Peer Revenue Publish or profit

Papers

Fresh from arXiv, typeset properly. Highlighted words trade on the market.

Physics Astrophysics Instrumentation and Methods for Astrophysics

astro-ph.IM Watch Instrumentation and Methods for Astrophysics

Thursday 1 October

Instrumentation and Methods for Astrophysics

Bayesian Image Reconstruction with Spatially Variant PSFs in X-ray Astronomy

Vincent Eberle, Matteo Guardiani, Margret Westerkamp et al. (+4 more)

X-ray observatories introduce unwanted instrumental effects into photon count data that must be accurately accounted for. In particular, the spatially variant point spread function (PSF) and shot noise pose a non-trivial inverse problem. We apply Bayesian inference grounded in information field theory (IFT) combined with a fast spatially variant PSF representation to infer the posterior mean and uncertainty of the X-ray flux under a minimal set of physically motivated prior assumptions, thereby removing or reducing these instrumental effects. First, ignoring spatial PSF variability, the IFT-based deconvolution is benchmarked on a synthetic example against three variants of the Richardson-Lucy (RL) algorithm using SSIM, RMSE, and NLL as evaluation metrics. Second, the ability of an interpolated patch-based convolution scheme to accurately represent the complex, spatially variant Chandra PSF is assessed on simulated PSF data. Finally, deblurring and spatially variant PSF representation are combined and applied to real Chandra observations of the supernova remnant Cassiopeia A. The IFT-based deconvolution outperforms all tested RL variants on the synthetic benchmark. The patch-based convolution scheme accurately recovers the complex structure of the Chandra PSF. Applied to Cassiopeia A, the reconstructed image appears visually sharper than the exposure-corrected data, and the data residuals are consistent with pure noise, indicating an accurate model of both the instrument and the sky. A comparison with a highly resolved reference dataset confirms this impression. The presented framework enables the removal of spatially variant PSFs from noisy data and opens avenues for future work on cross-calibration between multiple observations or different X-ray instruments, building on the principled uncertainty quantification inherent to the Bayesian framework.
Read the full abstract

All authors Vincent Eberle, Matteo Guardiani, Margret Westerkamp, Philipp Arras, Philipp Frank, Julia Stadler, Torsten Enßlin

Instrumentation and Methods for Astrophysics

Spurious sources in high-resolution VLA surveys

Eric F. Jiménez-Andrade, Emmanuel Momjian, Eric J. Murphy et al. (+2 more)

Blind extragalactic radio continuum surveys are increasingly conducted at sub-arcsecond angular resolutions. However, these surveys contain substantially more spurious sources than expected from purely Gaussian noise statistics. We investigate the origin of these false detections using the 10GHz GOODS-N mosaic obtained with the Karl G. Jansky Very Large Array (VLA), which reaches a native angular resolution of 0.22arcsec and exhibits a nearly Gaussian pixel-noise distribution. We show that part of the increase in the spurious-source fraction at high angular resolution arises naturally from the larger number of independent resolution elements, while residual wide-field imaging effects associated with the primary-beam response make only a modest contribution. Nevertheless, even after accounting for these effects, the observed fraction of false detections remains several times higher than predicted for Gaussian noise. An analysis of the noise autocorrelation function reveals that images with relatively sparse uv coverage retain significant dirty-beam sidelobes that introduce correlated noise fluctuations, substantially increasing the probability of false detections. We conclude that the excess of spurious sources is an intrinsic consequence of incomplete uv sampling rather than the imaging methodology itself, highlighting that future deep radio continuum surveys require not only high angular resolution but also sufficiently dense uv coverage to minimize spurious detections.
Read the full abstract

All authors Eric F. Jiménez-Andrade, Emmanuel Momjian, Eric J. Murphy, James J. Condon, Miguel Vega

Instrumentation and Methods for Astrophysics · Cross-listed from astro-ph.HE

Intrinsic versus observation induced spin nonstationarity in pulsar timing

Reginald Christian Bernardo

Whether spin noise of a neutron star is stationary is, in two-component models, a question of whether stellar angular momentum is conserved internally or exchanged externally through independent stochastic torques on the crust and superfluid. The question is posed in the two components' angular velocities, but radio pulsar timing observes only the crust, through its rotational phase after a deterministic timing model is fitted. We develop a Gaussian process method for testing spin noise stationarity directly with such phase data. Starting from the exact analytical phase means and covariances of a singular, nonstationary and a minimal, stationary two-component model, we show that the crust phase covariances are semiseparable and construct a likelihood that scales linearly with the number of observations, with the timing model marginalized analytically. We analyze simulated phase data from both models using full-state and crust-only observations, and after differentiation into local spin frequencies. Integration, timing model removal and differentiation neither create nor destroy the distinction between stationary and nonstationary spin noise, but determine how much survives in a finite, noisy data set. With both components observed, the models are distinguished decisively and the generating parameters are recovered. With the crust alone, both models describe the observed phase equally well over much of parameter space, and the Bayes factors are of order unity and prior-limited. The minimal model nonetheless ties the hidden superfluid phase to the observed crust phase through a single factor set by the superfluid inertia fraction, so an independent estimate of this fraction, as from glitches, turns it into a falsifiable prediction that a continuous gravitational wave observation of the interior could test.
Read the full abstract

