arXiv subjects

Rodrigo Ibata

Publications and source records attributed to Rodrigo Ibata.

At least 19 recordsLinked to original sources

arXiv2026

A direct measurement of the tilting rate of the Milky Way disc

The Milky Way disc is typically described, at lowest order, as a flattened rotating stellar component with a fixed spin axis. However, interactions and mergers with smaller galaxies are expected to have an impact on the disc properties. Such interactions can leave long-lasting signatures within galactic discs, including warps and corrugation waves that have already been detected in the Milky Way, but also a slow reorientation of the disc known as tilting, which has never been directly measured up to now. Here, using the vertical proper motions of 110 million stars from the Gaia DR3 catalogue, we present the first-ever direct measurement of the tilting rate of our Galaxy's disc. The detected signal of 14 +/- 2 deg/Gyr around the Sun-Galactic Centre axis is statistically significant and is consistent with typical values found in cosmological simulations. By comparing our measurements with high-resolution tailored simulations, we infer that the observed tilting rate is likely the long-term consequence of a major accretion event that occurred around 8-10 billion years ago. The signal is consistent with the merger of a massive satellite galaxy on a retrograde orbit with a ratio of about 1:4, probably associated with the Gaia-Enceladus/Sausage event. These results provide independent evidence for a key episode in the Milky Way's formation history and introduce a new way to reconstruct the assembly histories of disc galaxies from their present-day motions.

astro-ph.GA↗

arXiv2026

NestyNet. III. Symbolic Regression from Analytic Neural Surrogates

Many physical laws are simple only after the right representation, decomposition or internal coordinate has been found, but discovering that structure from data is combinatorially hard. This task is symbolic regression (SR), the search for closed-form expressions that fit data without assuming a fixed model class. Here we present NestyNet-SR. A neural surrogate with analytic derivatives is used to detect separability, recursively reducing multivariate problems to simpler neural atoms. These atoms are distilled into closed form by a tiered symbolic-search stack, whose final tier is a novel factorized symbolic search that separates structure from calibration. Composing candidate internal coordinates freely, it scores each coordinate by how well calibrated functions of it (e.g., polynomials, power laws, sinusoids) fit the data, so the constants of those calibrated maps, however deeply nested in the final expression, are fitted rather than searched. The method supports multi-dataset regression, automated feature discovery, and dimensional-analysis pruning. On the SRBench AI~Feynman benchmark, NestyNet-SR achieves exact symbolic recovery of all 120 noiseless equations, the first such result, and under noise a statistical audit certifies which structures survive. As a real-data vignette, given only the separate mass-model components of SPARC-survey galaxies, the algorithm discovers the baryonic acceleration coordinate, reproduces the established mass-to-light and acceleration scales and the non-unique form of the radial acceleration relation, and adds held-out-galaxy generalization, a calibrated symmetry abstention, and a posterior for the local slope of the law. Analytic derivatives thus provide a practical route from neural surrogates to interpretable closed-form empirical laws.

astro-ph.IM↗

arXiv2026

NestyNet. IV. Laws Chosen by Nothing in Advance

Differential-equation (DE) discovery tends to break down precisely where much of physics begins. Fields are coupled, governing laws are nonlinear in the state, amplitudes, coordinates, or operators of interest, yet derivatives must remain consistent across fields, channels, and differentiation orders. NestyNet-DE addresses this via two complementary DE search strategies, both employing analytic derivatives from segmented neural surrogates: a sparse-library route linear in the outer coefficients, and a new operator-factorized route that searches directly over equation structure rather than a fixed library, recovering compositional laws the sparse route misses. The recovered laws can be strongly nonlinear in states, fields, coordinates, and their couplings. The framework also handles multi-dataset shared-support discovery, complex and vector laws, and Hamiltonian discovery from phase-space trajectories. Beyond a law's form, the same data yield its geometry, the Lie point symmetries of the recovered equation. We apply the pipeline to 30 years of daily ephemerides of $308$ main-belt asteroids and recover the reduced-Kepler hierarchy (areal law, inverse-square force, and reduced Hamiltonian), where a discovered rotational symmetry fixes the centrifugal coefficient rather than fitting it. We also present a benchmark of 57 real-valued ODEs and 26 complex-valued systems, including the Schr\"odinger and Dirac equations, with Maxwell's equations as a coupled vector-PDE case study. Here, discovery is not shackled to a fixed library, nor does it end at the equation. It composes laws that no dictionary anticipated, and closes the loop from data, to a law chosen by nothing in advance, to the geometry that explains and integrates it.

