Instrumentation and Methods for Astrophysics · Cross-listed from astro-ph.EP
A Probabilistic Trajectory and Dispersion Study of Martian Tumbleweed Rover Swarms in Global Dust Storm Conditions
Emma-Catherine Belhadfa and Gabriele MT D'Eleuterio
Global Dust Storms (GDSs) dominate the Martian
climate, yet the mechanisms of dust lifting, transport, and deposition remain poorly constrained because in situ observations are limited: landers are stationary, and their Radioisotope Thermoelectric Generators (RTGs) contaminate local measurements. Our solution is a swarm of lightweight, wind-propelled spherical rovers – Dust Rovers (DRs) – that follow natural wind patterns to collect high-spatial-resolution in situ dust data during storms. Here, we quantify the dispersion and communication connectivity of this robotic architecture. Using 625 wind states calculated using the Mars
Climate Database (MCD) for the MY34 storm at the Curiosity site, we build a bivariate-normal model of the near-surface wind velocity and characterise its diurnal structure. Rover trajectories and swarm dispersion are propagated with three integrators: the Euler and fourth-order Runge-Kutta schemes for the deterministic dynamics, and a
stochastic Ornstein-Uhlenbeck formulation that simulates turbulent gust forcing. Dispersion is controlled by the unmeasured gust correlation time: with a static rolling threshold enforced, the white-gust case (tau_g = Delta t = 1 s) provides a two-rover separation below 1 m over an hour (0.44 km over a sol), whereas correlation times of 10-60 s yield 6-25 m over an hour (1.4-3.3 km over a sol). We demonstrate that a 20-rover swarm needs only a 1.6-4.1 km per-rover range to remain connected over a sol, and stays connected across the plausible envelope – for gust amplitudes up to twice the nominal value with tau_g <= 60 s, and at the nominal amplitude up to tau_g ~ 150 s.
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Instrumentation and Methods for Astrophysics · Cross-listed from astro-ph.EP
aRieL - Reinforcement Learning for the Ariel Space Telescope Scheduling
James Kostas Ray, Kai Hou Yip, Alexandra Thompson et al. (+2 more)
The Ariel mission will conduct a population level survey of hundreds of
exoplanet atmospheres, requiring observations to be scheduled across a large and diverse catalogue of potential targets. By framing Ariel scheduling as a long-horizon optimisation problem in which individual decisions affect both mission time and the scientific opportunities available later in the survey, Reinforcement Learning (RL) can be utilised as a framework for target selection. We introduce aRieL, a simulated observing environment coupled to a Set
Transformer Proximal Policy Optimisation (PPO)
agent that is trained to generate a scheduling policy. The RL policy consistently finds a strong compromise between competing survey objectives, producing large Tier~1 samples while maintaining high Tier~3 completion, population coverage, and observing efficiency when compared to a set of baseline heuristic polices. This behaviour is retained when the candidate catalogue is substantially expanded, while modifications to the reward function produce corresponding changes in the learned observing strategy. We further show that a policy trained on the baseline mission scenario can generalise without retraining to a modified scenario requiring repeated Tier~3 observations, revealing the resulting trade-off with the wider survey. These results demonstrate that reinforcement learning provides a flexible approach to large-scale astronomical scheduling, in which the observing strategy can respond directly to changes in mission state and to the scientific priorities, and motivate its wider exploration for observatories with similarly complex, state dependent scheduling problems.
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All authors James Kostas Ray, Kai Hou Yip, Alexandra Thompson, Luís F. Simões, Nikolaos Nikolaou
Instrumentation and Methods for Astrophysics
Fisher and DALI posterior approximations for testing gravity with gravitational waves
Felipe A. da Silva Barbosa, Davi C. Rodrigues, Josiel Mendonça Soares de Souza et al. (+1 more)
We investigate the accuracy and computational efficiency of posterior approximations for gravitational-wave (GW) parameter estimation based on Fisher
Matrix and its higher-order extension, the Derivative Approximation for LIkelihoods (DALI), in the context of tests of General Relativity using parameterized deviations in the inspiral waveforms (the TIGER formalism). We adopt the IMRPhenomD waveform and compare the exact posterior sampling to the following approximations in increasing order of complexity: the traditional Fisher
Matrix, the Fisher sampling (singlet-DALI), the doublet-DALI and the triplet-DALI. We find that both singlet and doublet-DALI are significantly faster than the exact posterior by factors between 20 and 1000, depending on the inspiral SNRs – larger gains correspond to larger SNRs. These gains are considerably larger for more complete waveforms, such as IMRPhenomHM. To evaluate the accuracy of the approximations, we use Jensen-Shannon divergence. We conclude that both singlet and doublet-DALI systematically improves over the standard Fisher
Matrix approximation, while triplet-DALI shows large variability, limiting its practical advantage. We also introduce a code with a new implementation of Fisher and DALI analysis (Symbolic DALI, SymDALI) which computes derivatives symbolically via Wolfram
Language.
