Instrumentation and Methods for Astrophysics · Cross-listed from cond-mat.supr-con
Nonequilibrium pulse dynamics and metastable latching in nonlinear kinetic inductance detectors
M. Rouble, C. Albert, P. Day et al. (+3 more)
Microwave kinetic inductance detectors are typically operated at high readout power to raise the detector signal above system noise. At sufficiently large readout power, the current-dependent kinetic inductance couples the detector response to its readout bias. Using a nonlinear resonator framework and time-domain
circuit calculations, we show that the amplitude, shape, and relaxation time of the driven detector's response depend on both the absorbed energy and on the readout bias. Strongly driven bias points produce amplified, extended, and non-exponential pulse responses. Qualitative agreement between calculated and measured pulse responses indicates that these effects are dominated by the driven nonlinear resonator dynamics rather than by altered quasiparticle dynamics. Beyond resonance bifurcation, sufficiently large pulse events drive the resonator between stable branches, resulting in a metastable latched state which persists after the quasiparticle transient has decayed. The pulse energy required for branch switching is set by the readout bias, suggesting a mode of triggered detection with an in-situ tunable threshold. Although nonlinear operation requires calibration of the bias- and energy-dependent response, the enhanced pulse amplitude and duration, together with tunable latching and the ability to select these parameters via the readout operating state, are likely to be of interest for single-
photon and rare-event experiments, especially those limited by amplifier or system noise.
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All authors M. Rouble, C. Albert, P. Day, M. Dobbs, H. G. Leduc, J. Montgomery
Instrumentation and Methods for Astrophysics · Cross-listed from astro-ph.EP
GravDyn: A Python Framework for Gravitational Modeling of Irregular Celestial Bodies Using Polyhedral, mascon, and Series Expansion Methods
Safwan Aljbaae, valerio Carruba, Allan K. de Almeida Jr et al. (+4 more)
GravDyn is an open-source Python package for computing gravitational potentials and accelerations around irregular celestial bodies. It implements three approaches within the same workflow: the constant-density polyhedral method, a layered mascon approximation based on tetrahedral decomposition, and the Potential Series Expansion Method (PSEM). The package handles shape-model preprocessing, builds the selected
gravity representation, and evaluates potentials and accelerations efficiently. The three implemented methods serve different regimes: the polyhedral model provides a reference solution near the surface, the mascon model supports layered internal density structures, and PSEM offers fast evaluation outside the Brillouin sphere once the
polynomial coefficients have been generated. Validation tests against the polyhedral solution show that the mascon and PSEM models reproduce the reference potential with small relative errors while reducing evaluation costs. GravDyn is intended for mission design and studies of spacecraft motion, orbital stability, and gravitational modeling around asteroids and other small bodies.
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All authors Safwan Aljbaae, valerio Carruba, Allan K. de Almeida Jr, Gabriel Antonio Caritá, Antonio F. B. A. Prado, Marcelo L. Mota, Carlos E. Ferreira Lopes
Instrumentation and Methods for Astrophysics
A General Response Theory for Closure Phases through Visibility Nulls and Image Structure
Shokoufe Faraji and Avery E. Broderick
Closure phase is robust to station based phase errors, yet its connection to image structure is often interpreted only qualitatively and a nonzero closure phase is still interpreted mainly through qualitative
symmetry arguments or source specific models. We develop, to our knowledge, the first unified response theory for an arbitrary brightness distribution and an arbitrary zero free observing triangle. Away from visibility zeros, closure phase is naturally a log-visibility observable. This structure yields an exact first order master kernel for image perturbations and a Cartesian moment hierarchy that separates the spatial content of the source change from the sensitivity of the sampled triangle. For a fixed image, the same framework gives a local expansion in
Fourier plane displacement, providing a direct description of closure phase
evolution along observing tracks. We then identify the boundary of this regular regime: at a true visibility null, the log-visibility expansion fails and the phase behavior is controlled by the local properties of the null, including its order and winding charge, distinguishing removable phase wraps, non-null visibility minima, and nulls for which the visibility phase is multivalued. We validate these expansions using structured ring models. We then apply both branches of the theory to EHT data of M87*, and demonstrate the ability to measure asymmetric image structure associated with higher image moments directly from the
evolution of the closure phase. Finally, we show that the same formalism extends naturally to Phase Closure Nulling in optical and infrared interferometry. The resulting framework turns closure phase structure into a quantitative probe of faint and asymmetric source features across interferometric regimes.
