard bot development framework which requires training intent classifiers to recognize user input. The need to create a proper set of training examples is often the bottleneck in the development process. In many occasions agent developers have access to historical chat logs that can provide a good quantity as well as coverage of training examples. However, the cost of labeling them with tens to hundreds of intents often prohibits taking full advantage of these chat logs. In this paper, we present a framework called \textit{search, label, and propagate} (SLP) for bootstrapping intents from existing chat logs using weak supervision. The framework reduces hours to days of labeling effort down to minutes of work by using a search engine to find examples, then relies on a data programming approach to automatically expand the labels. We report on a user study that shows positive user feedback for this new approach to build conversational agents, and demonstrates the effectiveness of using data programming for auto-labeling. While the system is developed for training conversational agents, the framework has broader application in significantly reducing labeling effort for training text classifiers.

cs.AI↗