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All authors Felipe A. da Silva Barbosa, Davi C. Rodrigues, Josiel Mendonça Soares de Souza, Miguel Quartin
Instrumentation and Methods for Astrophysics
PTED: A multi-dimensional two-sample test for scientific inference and generative machine learning
Connor Stone
Two-sample tests are widely applicable in inference and generative modelling, yet users frequently fall back on heuristics and visual inspection due to lack of an accessible test that operates in multiple dimensions. I present Permutation Test using the Energy Distance (PTED), a Python implementation of a powerful two-sample test described in Székely and Rizzo [2004]. The test statistic is the energy distance, a metric on probability distributions that admits a natural sample
estimator built from pairwise distances. A permutation test is run on energy distances to produce an exact two-sample test. Because the statistic depends on the data only through pairwise distances, the test applies in high dimensions, on learned feature representations, at large or small or imbalanced sample sizes, and to any data type on which a distance can be defined. An approximation to the formula allows PTED to scale linearly with both number of dimensions and samples while retaining most discriminative power. I highlight a number of instructive sensitivity tests on PTED alongside other multi-dimensional two-sample tests for comparison. PTED is the only method that is sensitive to all tests, though conventional Maximum Mean Discrepancy is identical for all but an over-fitting test.
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Instrumentation and Methods for Astrophysics · Cross-listed from astro-ph.HE
VLBI Astrometric Procedure and Performance with CHIME/FRB Outriggers
Shion Andrew, Mattias Lazda, Calvin Leung et al. (+15 more)
Precise localizations of fast
radio bursts (FRBs) to specific environments within their host
galaxies with very-long-baseline interferometry (VLBI) have provided valuable insights into the nature of their progenitors. To date, such localizations have relied primarily on traditional VLBI facilities, whose observational capabilities limit them to targeted follow-up on a small subset of repeating FRBs that are observable at frequencies above $\sim$1 GHz. CHIME/FRB Outriggers is a low-frequency (400–800 MHz), wide-field VLBI network designed to overcome these limitations, aiming to localize a substantial fraction of CHIME-detected FRBs to $\sim$50 mas precision. In this work, we detail the analysis procedure used to localize FRBs with the CHIME/FRB Outriggers VLBI array. With over a thousand test localizations consisting of continuum calibrators and
pulsars with known VLBI positions, we provide a comprehensive demonstration of the VLBI array's ability to robustly meet, and under some conditions exceed, the nominal astrometric specification required for the central science goals of CHIME/FRB Outriggers. By demonstrating that a wide-field survey instrument can deliver routine $\sim$50 mas astrometry on one-off FRBs, this work lays the foundation for the upcoming generation of wide-field VLBI FRB surveys and the population-scale FRB samples they will enable.
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All authors Shion Andrew, Mattias Lazda, Calvin Leung, Kiyoshi W. Masui, Danté M. Hewitt, Jacob Willis, Alyssa Atkinson, Kevin Bandura, Shami Chatterjee, Nina V. Gusinskaia, Adam E. Lanman, Juan Mena-Parra, Kenzie Nimmo, Aaron B. Pearlman, Ziggy Pleunis, Alexander W. Pollak, Gurman Sachdeva, Vishwangi Shah
Instrumentation and Methods for Astrophysics
An X-ray Extremely wide-field Spectroscopic Surveyor (XESS, or "chess")
W. P. Maksym, M. Ulmer, S. Friedrich et al. (+20 more)
We propose an X-ray observatory, the X-ray Extremely wide-field Spectroscopic Surveyor (XESS, pronounced "Chess") to bring high energy resolution X-ray spectroscopy into the era of synoptic surveys and of time domain and multi-messenger astrophysics (TDAMM). Such an observatory is optimally suited to addressing key elements of the 2020 Decadal Survey on Astronomy and Astrophysics.