Instrumentation and Methods for Astrophysics · Cross-listed from astro-ph.HE

Stationarity and angular momentum conservation in pulsar spin noise

Reginald Christian Bernardo

We present an analysis of pulsar spin noise based on physically-motivated two-component models of spin wandering. We focus on two models, distinguished by their total angular momentum dynamics: a singular, nonstationary model with a diffusive total angular momentum, and a minimal, stationary model anchored on a conserved total angular momentum. We develop scalable Gaussian process methods and, using mock data, show that the two models are fully testable and distinguishable with full-state observations, i.e., simultaneous independent data on the crust and the superfluid rotational states. This paves a path to testing stationarity in pulsar spin noise, potentially achievable with joint continuous gravitational wave observations and radio timing of pulsars. However, robust inferences and predictions are harder to achieve, and depend on the priors and data quality, when only one component is observationally accessible.
Read the full abstract

Instrumentation and Methods for Astrophysics

Radio Interferometric Calibration with the Exponential Map

L. Cabral, M. Fernandez-Corazza, G. Gancio et al. (+1 more)

The antenna-elements that make up a radio interferometer form a spatial filter that samples components of the Fourier transform of a target radio astronomical source brightness. Along the signal path, there are multiplicative and additive perturbation effects that alter the signal and that should be corrected. The process of mitigating these perturbation effects is called calibration. In this work, we develop a new and fast Maximum Likelihood based estimator of these perturbation effects using the Exponential Map and Lie groups. To evaluate its performance, we compare it to the Cramér-Rao Lower Bound and, to test the estimation time, we compare it with the Expectation Maximization algorithm, another fast Maximum Likelihood estimator. Finally, we apply our estimator to a real observation of the protoplanetary disk AS 209 made with the Submillimeter Array. We found that our proposed estimator meets the Cramér-Rao Lower Bound and it was approximately 40 times faster than the Expectation Maximization algorithm.
Read the full abstract

All authors L. Cabral, M. Fernandez-Corazza, G. Gancio, P. Benaglia

Instrumentation and Methods for Astrophysics

hyprfine: simulating the 21-cm signal from the Dark Ages through to the Epoch of Reionization on a GPU

Harry T. J. Bevins

hyprfine is an analytic simulation of the sky-averaged 21-cm signal from $z=1100 - 6$ written using JAX and Python for native GPU capabilities. It models the average temperature of the 21-cm signal over cosmic time as a function of the $\Lambda$-CDM cosmology parameters and the astrophysics of the first stars and galaxies. As far as we are aware, the code is the first analytic GPU native simulation of the 21-cm signal. It runs in a fraction of a second, parallelises efficiently across a GPU and is differentiable through the Dark Ages ($z \geq 35$). The code is publicly available at https://www.github.com/htjb/hyprfine.
Read the full abstract

Instrumentation and Methods for Astrophysics · Cross-listed from astro-ph.EP

Constraining Particle Stiffness in Asteroid Regolith from Early-Time High-Speed Penetrator Dynamics under Local Granular Variability

Zichen Wang, Yang Yu, Chenyang Huang et al. (+4 more)

Mechanical properties of rubble-pile asteroid regolith remain poorly constrained because contact responses depend on both material properties and local particle configuration. This study examines whether early-time high-speed penetrator dynamics can constrain particle stiffness, represented by the particle Young's modulus E, within a controlled discrete-element model. A bidirectionally coupled EDEM-Adams discrete-element-multibody model is used to screen six parameters: Young's modulus, Poisson's ratio, coefficient of restitution, static friction coefficient, rolling friction coefficient, and adhesion strength. Five Young's-modulus levels are then tested at the same 17 spatial locations in one settled granular bed, giving 85 simulations with location-matched comparisons across modulus levels. Within the 75-90 m s^-1 speed range, varying Young's modulus yields the clearest and most systematic differences in probe deceleration, whereas the other parameters have weaker effects over the tested ranges. Validation by withholding all modulus cases at one location in turn indicates that the early response is more informative for distinguishing broad modulus ranges than adjacent modulus levels. Mesoscale analysis links the response differences to concentrated interface load sharing and spatially extended three-dimensional high-capacity contact paths as Young's modulus increases. The results provide a numerical basis for narrowing the particle Young's-modulus range from high-speed penetration responses despite local granular variability. Quantitative application to real asteroid regolith requires further experimental validation.
Read the full abstract