astro-ph.IM↗

arXiv2026

NestyNet. I. Physics Functions Are Hard to Fit with Neural Networks: A Framework for Accurate Surrogates and Analytic Derivatives

Many of the smooth functions that matter most in physics are precisely the ones that standard neural network methods struggle to fit accurately. Here we present NestyNet, a coupled model-and-optimizer framework capable of fitting such targets to high accuracy while also delivering their gradients, Hessians, Laplacians, and antiderivatives analytically and at low cost. This makes it a natural substrate for scientific machine learning tasks. The model is a deterministic segmented analytic surrogate, and its optimizer is a second-order Levenberg--Marquardt scheme whose damping and linear solves are tailored to the stiff, strongly correlated parameter geometries induced by multiscale and sharply structured targets typical in physics and other scientific applications. On the AI Feynman benchmark of 120 physics equations, NestyNet achieves median improvement factors of $2\,100\times$ for function values, $1\,400\times$ for first derivatives, and $780\times$ for second derivatives relative to standard neural networks trained with first-order optimization (Adam). Even after refining those fits with quasi-Newton (L-BFGS) optimization, the corresponding improvements are $540\times$, $450\times$, and $250\times$. Owing to the analytic design it is up to $\approx 44\times$ faster than vectorized automatic-differentiation (autograd) baselines, with a margin growing with model size. The same analytic-derivative framework also supports vector- and complex-valued targets, measurement uncertainties in both inputs and outputs, and constraints, and its modules can be composed flexibly to build scientifically useful model architectures, all without reverting to autograd. Together, these components provide a practical modular framework for fitting difficult scientific surrogates while delivering accurate differential operators for subsequent analysis.

astro-ph.IM↗

arXiv2026

NestyNet. II. Coherent Function-Space Posteriors from Scientific Neural Surrogates (or How to Avoid Expensive MCMC)

Scientific analyses increasingly use flexible neural networks, but their thousands of correlated parameters make it challenging to interpret the associated uncertainties. Here we develop a low-dimensional posterior for the fitted function itself, for scientific neural surrogates trained with second-order optimization. Linearizing the fitting procedure with respect to the randomized residual rows gives a measurement-to-function transport, the linear map, assembled from the converged Jacobians and Gauss--Newton curvature, that carries measurement perturbations into the function perturbations that refitting would produce. Its leading singular functions define coherent deformation modes. Independent Gaussian coefficients then generate smooth function draws, so that any derived quantity, including those requiring derivatives or integrals of the draw, inherits the posterior. The construction distinguishes repeated-experiment covariance from the local Gauss--Newton/Laplace posterior and propagates both to correlated quantities of scientific interest. The result is conditional on the fit's declared choices (architecture, hyperparameters, active set, and optimization branch), and every fit is certified as converged by checking that a further optimization step would change the fitted predictions by less than a chosen small fraction of the measurement errors. Our primary example is an 800-parameter phase-space distribution function fit for a mock stellar disk. Four uncertainty coordinates, two orders of magnitude fewer than the fitted parameters and stable under refinement of the force basis, capture 99\% of the vertical-force posterior variance, and 4000 coherent draws propagate through the force, total-density, surface-density, and frequency calculations in 0.8s. The method provides a highly efficient route to uncertainty propagation for derivative-dependent scientific inference.

astro-ph.IM↗

arXiv2026

Reionisation time field reconstruction from 21-cm Maps: Investigating predictor coherence in WDM cosmology