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Instrumentation and Methods for Astrophysics · Cross-listed from astro-ph.SR
Agentic Active Learning Meets Visual Embeddings: Finding Anomalies among 370 000 Variable Stars from ASAS-SN
Milan Pesta and Yuan-Sen Ting
Unusual light-curve morphologies can point to rare physical configurations or new phenomena, but automatic searches for anomalies are often dominated by artifacts. Separating genuine anomalies from false positives has traditionally required manual vetting, which does not scale to modern surveys. We present an active learning framework for detecting anomalies in samples of periodic variable
stars, with the vetting delegated to
multimodal large
language model
agents. The initial ranking comes from isolation forests trained on DINOv2 ViT-g/14
embeddings of phase-folded light curves. The
agents iteratively review the light-curve images of the top-ranked candidates and assign relevance scores, which are propagated through the
embedding space to prioritize the next targets. A logistic regression step then extends the search beyond label propagation, and a multi-
agent consensus review filters false positives. Using Gemini~3 Flash
agents, we applied the pipeline to $373\,646$ periodic variables from ASAS-SN Sky Patrol V2.0. Across $51$ iterations, the
agents labeled ${\sim}1\%$ of the sample in roughly $3$ hours at a cost of ${\approx}$ $$40$, yielding our final catalog of $24$ anomalies ($18$ newly reported) and $153$ potentially interesting objects. The anomalies include eclipsing binaries with extremely deep primary eclipses, high-amplitude contact binaries and pulsators, and a symbiotic nova in outburst for over two decades. At the same labeling budget, the initial ranking would have recovered only $30%$ of our catalog, while reaching its lowest-ranked anomaly would have required ${\sim}70$ times the budget. These results demonstrate the feasibility of agentic active learning for anomaly detection in existing and upcoming photometric surveys.
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Instrumentation and Methods for Astrophysics
BlueBird 6 is Fainter than Block 1 Satellites
Anthony Mallama and Richard E. Cole
The mean apparent magnitude for BlueBird 6, the first Block 2 satellite of the BlueBird constellation, is 3.85 +/- 0.10 while that for Block 1 spacecraft is 3.30 +/- 0.07. So, BlueBird 6 is 0.55 magnitudes fainter. Likewise, the means of apparent magnitudes adjusted to a uniform distance of 1,000 km are 4.32 +/- 0.08 and 3.77 +/- 0.06, respectively. This difference is also 0.55 magnitude. The dimming is unexpected because the Block 2 satellites are ~3.5 times as large as Block 1. BlueBird 6 is only 17% as bright as Block 1 spacecraft per unit surface area. Possible explanations are discussed. All BlueBird satellites exceed the brightness limits recommended by the International Astronomical Union. However, they will be fewer in number than other satellite constellations.
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Instrumentation and Methods for Astrophysics
Strong Lensing Cosmology with Population-level Calibrated Neural Ratio Estimation
Sreevani Jarugula, Brian D. Nord, Aleksandra Ćiprijanović et al. (+1 more)
Strong gravitational
lensing contains key information about
cosmic acceleration. Modern and next-generation
galaxy imaging surveys are expected to provide high-quality data on $\mathcal{O}(10^5)$
galaxy-
galaxy lensing systems. The plethora and complexity of the data are likely to present computational challenges for parameter inference methods for fitting high-dimensional likelihoods, which are often analytically intractable. Neural Ratio Estimation (NRE) efficiently computes individual likelihood ratios that can be combined into population-level posteriors. We use simulations to study the capacity of NRE to jointly predict the dark energy equation-of-state parameter $w$ and the total matter density $\Omega_{m}$ from
lensing images and companion spectroscopic information. We also introduce a post hoc posterior coverage calibration procedure that mitigates the model overconfidence that is typically found in neural density estimation applications. Our experiments show that the errors on both parameters decrease with increasing inference population sizes. In particular, for 100 lenses in a standard $\Lambda$CDM Universe, our calibrated NRE model achieves median fractional uncertainty of $22.8\%$ in $w$ and $2.9\%$ in $\Omega_{m}$. This proof of concept demonstrates a potentially scalable approach for efficient cosmological parameter inference with large populations of
galaxy-scale lenses observed in future surveys.