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All authors W. P. Maksym, M. Ulmer, S. Friedrich, F. Tombesi, R. Arcodia, S. P. Singam, S. Bongiorno, N. Thomas, M. Elvis, Ming Sun, John ZuHone, M. Lynne Saade, Philip Kaaret, Doug Swartz, Erin Hicks, Ming-Yi Lin, Pallavi Patil, Jimmy Irwin, Shrabani Kumar, Kirtan Dixit, Sudip Chakraborty, Andrea Gnarini, Steven Ehlert
Instrumentation and Methods for Astrophysics
Gestalt: a meta-foundation model for astronomy
Michael J. Smith and Shashwat Sourav
The Platonic Representation Hypothesis predicts that sufficiently scaled foundation models converge on a shared representation of the world. As each non-converged model gives a noisy view of a common structure when passed the same input, we ask whether we can combine models into a representation that outperforms its individual components. We test this on
galaxies: we embed images via a basket of 22 frozen foundation models from eight families, whiten each view, and take a randomised SVD of the
embedding concatenation. The resulting 1024-dimensional
embedding outperforms every basket member on 19/21 of our tested metrics for physical property and
galaxy morphology estimation for HSC, JWST, and DESI Legacy Survey imagery. We find that performance rises with basket size and basket architectural diversity, and that the meta-foundation model's performance transfers across astronomical surveys. We conclude that a useful astronomical foundation model can be assembled from existing generalist models with no training required beyond a single unsupervised projection. By leveraging the community's already-spent work, we save a lot of compute: a fresh pre-train of a comparable single-domain model would cost $\mathcal{O}(10^{4}$–$10^{5})$ A100 GPU hours (emitting several tonnes of CO$_2$eq.), whereas assembling Gestalt requires minutes on a single machine.
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Instrumentation and Methods for Astrophysics · Cross-listed from gr-qc
Fast and Flow-rious: Gravitational Wave Background Bayesian Model Comparison using Normalizing Flows and Nested Sampling
David C. Wright, Aaron D. Johnson, Jeffrey S. Hazboun et al. (+1 more)
In 2023,
pulsar timing array (PTA) collaborations around the world announced evidence for a nanohertz gravitational-wave background (GWB). Beyond increasing the detection significance, the next major goal is to characterize the GWB and identify its source(s). The typical model-comparison metric used in PTA analyses is the Bayes factor (BF). However, BF computation methods used in the PTA literature, such as product-space sampling and thermodynamic integration, are computationally expensive or inefficient if the models compared are dissimilar or non-nested. In this work, we introduce a PTA model-comparison method using a combination of normalizing flows, nested sampling, and a PTA likelihood that is marginalized over all non-GWB parameters. The flows are trained using a newly-developed Python package coppuccino, which learns the data's copula: the dependence structure after transforming to uniform marginals. We validate the method on both analytic targets and simulated PTA data, recovering the analytic evidence within error bars and achieving an area-under-the-curve (AUC) score of 0.996 when treating the BFs as binary classifiers of the injected models. The result is a method to perform
Bayesian model comparison of arbitrary GWB spectral models in minutes on consumer hardware.
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All authors David C. Wright, Aaron D. Johnson, Jeffrey S. Hazboun, William G. Lamb
Instrumentation and Methods for Astrophysics · Cross-listed from astro-ph.GA
Detecting HI Self-Absorption using Neural Networks
Eric G. M. Muller, Naomi M. McClure-Griffiths, Hiep Nguyen et al. (+5 more)
Cold atomic hydrogen plays a crucial role in the life cycle of interstellar gas. It serves as the intermediary phase in the condensation and cooling processes that bridge the warm diffuse gas in and around
galaxies, and the cold molecular gas that drives
star formation. HI self-absorption in the 21-cm emission line is the most direct observational tracer of cold HI gas that does not require a continuum background source, yet its systematic extraction remains a long-standing challenge. Existing methods of self-absorption identification rely on subjective by-eye inspection or modelling of the underlying emission, making repeatable, thorough, and large-scale applications difficult. We present a lightweight convolutional neural network designed to detect self-absorption features and infer their velocities via a post-hoc process, without assumptions about the underlying emission or the use of ancillary data. Trained on synthetic emission spectra, the neural network achieves 96.5 per cent accuracy, 96.0 per cent precision, and 97.0 per cent recall on held-out synthetic data. Applied to observed 21-cm emission data from the Riegel–Crutcher cloud and giant molecular filament regions towards the Galactic Plane, the network recovers the spatial distributions and velocities of known self-absorption structures when compared to previous analyses and ancillary 13CO emission data. Crucially, the neural network is computationally efficient, processing detections for ~15,000 spectra per second on a single consumer laptop GPU, enabling real-time cold HI detection at the data rates anticipated by next-generation facilities such as the Square Kilometre Array.