All authors Zichen Wang, Yang Yu, Chenyang Huang, Zhijun Song, Yutian Wu, Xiaojing Zhang, Xuhui Zhang

Instrumentation and Methods for Astrophysics · Cross-listed from astro-ph.SR

Solarprop 2.0: Modern charge-sign dependent solar modulation for everyone

Henning Gast

I present a greatly improved version of SOLARPROP, an open source framework for building models of charge-sign dependent solar modulation of cosmic rays in the heliosphere. The transport equation of charged particles is solved by converting it to a set of stochastic differential equations (SDEs). The code now includes the full set of SDEs in two spatial dimensions, allowing for magnetic fields with latitudinal components, and it has been generalized to accommodate models in three spatial dimensions. Due to code parallelization and optimization, a speedup of up to three orders of magnitude with respect to the original version is achieved. Python bindings have been added for ease of use. The code has been validated against the results of several model calculations found in the literature. While excellent agreement is found in many cases, important differences are observed for two model implementations. Possible root causes for the differences have been investigated.
Read the full abstract

Instrumentation and Methods for Astrophysics

Comparing Optimized Systematic Error Correction Methods on Selected TESS Light Curves

David Rapetti, Jon Jenkins, Joseph Twicken et al. (+2 more)

The correction of systematic errors in TESS light curves is crucial for all astrophysical analyses employing these observations. Here we present a data analysis and a software package, SysCoCoPy, to investigate and directly compare the performance of the Presearch Data Conditioning (PDC) correcting method from the Science Processing Operations Center (SPOC) pipeline and three correctors developed by the community. We incorporate these three correctors based on their implementations in Lightkurve and are particularly interested in the ability of the four correctors to remove scattered light contamination from the Earth and the Moon, which is a key systematic for TESS. We implemented these correctors in SysCoCoPy with a framework that allows an automatic optimization of their parameters to scale the analysis towards increasingly larger samples. SysCoCoPy provides qualitative and quantitative products for the comparison of individual cases as well as statistical results for selected samples. We currently find that while our automatic parameter optimization provides a significant number of successful scattered-light corrections for two of the correctors with a design that favors this purpose, an statistical analysis of our largest sample indicates that PDC is presently more robust, with a larger overall success level of the metrics used.
Read the full abstract

All authors David Rapetti, Jon Jenkins, Joseph Twicken, Douglas Caldwell, Jeffrey Smith

Instrumentation and Methods for Astrophysics

LeoNet: A Machine Learning Method for Binary Pulsar Classification

Zhaocheng Gong, Jack White, Zeyu Yang et al. (+3 more)

Binary pulsars provide valuable laboratories for testing theories of gravity, but orbital Doppler shifts complicate their detection. Fourier-domain acceleration and jerk searches address this challenge via matched filtering, but at substantial computational cost. We present LeoNet, a convolutional neural network that uses ten learnable filters to extract features of signals affected by Doppler shifts. The resulting ten-channel feature map provides a compact, lower-dimensional alternative to an explicitly sampled acceleration-jerk response grid and is analysed by a convolutional classifier to identify candidate signals. For simulated observations lasting 500 s, LeoNet achieves a mean relative reduction in false negative rate of 55.5% across five sampling intervals compared with the evaluated PRESTO acceleration-search configuration. TensorRT-optimised LeoNet processes each 500 s observation in 3.44-4.37 ms in FP32 on an NVIDIA H100 PCIe GPU across eight sampling intervals, including preprocessing, inference, and postprocessing. At a sampling interval of 128 microseconds, its mean processing time is 3.54 ms, compared with 1.767 s for PRESTO FDAS on an AMD EPYC 9825 CPU with search-frequency limits of 96-1000 Hz, corresponding to an approximately 499-fold speedup in the measured processing time. These results suggest that LeoNet has the potential to improve detection performance, while its millisecond-scale processing time supports its use as a candidate-identification stage in real-time binary pulsar search pipelines.
Read the full abstract

All authors Zhaocheng Gong, Jack White, Zeyu Yang, Jayanta Roy, Karel Adamek, Wesley Armour

Wednesday 30 September

Instrumentation and Methods for Astrophysics

A Missing Latent, Not a Missing Simulator: Radius-Augmented Inference for Real JWST Retrieval