The reionisation time field treion(r) captures the entire history of cosmic reionisation by mapping the moment where each region of the Universe became ionised. Previous work has shown that treion(r) can be inferred from 21-cm observations, using convolutional neural networks (CNNs). However, these CNN predictors are trained on specific reionisation models, raising critical concerns about their reliability when applied to observational data potentially differing from their training assumptions. This paper aims to propose and test a method to evaluate the coherence of our CNN predictors with respect to their input model, thereby enabling the validation or exclusion of underlying reionisation models based on their reconstruction behaviour. By setting the CDM model as reference input, we evaluate the coherence of treion(r) reconstructions by comparing them across different redshifts for several prediction models as the statistics of treion (r) reconstructions should be the same for every redshift of the input maps. Our study particularly investigates CNNs trained on cold and warm dark matter (WDM) models, with WDM particle masses of 2, 3, 5, and 7 keV. We find that the predictors trained on 5 and 7 keV WDM models exhibit high-level self-consistency similar to the CDM predictor, while the 2 keV predictor, and to a lesser extent the 3 keV predictor, display significant deviations across several metrics. These findings seem to demonstrate that CNN predictors retain sensitivity to differences in the underlying reionisation model and can be used to assess model compatibility with observations. Our results highlight the necessity of validating machine-learning predictors against their input models before applying them to real data. The method proposed here offers a pathway to more trustworthy applications of CNNs in the study of reionisation.

astro-ph.CO↗

arXiv2026

The primordial nature of the C-19 stellar stream

Stellar streams, remnants of compact star systems stretched out by the tidal forces of the Milky Way, offer a unique way to study stellar populations that formed billions of years ago. A particularly unique stream is C-19, the most metal-poor stellar stream known at less than a thousandth of the Sun's metallicity. The nature of C-19 is not yet clear, with properties that resemble both star clusters and ultra faint dwarf galaxies, yet in either case its extremely low metallicity indicates very early star formation, <1 Gyr after the Big Bang. Here, we present the first detailed study on the nature of C-19 based on the chemical abundances of 14 member stars from high-resolution spectroscopy. These reveal that C-19 formed stars in an early, rapid, and prolific star formation event, with mild inhomogeneous mixing of elements produced in massive stars. There is otherwise no evidence for subsequent star formation, multiple stellar populations, nor chemical evolution. Although C-19 is currently disrupted in the Milky Way halo, it offers a rare and complementary window into the details of star formation and chemical evolution in the early universe, ideal for comparisons with current studies of primordial star formation in the high-redshift universe.

astro-ph.GA↗

arXiv2025

Asymmetries in stellar streams induced by a galactic merger

Stellar streams are sensitive to perturbations from, e.g., giant molecular clouds, bars and spiral arms, infalling dwarf galaxies, or globular clusters which can imprint gaps, clumps, spurs, and asymmetries in tails. In addition to these effects, the impact of a galactic major merger on a population of stellar streams remains to be explored. Here, we focus on the emergence and longevity of asymmetries between the leading and trailing tails of streams caused by such interactions. We run collisionless N-body simulations of a Milky Way-like galaxy hosting 36 globular cluster streams and merging with a perturber galaxy. We propose a new asymmetry metric to quantify the structural differences between both tails from their respective cumulative density profiles. We find that the over- and under-densities along streams induced by the merger depend on the orbital characteristics of their progenitors. The non-simultaneity of this effect from stream to stream implies that global asymmetry signatures are less prominent than in individual cases. These population-averaged imprints remain detectable over only 2.5 Gyr but asymmetric signatures can persist over much longer periods for individual streams with wide orbits that have been perturbed prior to coalescence. We thus caution that the interpretation of streams' morphology in the context of dark matter mapping is strongly subject to degeneracies and should be performed considering the merger history of the host.