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All authors Eric G. M. Muller, Naomi M. McClure-Griffiths, Hiep Nguyen, Matthew J. Alger, Frances Buckland-Willis, J. R. Dawson, Min-Young Lee, Antoine Marchal
Instrumentation and Methods for Astrophysics · Cross-listed from astro-ph.EP
Constraining exoplanet population parameters with Kepler and simulation-based inference
Chloé Padois, Friedrich Anders and Daniel del Ser
In the context of the rapidly growing field of galactic
exoplanet population studies, we present an
exoplanet simulator capable of generating synthetic populations for millions of
stars in a few seconds (Padois et al., 2025). Our framework combines observed
exoplanet detections with the latest planetary formation models, incorporating dependencies between host
star properties and
planet occurrence rates, orbital period distributions, and other key demographic features. However, many model parameters remain uncertain and model-dependent. To constrain some of the most critical ones, we couple our simulator with simulation-based inference (SBI): we define a set of 44 free parameters describing the population of different
planet types (occurrence rates, mass-period distribution, mass-radius relation, etc.), run millions of simulations sampling from broad prior distributions, simulate their detectability by Kepler, and train a machine-learning
algorithm to learn the relationships between input parameters and the resulting "observed" synthetic populations. The best-fit parameters are then inferred by comparing model outputs to real observations, for which we adopt the Kepler confirmed
planet catalogue as our reference
dataset, since it provides the largest homogeneous sample from a single facility. We present preliminary results for the
planet occurrence rate as a function of stellar mass and
metallicity, across different
planet types, along with constraints on several key simulation parameters such as the orbital period distribution and the inclination dispersion within planetary systems. These refined parameters enable us to refine our yield predictions for upcoming
exoplanet detection missions, including PLATO, Roman, and HAYDN.
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Instrumentation and Methods for Astrophysics · Cross-listed from astro-ph.GA
A Full-Sky SPHEREx Map of Integrated 3.3 and 3.4 {\mu}m PAH Emission
Roke Cepeda-Arroita, Clive Dickinson, J. Alberto Rubiño-Martín et al. (+4 more)
Polycyclic aromatic hydrocarbons (PAHs) are ubiquitous in the interstellar medium and are among its smallest carbonaceous grains. Their infrared emission bands trace this small-grain population together with the interstellar radiation field that excites it, and are widely used to diagnose photodissociation regions, dust processing and
star formation. We present full-sky maps of the integrated radiance of the 3.3 $\mu$m aromatic C-H stretch and the adjacent 3.4-3.5 $\mu$m aliphatic complex, from SPHEREx QR2 Level-2 data. Existing all-sky PAH tracers are broadband, so the band emission can only be inferred as an excess over stellar and continuum emission; SPHEREx samples the spectrum finely enough for the bands to be fitted directly. We mask flagged detector pixels, subtract the per-pixel zodiacal-light model, subtract point sources with an empirically derived PSF, and coadd the results into inverse-variance-weighted HEALPix maps at 2.6' FWHM and Nside=4096 in 102 narrowband channels across Detector 4 (2.42-3.82 $\mu$m). At every sky pixel we fit a continuum and the two bands. The combined band radiance is the primary product; its decomposition into the aromatic and aliphatic components is released alongside it as an estimate, with its modelling systematic stated. These are the first full-sky maps of PAH band emission, resolving the 3.3 and 3.4-3.5 $\mu$m bands spectrally, and the first full-sky PAH maps from SPHEREx to be released. Because the 3.3 $\mu$m band is emitted preferentially by the smallest PAHs, the maps trace directly a grain population, including the candidate carriers of anomalous microwave emission, that broadband PAH proxies reach only indirectly. The maps and their associated products, including a reliability mask, are publicly available with an explanatory supplement at https://research.iac.es/proyecto/radioforegroundsplus/pages/data-products/spherex-pahs.php.
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All authors Roke Cepeda-Arroita, Clive Dickinson, J. Alberto Rubiño-Martín, Ricardo T. Génova-Santos, Gabriel A. Hoerning, Stuart E. Harper, Debabrata Adak
Instrumentation and Methods for Astrophysics
An End-to-End Numerical Framework for Tidal Disruption Events with AthenaK
Hong-Xuan Jiang, Mengqi Yang, David A. Velasco-Romero et al. (+4 more)
In a tidal disruption event (TDE), a black hole (BH) tears apart a
star, whose bound debris returns over many orbits to form an
accretion disk. We present a framework built on the GPU-accelerated finite-volume code AthenaK that follows these stages in a single simulation with self-
gravity throughout. We extend AthenaK with a moving frame, restart remapping between domains, an adaptive-mesh multigrid Poisson solver, a tabulated hydrogen–helium equation of state including recombination, a dual-energy update for the cold supersonic debris stream, a moving BH potential with excision, and localized adaptive time stepping (LAT), which speeds up the production run more than threefold. Each component is validated separately and in combination: the
gravity solver maintains an isolated Lane–Emden sphere to a density error of $2.0\times10^{-3}$ over 16.3 dynamical times, and dual-energy recovery reduces the pressure error of a Mach $7.75\times10^7$
entropy wave from 15.7 to $4.0\times10^{-12}$. We demonstrate the framework with a Newtonian $\beta=1$ disruption of a $1\,M_\odot$, $1\,R_\odot$
star by a $10^3\,M_\odot$ BH. The debris has the expected energy spread, insensitive to the self-
gravity update interval, splits evenly into bound and unbound material, and yields a fallback rate approaching $t^{-5/3}$ at late times. At the pericenter nozzle, the thermal energy gained in the high-resolution run matches the vertical kinetic energy lost to within 6%, whereas the fiducial run, with 4–8 times coarser
cells, overheats the thinnest early stream through numerical dissipation. Their heating agrees to 10% once the returning stream thickens. Multifrequency LTE post-processing produces synthetic images and luminosities.