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All authors Sreevani Jarugula, Brian D. Nord, Aleksandra Ćiprijanović, Shubhendu Trivedi
Instrumentation and Methods for Astrophysics · Cross-listed from cs.LG
Traceable Spectral Inference via Influence Functions: Efficient Data Attribution and Error Proxies for the Ariel Mission
Nikki Grens, Luís F. Simões, Kai Hou Yip et al. (+1 more)
Interpretability is critical for machine learning models deployed in scientific space missions such as ESA's Ariel, where ground truth is unavailable during operations and physical plausibility must be assessed. While most explainable AI methods focus on feature attribution, this work investigates training data attribution through influence functions and introduces three key contributions for operational spectroscopy pipelines. First, influence is reformulated in terms of prediction rather than loss, enabling label-free deployment. Second, by leveraging the closed-form ridge solution of an Extreme Learning Machine, infinitesimal prediction influence is efficiently computed. Third, an influence-based conservative error proxy is derived by propagating training residuals through the influence sensitivities. Evaluated against simulated spectra, the proposed proxy correlates strongly with scale and shape-based spectral errors. Furthermore, influence functions enable the identification of the most influential samples and the approximation of the most harmful ones. Together, these results suggest that this approach can serve as an operational framework for scientific machine learning.
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All authors Nikki Grens, Luís F. Simões, Kai Hou Yip, Theresa Lueftinger
Instrumentation and Methods for Astrophysics
Inverting and Up-channelizing Critically-Sampled Polyphase Filter Banks
Stephen Fay, Mohan Agrawal, Hsin Cynthia Chiang et al. (+3 more)
Polyphase filter banks (PFBs) are widely used to channelize digitized time-domain data in real time. However, applications such as
radio astronomy often require higher spectral resolution than real-time systems can provide. We introduce a circulant inverse-PFB formalism and present a new, scalable, fast
Fourier transform (FFT)-based
algorithm for inverting critically sampled PFBs. This formalism explicitly identifies the origin of quantization noise amplification and enables two complementary mitigation strategies: (1) a fast, practical Wiener filter and (2) a rigorous maximum-likelihood reconstruction with time-domain priors. We evaluate both approaches using simulated 4-bit quantized PFB data. For autospectra, Wiener filtering confines reconstruction errors to less than 10% of the channel width, with a peak error below 10%. The maximum-likelihood approach further reduces reconstruction errors as additional time-domain prior information is incorporated, with priors spanning 10% of the time-domain samples reducing worst-case errors to below 2%. This framework enables high-resolution spectral analysis of archival PFB data, facilitating applications including long-baseline interferometry,
pulsar and fast
radio burst searches, and ultra-narrow-band physics experiments.