astro-ph.GA↗

arXiv2025

UNIONS: The Ultraviolet Near-Infrared Optical Northern Survey

The Ultraviolet Near-Infrared Optical Northern Survey (UNIONS) is a "collaboration of collaborations" that is using the Canada-France-Hawai'i Telescope, the Pan-STARRS telescopes, and the Subaru Observatory to obtain $ugriz$ images of a core survey region of 6250 deg$^2$ of the northern sky. The $10\sigma$ point source depth of the data, as measured within a 2-arcsecond diameter aperture, are $[u,g,r,i,z] = [23.7, 24.5, 24.2, 23.8, 23.3]$\ in AB magnitudes. UNIONS is addressing some of the most fundamental questions in astronomy, including the properties of dark matter, the growth of structure in the Universe from the very smallest galaxies to large-scale structure, and the assembly of the Milky Way. It is set to become the major ground-based legacy survey for the northern hemisphere for the next decade and provides an essential northern complement to the static-sky science of the Vera C. Rubin Observatory's Legacy Survey of Space and Time. UNIONS supports the core science mission of the {\it Euclid} space mission by providing the data necessary in the northern hemisphere for the calibration of the wavelength dependence of the {\it Euclid} point-spread function and derivation of photometric redshifts in the North Galactic Cap. This region contains the highest quality sky for {\it Euclid}, with low backgrounds from the zodiacal light, stellar density, extinction, and emission from Galactic cirrus. Here, we describe the UNIONS survey components, science goals, data products, and the current status of the overall program.

astro-ph.GA↗

arXiv2025

A primordial origin to cosmic tensions: towards reconciling $H_0$ and $S_8$ with early dark energy and scale-dependent primordial non-Gaussianities

The Hubble ($H_0$) tension between direct measurements of the expansion rate and the prediction of the $\Lambda$CDM cosmological model calibrated on the Cosmic Microwave Background (CMB), is a strong motivation to explore alternative cosmological models. A popular class of such models includes an additional component of dark energy relevant in the early Universe, which solves the Hubble tension. These Early Dark Energy (EDE) models however typically overpredict the value of the $S_8$ parameter. Here, we show how combining EDE with scale-dependent primordial non-Gaussianities (sPNG) can in principle both increase $H_0$ and decrease $S_8$ at once, even conceivably allowing to solve the potential $S_8$ tension between measurements of weak gravitational lensing and the $\Lambda$CDM expectation. Such sPNG are related to non-trivial inflationary physics, and observationally affect the non-linear regime of structure formation while leaving the linear regime mostly untouched. Depending on the amplitude of the sPNG, such models can either yield back the $\Lambda$CDM expectation for the power spectrum in the non-linear regime, and hence an $S_8$ parameter compatible with $\Lambda$CDM, or can even reconcile the value of $S_8$ from old weak-lensing measurements with the CMB, while solving the Hubble tension in all cases. In such models, both tensions would then be entirely related to pre-CMB physics of the early Universe.

astro-ph.CO↗

arXiv2025

Gaps in stellar streams as a result of globular cluster fly-bys

Thin stellar streams, such as those resulting from the tidal disruption of globular clusters, have long been known and used as probes of the gravitational potential of our Galaxy, both its visible and dark contents. In particular, the presence of under-density regions, or gaps, along these streams is commonly interpreted as being due to the close passage of dark matter sub-halos. In this work, we investigate the perturbations induced on streams by the passage of dense stellar systems, such as globular clusters themselves, to test the possibility that they may cause the formation of gaps as well. In particular, we focus on the study of the stream of Palomar 5, a well-known globular cluster in the Galactic halo, which has particularly long tidal tails. For this purpose, we used a particle-test code to simulate Palomar 5's tidal tails when subjected to the Galaxy's gravitational field plus its whole system of globular clusters. Our study shows that the tails of Palomar 5 can be strongly perturbed by the close passage of other clusters, in particular of NGC 2808, NGC 7078, NGC 104, and that these perturbations induce the formation of gaps in the tails. These results show that globular clusters are capable of inducing gaps in streams--as other baryonic components such as giant molecular clouds and the galactic bar have been shown to do in other works. Therefore, when searching to construct the distribution function of dark matter sub halos within the Milky Way, the gap contribution from globular clusters must be included.

astro-ph.GA↗

arXiv2025

A cosmic degeneracy story: structure formation with warm dark matter and scale-dependent primordial non-Gaussianities