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All authors Hong-Xuan Jiang, Mengqi Yang, David A. Velasco-Romero, Fangyuan Yu, Jing-Ze Xia, Xinyu Li, Yosuke Mizuno
Instrumentation and Methods for Astrophysics · Cross-listed from astro-ph.HE
ALMA - CTAO synergies
Paola Andreani and Maria Diaz-Trigo
This article aims to raise awareness within the ESO astronomical community of the scientific opportunities offered by the upcoming Cherenkov
Telescope Array, CTAO, highlighting cases where combining millimetre/submillimetre capabilities of the Atacama Large Millimeter / submillimeter Array, ALMA with the CTAO's very high energy, TeV, observations enables new avenues for discovery. Many astrophysical sources and environments exhibit emission processes detectable in both regimes; however, the synergies extend beyond shared emission mechanisms and include broader physical processes and observational connections linking millimetre, submillimetre and TeV energies.
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Instrumentation and Methods for Astrophysics
On-sky commissioning and technical performance of Proto-NUX at Pic du Midi
Rasjied Sloot, Rudy Wijnands, Steven Bloemen et al. (+3 more)
The Near-Ultraviolet eXplorer (NUX) is a proposed ground-based observing facility designed to expand near-ultraviolet (NUV) astronomy by enabling observations in the 300–350 nm range from Earth. The full system is intended to consist of four modified 36 cm Celestron RASA
telescopes with custom NUV-optimized optics and UV-sensitive CMOS detectors, providing a total field of view of about 67 square degrees for high-cadence searches for fast, hot transients. To validate this concept, a single-
telescope prototype, Proto-NUX, was constructed. Its optical design and development are described in previous work. Initial sea-level tests in the Netherlands showed that the instrument could be operated successfully and that the main technical requirements were met. In March 2026, Proto-NUX was deployed at the Pic du Midi Observatory in the French Pyrenees, at an altitude of 2877 m, for its first high-altitude on-sky commissioning campaign. The campaign tested the full system under realistic observing conditions, including deployment, remote operation, calibration procedures, detector behaviour, image quality, tracking, ghosting, and simultaneous optical monitoring. The observations demonstrate that Proto-NUX is a viable platform for ground-based NUV observations, but also show that the system was not yet operating at its expected optical limit. In particular, the delivered NUV point-spread function was broader than expected, with likely contributions from residual collimation error, defocus, chromatic effects, and internal reflections. The detector dark current was also found to be a significant contributor to the noise budget. We discuss these limitations and identify the main improvements needed for future Proto-NUX campaigns and for scaling the concept to the full NUX facility.
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All authors Rasjied Sloot, Rudy Wijnands, Steven Bloemen, Hans Ellermeijer, Alexander Hoogerbrug, Rik ter Horst
Instrumentation and Methods for Astrophysics · Cross-listed from astro-ph.EP
Detecting atmospheres in the presence of stellar contamination: an empirically calibrated framework applied to four JWST/NIRSpec PRISM transits of TRAPPIST-1c
Alexander D. Rathcke and Lars A. Buchhave
Transmission spectroscopy of rocky
exoplanets orbiting late M dwarfs is limited by stellar contamination rather than
photon noise. We present a framework for this contamination-dominated regime, fitting all transit epochs jointly with a Gaussian process per epoch for the time-varying stellar contamination and a single atmospheric model shared across epochs. We use pure CO$_2$, H$_2$O, and CH$_4$ models scaled by their peak-to-trough amplitude, allowing us to measure how large a signal must be to be recovered. We calibrate this framework using injection-recovery tests and 17 archival JWST/PRISM transits of the airless sibling TRAPPIST-1 b to quantify the spurious signals that stellar contamination alone produces. Applied to four JWST/NIRSpec PRISM transits of TRAPPIST-1 c, our framework recovers injected CO$_2$ signals without bias and reaches detection thresholds of 146 (171) ppm for moderate (strong) evidence, compared with 96 ppm if there were no stellar contamination. CO$_2$ and CH$_4$ produce no spurious signals on the airless control
planet, whereas H$_2$O shows a persistent stellar floor of $+55 \pm 34$ ppm. TRAPPIST-1 c is consistent with a featureless, airless baseline for all three molecules. Our 95 per cent upper limit of 90 ppm for CO$_2$-shaped signals effectively rules out clear, isothermal pure-CO$_2$ atmospheres ($\geq$0.1 bar) and N$_2$-dominated atmospheres with 1 per cent CO$_2$ ($\geq$1 bar) at the
equilibrium temperature. An empirical scaling study using 2 to 10 transits of TRAPPIST-1 b shows the CO$_2$ detection threshold decreasing with the number of transits $k$ as $k^{-0.6}$, reaching 80 ppm at ten transits, while water searches become limited by the stellar floor beyond about six transits. CO$_2$ atmospheres, by contrast, are detectable when we utilize our framework to account for stellar activity at the cost of roughly 1.5 to 2 times as many transits.