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All authors Stephen Fay, Mohan Agrawal, Hsin Cynthia Chiang, Aman Chokshi, Jonathan Sievers, Simon Tartakovsky
Instrumentation and Methods for Astrophysics · Cross-listed from astro-ph.EP
Exploring Long-period Architectures: Four New Planet Candidates from Kepler with Periods >342 days
Matthew T. Hansen and Jason A. Dittmann
The Kepler detection pipeline, as well as the transit method, has a bias towards shorter periods, leaving a dearth of detections at longer orbital periods. This relative lack of detections has left an incomplete picture of the architectures of
exoplanet systems within the long-period regime. We have built a single transit detection pipeline, utilizing a classification convolutional neural network and the onboard spacecraft diagnostics of the Kepler spacecraft, to detect long-period
planets. We apply our pipeline to all currently known planetary systems in the Kepler field hosting at least one
planet with an orbital period longer than 6 days. We manually vet all new signals from our pipeline, and identify four new planetary candidates, all of which are in systems where the inner
planets exhibit transit timing variations (TTVs). Two of these candidates, Kepler 1752.02 and Kepler 199.03, cause two transit events that are consistent with periods of $777.78^{+0.01}_{-0.02}$ and $505.495^{+0.004}_{-0.004}$ days, and radii of $3.55^{+0.15}_{-0.15}$ and $2.74^{+0.05}_{-0.05}$ $R_{\oplus}$, respectively. Our remaining two candidates, Kepler 1897.02 and Kepler 1811.02, are single transit candidates with radii $4.81^{+0.20}_{-0.19}$ and $3.25^{+0.28}_{-0.30}$ $R_{\oplus}$, respectively. The shortest orbital periods for these candidates, consistent with the Kepler
dataset (gaps and coverage), are 342 days for Kepler 1897.02 and 544 days for Kepler 1811.02. The new planetary candidates, on their own, are incapable of reproducing the observed TTV signals in the inner system. Although difficult to schedule, follow-up observations are needed to further constrain the new candidates and potentially discover the
planets causing the perturbations.
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Instrumentation and Methods for Astrophysics
AI-Assisted Extraction of Follow-up Observations from GCN Circulars in Astro-COLIBRI
Fabian Schüssler, S. Bisero, M. Cellier et al. (+6 more)
We present a new Astro-COLIBRI component that converts free-text GCN Circulars into structured, event-linked follow-up records and combines them with structured reports submitted directly by the community. A continuously running Circular listener associates new reports with transient events, applies deterministic pre-analysis, and invokes a schema-constrained large
language model extraction step for photometry, contacts,
redshifts, and other reported results and metadata. The resulting records are submitted to the Astro-COLIBRI API, normalized into a common event-level follow-up database, and exposed through the web and mobile interfaces as report summaries, contact tools, optical-afterglow context figures, and downloadable CSV or VOTable products. After tuning, all 1,775 Circulars of an operational evaluation corpus covering the first half of 2026 completed the workflow without failures. An internal human audit of 210 Circulars confirmed 25,827 of 25,880 definite field-level decisions (99.80%), with the remaining errors confined to observation timing and facility attribution in eight of 231 assessed reports. Extracted
redshifts agree with the independent GRBweb compilation for 228 of the 249 events the two share, and every disagreement traces to a limit, a candidate-host estimate, or a value later refined rather than to a misread Circular. The final pipeline was applied to the full GCN archive since 2016, yielding 68,393 individual observations from 26,811 reports across 5,787 transient events, and is now running in real time on new Circulars. The reusable parsing and normalization pipeline is released as the open-source Python package astro-colibri-circular-parser. This paper describes the scientific motivation, architecture, extraction schema, quality-control safeguards, user-facing products, and current use-cases of the system.
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All authors Fabian Schüssler, S. Bisero, M. Cellier, A. Ciric, B. Cornejo, I. Jaroschewski, A. Kaan Alkan, W. Kiendrébéogo, A. Saint-Paul
Instrumentation and Methods for Astrophysics · Cross-listed from astro-ph.SR
Solar chemical composition
A. M. Amarsi and N. Grevesse
The Sun is one of the fundamental
benchmarks in astronomy, and there is ever growing interest in precise and accurate determinations of its chemical composition. We present the current state-of-the-art in spectroscopic determinations of the solar elemental abundances, that now routinely employ three-dimensional (3D) radiative-hydrodynamic simulations of the solar photosphere and post-processing in non-local thermodynamic
equilibrium (non-LTE). We critically review the recent literature and present recommended present-day and protosolar abundances of the 83 long-lived elements, and discuss their implications vis-à-vis primitive meteorites, helioseismology,
neutrino fluxes, and the Solar Modelling Problem.