It has been recently shown that cosmological models with scale-dependent primordial non-Gaussianities (sPNG) could provide a possible path to solve current cosmic tensions. Moreover, it has been pointed out that some of these models might mimic the effects of Warm Dark Matter (WDM) for several observables at low redshift. Here, we confirm the qualitative similarity of the matter power spectrum for sPNG and WDM models, but also point out differences in the halo mass function and void size function. We then jointly simulate WDM and sPNG together. Such simulations allow us to demonstrate that the joint impact of WDM and sPNG is close to the linear superposition of their respective effects at low redshift, at the percent level. We finally propose a model with mixed hot and cold dark matter together with sPNG, that reproduces the $\Lambda$CDM power spectrum at redshifts $z \leq 3$ but is still distinct in terms of halo statistics.

astro-ph.CO↗

arXiv2024

C-19 and Hot, Wide, Star Streams

The C-19 star stream has the abundance characteristics of an unusually metal poor globular cluster but kinematically is uncharacteristically hot and wide for a cluster stream, having a line of sight velocity dispersion of 7 +/- 2 km/s and a 1-sigma width of 240 pc. We show that the tidal dissolution of an old, lower mass, globular cluster in a CDM galactic halo can create a hot, wide stream currently near orbital apocenter. A cosmological Milky Way n-body simulation motivates the parameters for an evolving Milky Way halo potential containing an orbiting subhalo population in which we model a star cluster progenitor of C-19. The same model parameters have been used for a GD-1 stream model. The ~7 km/s velocity dispersion is readily accomplished with an evolving CDM subhalo population, a progenitor cluster mass ~2x10^4 M_sun and an orbit that keeps the progenitor orbital pericenter within about 10 kpc of the Milky Way dark halo or its precursors.

astro-ph.GA↗

arXiv2024

Reassessing the proper motions of M31/M33 with Gaia DR3. Unraveling systematic uncertainties

We provide an updated inference of the proper motion of M31 using the Gaia DR3 proper motions of bright stars from the disc of M31. By refining the motion of the quasar reference frame, and statistically accounting for the variations in the inferred proper motions obtained across different regions of M31, we demonstrate that these inconsistencies most likely arise from systematic uncertainties. Our updated favoured values for the proper motion of M31 are 46.9$\pm$11.7(stat)$\pm$50.6(sys)$\mu$as yr$^{-1}$ along the right ascension direction, and -29.1$\pm$9.4(stat)$\pm$35.6(sys)$\mu$as yr$^{-1}$ along the declination direction, the systematics being determined at a 90% confidence level (the values for M33 are given in the paper). This clearly highlights that the systematics are the dominant source of uncertainty, their magnitudes being comparable to the proper motion of M31 itself. The analysis conducted using Gaia DR2 instead of DR3 revealed that a net reduction in these systematic uncertainties occurred between the two data releases. If similar progress is made with the upcoming DR4, the future Gaia-based estimates could match the level of uncertainties of HST, and could be used to refine the dynamics and history of M31 and M33.

astro-ph.GA↗

arXiv2024

Tackling fluffy clouds: robust field boundary delineation across global agricultural landscapes with Sentinel-1 and Sentinel-2 Time Series

Accurate delineation of agricultural field boundaries is essential for effective crop monitoring and resource management. However, competing methodologies often face significant challenges, particularly in their reliance on extensive manual efforts for cloud-free data curation and limited adaptability to diverse global conditions. In this paper, we introduce PTAViT3D, a deep learning architecture specifically designed for processing three-dimensional time series of satellite imagery from either Sentinel-1 (S1) or Sentinel-2 (S2). Additionally, we present PTAViT3D-CA, an extension of the PTAViT3D model incorporating cross-attention mechanisms to fuse S1 and S2 datasets, enhancing robustness in cloud-contaminated scenarios. The proposed methods leverage spatio-temporal correlations through a memory-efficient 3D Vision Transformer architecture, facilitating accurate boundary delineation directly from raw, cloud-contaminated imagery. We comprehensively validate our models through extensive testing on various datasets, including Australia's ePaddocks - CSIRO's national agricultural field boundary product - alongside public benchmarks Fields-of-the-World, PASTIS, and AI4SmallFarms. Our results consistently demonstrate state-of-the-art performance, highlighting excellent global transferability and robustness. Crucially, our approach significantly simplifies data preparation workflows by reliably processing cloud-affected imagery, thereby offering strong adaptability across diverse agricultural environments. Our code and models are publicly available at https://github.com/feevos/tfcl.