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Instrumentation and Methods for Astrophysics · Cross-listed from gr-qc
Scaling RIFT 1: Extending RIFT to third-generation gravitational-wave analyses
R. O'Shaughnessy, A. Jan, J. Lange et al. (+1 more)
Interpretation of compact binary signals observed by third-generation gravitational-wave detectors is complicated by their high signal-to-noise ratios, often reaching hundreds to thousands, and by time- and frequency-dependent detector responses arising from Earth's rotation and the detectors' large physical extent. Building on prior work for LISA, we implement generic, robust extensions of the RIFT parameter estimation pipeline that enable interpretation of these sources using any existing waveform model, without recourse to intermediate pretrained data products like reduced-order approximations or normalizing flows. We demonstrate end-to-end recovery of finite-size binary-
neutron-
star injections in a three-site next-generation network to $\mathrm{SNR}=1000$, localizing the source's extrinsic parameters to $0.8\times10^{-3}\,\mathrm{deg}^2$ in minutes on a single GPU.
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All authors R. O'Shaughnessy, A. Jan, J. Lange, R. Mechum
Instrumentation and Methods for Astrophysics · Cross-listed from gr-qc
Gravitational-Wave Inference For Noise PSD Jumps Across Data Gaps
Jian-Ming Yan and Zong-Kuan Guo
Long-duration gravitational-wave (GW) inference inevitably encounters data gaps, which are often accompanied by abrupt, non-stationary changes in the detector noise power spectral density (PSD). Conventional practice typically analyzes the pre-gap and post-gap data separately, causing avoidable information loss and potentially degrading parameter inference. We build on
Bayesian gap augmentation in the Wilson–Daubechies–Meyer (WDM) time–frequency domain and focus on a practically important failure mode: short gaps with large PSD amplitude jumps. In this regime, the local diagonal (Whittle-like) approximation that underpins the WDM likelihood can no longer be relied upon in the time direction near the gap edges, which may lead to biased and/or less robust inference unless the local diagonal validity is restored without resorting to full segmentation. We propose a two-stage, validity-driven remedy. First, frequency-domain prior-predictive prewhitening (PW) incorporates endpoint-informed noise information to mitigate the dominant mismatch responsible for poor diagonal behavior, enabling a more favorable WDM representation while retaining computational tractability. Second, when a time-direction smoothness criterion still fails, we apply adaptive gap expanding (AGE), selectively enlarging only the minimal neighborhood around the gap boundaries needed to restore local diagonal validity. Toy-model simulations with chimeric PSD transitions show that WDM+PW+AGE yields substantially tighter and more accurate posteriors than single-sided pre-gap/post-gap analyses, while maintaining computational efficiency suitable for next-generation missions.
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Instrumentation and Methods for Astrophysics
First laboratory demonstration of iEFC for high-contrast imaging at small angular separation on ELT scales
Charles Sallard, Patrice Martinez, Alain Spang et al. (+7 more)
Implicit electric field conjugation (iEFC) is an efficient, model-free wavefront control technique for dark hole creation. Its model-free nature, relying on calibration with real data rather than a synthetic model of the instrument, makes it uniquely versatile and highly attractive. It has been successfully demonstrated in a variety of environments, both in the laboratory and on-sky, using different experimental platforms and coronagraphs. A common characteristic of these demonstrations is that the resulting DHs exhibit moderate to large inner working angles and wide fields of view, often extending over several tens of $\lambda/D$. Here, we report the first demonstration of iEFC at small angular separations, achieving a DH spanning 1 to 4 $\lambda/D$ on a segmented pupil in H-band under laboratory conditions. This combination of aggressive IWA and reduced FoV highlights the uniqueness of the SPEED facility (segmented pupil experiment for
exoplanet detection). SPEED is a high-contrast imaging testbed designed for extremely large
telescopes (ELTs), developed in the context of the ELT/PCS instrument (planetary camera and spectrograph), currently in pre-phase A. Its small-IWA coronagraph, an APCMC (apodized pupil complex mask coronagraph), and its multi- and out-of-pupil-plane deformable mirror (DM) architecture are specifically optimized for DHs at very small IWAs, down to the diffraction limit. Our results demonstrate (i) that widely separated out-of-pupil DMs are well suited to generating DHs with IWAs down to 1 $\lambda/D$, and (ii) the effectiveness of iEFC, when combined with the SPEED wavefront control architecture, for high-contrast imaging with segmented
telescopes. Ultimately, these results pave the way toward meeting the stringent high-contrast requirements of ELT-class instruments, opening new opportunities for
exoplanet detection at unprecedented angular resolutions.