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Instrumentation and Methods for Astrophysics
Development of an adaptive optics testbed at MPIA for the ELT/Planetary Camera and Spectrograph (PCS)
Raphaël Pourcelot, Thomas Bertram, Peter Bizenberger et al. (+5 more)
The Planetary Camera and Spectrograph (PCS) is a proposed second-generation instrument for the Extremely Large
Telescope (ELT), dedicated to the direct imaging and characterization of
exoplanets. To meet its demanding science requirements, PCS will incorporate an extreme adaptive optics (AO) system, building upon the heritage of existing ELT AO instruments such as ELT/METIS, as well as high-contrast AO systems at the ELT and the VLT, including SPHERE and its upcoming upgrade, SAXO+. PCS development requires extensive research and development to advance critical AO technologies. In this work, we present the Max Planck Institute for Astronomy (MPIA) plan for a modular testbed to validate key components and control strategies. This testbed will integrate two deformable mirrors, including a DM prototype developed by Bertin-ALPAO in collaboration with ESO, with an estimated delivery in 2029. The facility will enable testing of different
Fourier filtering wavefront sensors, including novel mask designs, while exploring different control architectures, such as woofer-tweeter configurations with a single wavefront sensor for both deformable mirrors or fully independent AO stages. Additionally, the testbed will leverage MPIA's expertise in real-time computer development to experiment with advanced control strategies, including predictive control and machine learning-enhanced AO techniques. This contribution presents the current status of PCS development at MPIA, highlighting the ongoing R&D efforts to mature its AO system for high-contrast imaging with the ELT.
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All authors Raphaël Pourcelot, Thomas Bertram, Peter Bizenberger, Markus Feldt, Markus Kasper, Silvia Scheithauer, Stefan Ströbele, Horst Steuer
Instrumentation and Methods for Astrophysics
Effects of near-surface sedimentary structure on Newtonian noise for the Einstein Telescope: a 2-D numerical study
Shi Yao, Patrick Schillings, Johannes Erdmann et al. (+1 more)
Near-surface low-velocity sediments can strongly modify seismic wavefields and therefore affect estimates of Newtonian noise at underground gravitational-wave observatories. We investigate these effects using 2-D viscoelastic simulations of a sediment layer overlying hard-rock basement. Controlled experiments examine the influence of sediment-basement interface geometry, sediment shear-wave velocity, attenuation, and test-mass position. Relative to a homogeneous model, the sediment layer produces frequency-dependent changes through wave trapping, interference, and attenuation. A constant-thickness layer generates a more laterally coherent wavefield and a sharper spectral enhancement than a basin-shaped interface, whereas lateral thickness variations broaden and shift the response. Sediment shear-wave velocity primarily controls the frequencies of the spectral features, while attenuation mainly controls their amplitudes. Newtonian noise is most sensitive to sediment structure for test masses located within or several hundred meters below the sediment layer. The sensitivity to lateral variations in sediment thickness decreases with burial depth and is weak at 2 km depth in the present model. These results demonstrate that near-surface sedimentary structure should be represented explicitly when assessing site-dependent Newtonian noise, particularly for test masses located between 200 m and 300 m depths.
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All authors Shi Yao, Patrick Schillings, Johannes Erdmann, Andreas Rietbrock
Instrumentation and Methods for Astrophysics
When a neural surrogate cannot accelerate a solver: runtime share, closed-loop drift, and the economics of uncertainty gating in a stiff coupled simulation
L. Thümmler and T. Kuroda
Learned surrogates for expensive inner solver blocks are a widely pursued route to faster multiphysics simulation. We report a controlled, end-to-end negative result and identify three structural barriers, none of them a deficiency of the network we trained. The testbed is the implicit Newton solve coupling energy-dependent
neutrino radiation to matter in a general-relativistic radiation-hydrodynamics code, its most expensive physics routine per call. First, per-call cost and share of runtime are different quantities, and only the second bounds acceleration. An exclusive self-time profile puts the target block at 16.9% of critical-rank wall clock, capping any surrogate at ~1.2x by Amdahl's law. A surrogate 5.8x cheaper per call merely ties the solver, and the configuration stable enough to run without fallback reaches only parity. Second, offline accuracy cannot rank surrogates for deployment: across fourteen networks the pooled Spearman error-versus-survival correlation (rho = +0.73) is a between-family confound that vanishes under control (rho = -0.04). Third, a correct out-of-distribution gate cannot accelerate a loop that leaves its training distribution. We give the break-even deferral fraction in closed form: because the visited states sit 73x off the data
manifold, the gate defers 96.8 to 99.7% of
cells, almost invariant to surrogate quality. Including its own cost, the gated loop is a 0.94 to 0.96x slowdown. We further separate stability from fidelity: a never-crashing gated run accumulates a linear -19.9% density bias over 6000 steps. The error is a directed, ballistically accumulating bias, not the variance-driven divergence the autoregressive literature targets.