cs.CV↗

arXiv2024

One Stream or Two -- Exploring Andromeda's North West Stream

We present results of our dynamical stream modelling for the North West Stream in the outer halo of the Andromeda galaxy (M31). Comprising two main segments, the North West Stream was thought to be a single structured arching around M31. However, recent evidence suggests that it is two separate, unrelated, streams. To test this hypothesis we use observational data from 6 fields associated with the upper segment of the North West Stream together with 8 fields and 5 globular clusters associated with the lower segment to constrain model orbits. We fit both segments of the stream using a fixed potential model for M31 and an orbit integrator to compare orbits with the observed streams. We measure the central tracks and predict proper motions for for the upper segment (lower segment) finding ${\mu^*_{\alpha}}$ = 0.078$^{+0.015}_{-0.012}$ (0.085$^{+0.001}_{-0.002}$) mas/yr and ${\mu_{\delta}}$ = $-$0.05$^{+0.008}_{-0.009}$ ($-$0.095$^{+0.003}_{-0.005}$) mas/yr. Our results support the hypothesis that the dwarf spheroidal galaxy Andromeda XXVII is the progenitor of the upper segment of the North West Stream and that the upper and lower segments do not comprise a single structure. We propose that the upper segment, which appears to be on an infall trajectory with M31, be renamed the "Andromeda XXVII Stream" and the lower segment, also apparently infalling towards M31, retain the name "North West Stream".

astro-ph.GA↗

arXiv2024

Properties of the Lower Segment of M31's North West Stream

We present a kinematic and spectroscopic analysis of 40 red giant branch stars, in 9 fields, exquisitely delineating the lower segment of the North West Stream (NW-K2), which extends for $\sim$80 kpc from the centre of the Andromeda galaxy. We measure the stream's systemic velocity as -439.3$^{+4.1}_{-3.8}$ km/s with a velocity dispersion = 16.4$^{+5.6}_{-3.8}$ km/s that is in keeping with its progenitor being a dwarf galaxy. We find no detectable velocity gradient along the stream. We determine $-$1.3$\pm$0.1 $\le$ <[Fe/H]$_{\rm spec}$> $\le$ $-$1.2$\pm$0.8 but find no metallicity gradient along the stream. We are able to plausibly associate NW-K2 with the globular clusters PandAS-04, PandAS-09, PAndAS-10, PAndAS-11, PandAS-12 but not with PandAS-13 or PandAS-15 which we find to be superimposed on the stream but not kinematically associated with it.

astro-ph.GA↗

arXiv2024

Simulations of cluster ultra-diffuse galaxies in MOND

Ultra-diffuse galaxies (UDGs) in the Coma cluster have velocity dispersion profiles that are in full agreement with the predictions of Modified Newtonian Dynamics (MOND) in isolation. However, the external field effect (EFE) from the cluster seriously deteriorates this agreement. It has been suggested that this could be related to the fact that UDGs are out-of-equilibrium objects whose stars have been heated by the cluster tides or that they recently fell onto the cluster on radial orbits, such that their velocity dispersion may not reflect the EFE at their instantaneous distance from the cluster center. Here, we simulate UDGs within the Coma cluster in MOND, using the Phantom of Ramses (\textsc{por}) code, and show that if UDGs are initially at equilibrium within the cluster, tides are not sufficient to increase their velocity dispersions to values as high as the observed ones. On the other hand, if they are on a first radial infall onto the cluster, they can keep high velocity dispersions without being destroyed until their first pericentric passage. We conclude that, without alterations such as a screening of the EFE in galaxy clusters or much higher baryonic masses than currently estimated, in the MOND context UDGs must be out-of-equilibrium objects on their first infall onto the cluster.

astro-ph.GA↗