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All authors Charles Sallard, Patrice Martinez, Alain Spang, Kian Milani, Lyu Abe, Mathilde Beaulieu, Carole Gouvret, Aurélie Marcotto, Julien Dejonghe, Olivier Preis
Instrumentation and Methods for Astrophysics · Cross-listed from astro-ph.EP
Detecting Extragalactic Exoplanets With Fast Radio Burst Nanolensing
Kenneth F. Olibrice and Dylan L. Jow
Fast
Radio Bursts (FRBs) are spatially and temporally compact sources at cosmological distances. As background sources, they will be uniquely sensitive to the gravitational
lensing of
exoplanets outside of our own
galaxy. We present a method to detect extragalactic
exoplanets using planetary echo nanolensing-periodic, nanosecond-scale variations in the relative time of arrival (ToA) between an initial FRB and its microlensed echo as the
planet orbits its host
star. For a simple binary lens model, we demonstrate that measuring the amplitude and period of these ToA fluctuations enables a determination of the
exoplanet mass and orbital parameters. FRBs will be most sensitive to echo nanolensing by lenses within ten megaparsecs of the Milky Way as well as within ten megaparsecs of the FRB host, opening the door to detections of
planets at a range of
redshifts. We estimate that at least one in a million FRBs will exhibit this phenomenon (i.e. an optical depth of $\tau_l = 10^{-6}$). While current burst rates and low repeating-FRB fractions make a detection challenging in the immediate term, upcoming high-volume surveys and next-generation arrays (such as the SKA Phase 2) will bring the unambiguous detection of extragalactic
exoplanets across cosmological
redshifts into possibility.
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Instrumentation and Methods for Astrophysics
LUMA: A CNN for Strong Gravitational Lens Searches in Astronomical Imaging
Giovanni Vincenzo Donatiello, Achille A. Nucita, Francesco De Paolis et al. (+2 more)
We present LUMA, a convolutional neural network (CNN) pipeline for the automated detection of strong gravitational lenses in simulated astronomical imaging. The method combines a physically motivated preprocessing stage, which enhances faint arc and ring features, with a compact three-block CNN trained using class reweighting and modern learning-rate scheduling. On simulated data, the model reaches test accuracies of about $96$% and receiver operating characteristic (ROC) area-under-the-curve (AUC) values of $\simeq 0.99$ for the non-trivial classes, while confusion-
matrix analysis shows high completeness and purity for lens candidates. These results demonstrate that relatively lightweight CNN architectures can provide a competitive baseline for strong-lens searches, and they motivate future extensions toward real survey images and
transformer-based models.
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All authors Giovanni Vincenzo Donatiello, Achille A. Nucita, Francesco De Paolis, Antonio Franco, Francesco Strafella
Instrumentation and Methods for Astrophysics · Cross-listed from astro-ph.GA
Morphological Identification of Dynamical Regimes in MHD Turbulence with ScaleAware-JEPA
Mengke Zhao, Guang-Xing Li and Keping Qiu
The morphology of turbulent interstellar gas reflects the coupled action of
turbulence,
magnetic fields, and self-
gravity, but density amplitude alone does not uniquely specify dynamical state. We use ScaleAware-JEPA to learn multiscale structural coordinates from density alone and test whether these learned coordinates organize dynamical states more clearly than density-based conditioning. Clump-like, filament-like, and diffuse-like latent neighborhoods show an ordered progression in turbulent velocity scaling that is not reproduced by density-selected populations. Velocity and
magnetic fields are withheld during training and used afterward as post-training physical diagnostics: MHD states occupy ordered but overlapping regions of the learned representation, and dimensionless dynamical balances vary coherently across the full latent atlas. These results show that a representation learned from density morphology can acquire a geometry that is systematically organized by established MHD diagnostics. Multiscale morphology therefore provides a useful coordinate for dynamical state beyond density amplitude alone.