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Instrumentation and Methods for Astrophysics · Cross-listed from astro-ph.EP
Korean Space Collision Environment Assessment Framework Based on 3D-Cell Model
Jaewoo Kim, Minchan Song, Jinsung Lee et al. (+6 more)
Space situational awareness (SSA) requires purpose-matched models across spatial, temporal, and fidelity scales. Building on our previously reported three-dimensional (3D)
cell formulation and implementation, this study establishes a reproducible, resolution-aware, catalog-conditioned framework for macroscopic assessment of the low Earth orbit (LEO) collision environment. The framework maps supplied catalog or scenario populations to time-averaged spatial density and target-specific impact metrics while retaining individual-object information. Using a 2025 Space-Track snapshot, we evaluate radial, declination, and right-ascension resolution sensitivity and computational performance for six targets, including two Korean space assets. Normalized expected impact counts range from 0.615 to 1.599 and vary nonmonotonically; for a synthetic 500-km circular target, the result at a 0.25-km radial width is 38.5% below the 10-km reference. Runtime and
memory show direction-dependent trade-offs. Ten annual snapshots show catalog growth from 15,723 objects in 2016 to 28,540 in 2025 and a 7.86-fold increase in the 500-km target metric, driven primarily by Starlink, other payloads, and unknown/TBA records. In a conditional stress test of the proposed 998,240-satellite SpaceX Orbital Data Center population, exact annual probabilities of at least one impact reach $3.75\times10^{-3}$ and $1.48\times10^{-3}$ for the 700-km and 1,000-km targets. The framework provides a reproducible, resolution-aware basis for catalog-conditioned environment monitoring, comparative scenario assessment, and prioritization of cases for higher-fidelity follow-up analysis.
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All authors Jaewoo Kim, Minchan Song, Jinsung Lee, Jiwoong Yu, Hosik Kam, Jung Hyun Jo, Eun-Jung Choi, Jin Choi, Jaemyung Ahn
Instrumentation and Methods for Astrophysics · Cross-listed from cs.LG
Neural Operator based Multi-Field Reconstruction of Inner Solar Boundary State
Vignesh Kumar Pandian Sathia, Reza Mansouri, Dustin J. Kempton et al. (+2 more)
The Solar wind is a continuous flow of charged particles emanating from the solar surface and governed by complex, interacting magnetohydrodynamic processes. Accurate specification of inner-boundary conditions is essential for heliospheric modeling and solar-wind prediction. In many practical applications, only a subset of interacting multi-field variables is directly available, but for a comprehensive view of solar wind prediction and downstream magnetohydrodynamic simulations, a more complete boundary state is required. In this work, we study the problem of learning the multi-field multi-scale solar magnetohydrodynamic state at 30 solar radii ($R_\odot$) using operator learning. Specifically, given the radial velocity and radial
magnetic field, we aim to reconstruct the non-radial velocity and
magnetic field components, radial and non-radial current density, thermodynamic density, and pressure components. This mapping is highly nonlinear, spatially coupled, and multi-scale, making it a challenging task for data-driven scientific machine learning. To address this problem, we employ a Local Neural Operator (LocalNO) that learns mappings between input and output function spaces while retaining locality and resolution-awareness. Unlike conventional regression models and autoencoder models, neural operators are better suited for learning structured field-to-field transformations arising from physical systems. The resulting predictions along with inputs are intended to serve as boundary condition variables for future inner-heliospheric modeling pipelines.
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All authors Vignesh Kumar Pandian Sathia, Reza Mansouri, Dustin J. Kempton, Pete Riley, Rafal A. Angryk