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Instrumentation and Methods for Astrophysics · Cross-listed from astro-ph.EP
A Bayesian Inference Framework for Binary Lens Events Fully Incorporating Higher-Order Effects
Kansuke Nunota and Kento Masuda
Higher-order effects, such as microlensing parallax and lens orbital motion, are essential for characterizing the physical properties of microlensing planetary systems including their masses and distances. In practice, higher-order effects are often included in light-curve modeling only when their signals are strong, because their inclusion in weakly constrained cases tends to drive the inferred parameters toward regions of parameter space that are disfavored by standard Galactic models. This binary choice over physically continuous effects introduces an event-dependent selection function that complicates population-level interpretation and prevents strong and weak detections from being combined within a single uniform framework. In this work, we investigate the origin of instabilities associated with higher-order effects, and show that such instabilities can arise from a mismatch between commonly adopted uninformative priors in the light-curve parameter space and the distributions predicted by Galactic models. Motivated by this, we develop a new
Bayesian framework that enables higher-order effects to be incorporated in a stable and uniform manner. As part of this framework, we introduce Galactic Prior Modeling Engine (gapmoe), a dedicated tool that enables efficient evaluation of the Galactic prior density and allows it to be incorporated directly into the light-curve inference. Using simulated events, we demonstrate that our framework robustly recovers the true physical parameters while fully accounting for microlensing parallax and lens orbital motion. This framework eliminates the need for ad-hoc model selection and provides a scalable and statistically consistent pathway for analyzing the large samples expected from next-generation surveys such as the Roman Space
Telescope.
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Instrumentation and Methods for Astrophysics
ECLIPSE-X: Plate-Scale Calibration as the Binding Constraint on Relativistic Astrometry with Spacecraft Navigation
Pedro J. Llanos
Precision stellar astrometry near the Sun can constrain the parametrized post-Newtonian (PPN) parameter gamma through gravitational light deflection. This study develops ECLIPSE-X, a localized multi-frame estimation architecture in which one persistent relativistic parameter is estimated simultaneously with frame-dependent spacecraft line-of-sight and roll states, separated by their differing spatial signatures and by the persistence of gamma across frames. The aim is not a competitive determination of gamma, but an assessment of whether such a joint solution and its formal covariance stay statistically valid under realistic astrometric model error. A synthetic field of 250
stars spanning 1.22-8 apparent solar radii is observed over 40 frames at 5-s cadence; weighted least squares estimates gamma with three navigation states per frame, and Schur-complement marginalization gives the formal uncertainty in gamma after navigation coupling. The nominal solution recovers gamma = 1.0001198167 with sigma_gamma = 3.075104e-4, and a 1000-realization ensemble confirms calibration. Perturbations unknown to the
estimator identify plate-scale mismatch as the dominant failure mechanism: a 300-ppm scale error alone inflates the empirical-to-formal uncertainty ratio to 7126.6 with zero 1-sigma and 2-sigma coverage, against 2.18 for catalog-coordinate mismatch and 1.12 for a frame-coherent radial perturbation, while every realization converges numerically: convergence does not guarantee statistical validity. Augmenting the state with a plate-scale parameter restores consistency, at a fixed 21.2% cost in the precision of gamma set by the correlation between the two persistent parameters. A calibration sweep shows the unaugmented
estimator is covariance-consistent only below sigma_p = 2.7884e-8, so the augmented state is a requirement, not a refinement, for near-Sun relativistic astrometric
estimators.
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Instrumentation and Methods for Astrophysics · Cross-listed from astro-ph.CO
Everything Everywhere All At Once: A Hierarchical Framework for Mapping Anisotropies in the Local Universe
Aryan Bansal, Edward N. Taylor, Michelle Cluver et al. (+1 more)
We present a
Bayesian hierarchical forward model for mapping the local luminosity-density field from heterogeneous
redshift surveys. By dividing the survey volume into angular
cells and
redshift shells, we model the observed
galaxy distribution directly in apparent-magnitude and
redshift space. This allows us to infer
cell-level luminosity functions and densities while preserving each survey's unique selection function. Simultaneously, the ensemble of
cells constrains the
cosmic-mean luminosity function, its
redshift evolution, and the hyperparameters describing
cell-to-
cell variation. We apply the framework to 2MRS, 6dFGS, and GAMA over $0.005<z<0.065$, combining wide sky coverage with deeper and fainter
galaxy samples. The model successfully recovers global $J$-band luminosity-function parameters and a mean luminosity density consistent with previous low-
redshift measurements. The inferred luminosity-density scatter decreases with volume, consistent with the
cosmic-variance amplitude expected in a $\Lambda$CDM universe. Within the volume we probe, we find no evidence for a large coherent underdensity. Instead, the local Universe is broadly consistent with smooth
evolution and
cosmic variance. The nearest shell at $z \simeq 0.01$ is the clearest exception, lying $\simeq 0.12$~dex ($\simeq 25\%$) below the smooth global model, a $-2.6\sigma$ underdensity with a one-sided significance of $\simeq 99.5\%$. This framework offers a scalable architecture for mapping
cosmic density fields with next-generation wide and deep surveys.
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All authors Aryan Bansal, Edward N. Taylor, Michelle Cluver, Matthew Colless