Angshuman Chakravertty and P V V Raj

Amortized simulation-based inference (SBI), which is trained on radiative-transfer simulators, recovers exoplanet atmospheres accurately on synthetic James Webb Space Telescope (JWST) spectra but collapses when it comes to real reduced spectra. The flow posterior collapsed on real WASP-39b (importance-sampling effective sample size (ESS) = 1, best-fit ḩi}2/N = 301), and such a failure is usually blamed on missing the forward-model physics, but ruling these levers out with nested sampling first (a temperature gradient, SO2 opacity, and a high-fidelity opacity set) leaves the fit unchanged, meaning the collapse is not from the simulator misspecification but instead from a missing latent, the planet radius. To fix this, we build MIRAGE, a radius-augmented flow-matching posterior calibrated against an independent nested-sampling reference with importance sampling and an optimal-transport map, which yields a physical and literature-consistent retrieval of real WASP-39b (with ḩi}2/N from 301 to 0.06). This same method transfers unchanged across two instruments and three real JWST targets, including one spectrum self-reduced end-to-end from raw Mikulski Archive for Space Telescopes(MAST) data. The lesson is cross-domain, as a latent the simulator encodes but the inference omits can masquerade as misspecification.
Read the full abstract

Instrumentation and Methods for Astrophysics · Cross-listed from astro-ph.HE

Optimal Pulsar Timing Array Strategies with the Deep Synoptic Array

Tyler Cohen, Paul B. Demorest, Jeremy G. Baier et al. (+50 more)

Pulsar timing array (PTA) experiments have seen evidence for a nanohertz-frequency gravitational wave background (GWB) through coordinated radio timing observations of millisecond pulsars. The significance of this evidence is expected to grow and the GWB's progenitor(s) uncovered with continued observations, especially as the next generation of radio telescopes comes online. We present a framework for simulating timing observations of a set of pulsars with a set of telescopes to predict the per-pulsar optimal instrument, observing frequency, and integration time. We apply this methodology to the NANOGrav source list of 85 pulsars observed with the Green Bank Telescope, Very Large Array, Canadian Hydrogen Intensity Mapping Experiment (CHIME), and the Deep Synoptic Array (DSA), a radio dish array under construction in Nevada. We discuss the dominant noise contributions to the timing precision of these pulsars and show that most are not dominated by intrinsic pulse phase jitter noise. We also determine which pulsars should be timed with DSA and CHIME or only DSA and show that an ab initio DSA PTA is more sensitive than one which uses current instruments. Finally, we solve for the optimal integration time per pulsar, subject to a fixed time budget, that maximizes the GWB signal-to-noise. We show that there is little benefit to optimizing over integration time for these pulsars observed with DSA. Through Monte Carlo realizations of the PTA, we show that the GWB sensitivity improves by $\sim 5\%$ for time-optimized observations compared to the equal-time-per-source scenario with an early-science, fixed source list.
Read the full abstract

All authors Tyler Cohen, Paul B. Demorest, Jeremy G. Baier, Ryan S. Lynch, Scott M. Ransom, Michael T. Lam, Gabriella Agazie, Akash Anumarlapudi, Anne M. Archibald, Zaven Arzoumanian, Paul T. Baker, Ava L. Battaglia, Paul R. Brook, Anabelle L. Brueggemann, H. Thankful Cromartie, Kathryn Crowter, Megan E. DeCesar, Timothy Dolch, Elizabeth C. Ferrara, William Fiore, Emmanuel Fonseca, Gabriel E. Freedman, Nate Garver-Daniels, Peter A. Gentile, Joseph Glaser, Deborah C. Good, Jeffrey S. Hazboun, Ross J. Jennings, Megan L. Jones, David L. Kaplan, Matthew Kerr, Duncan R. Lorimer, Jing Luo, Hugo E. Marquis, Alexander McEwen, Maura A. McLaughlin, Natasha McMann, Bradley W. Meyers, Cherry Ng, David J. Nice, Timothy T. Pennucci, Benetge B. P. Perera, Nihan S. Pol, Henri A. Radovan, Paul S. Ray, Ann Schmiedekamp, Carl Schmiedekamp, Brent J. Shapiro-Albert, Ingrid H. Stairs, Kevin Stovall, Abhimanyu Susobhanan, Joseph K. Swiggum, Haley M. Wahl

Instrumentation and Methods for Astrophysics

Near-Noiseless Single-Photon Detection in the Infrared with a Megapixel Semiconductor Array for Low-Background Astronomy

Guillaume Huber, Pavaman Bilgi, Charles-Antoine Claveau et al. (+7 more)

Sensor noise is a fundamental barrier to low-flux astronomical imaging and spectroscopy, where the ideal sensor would unambiguously distinguish individual photon arrivals. While optical semiconductor arrays can reach single-photon sensitivity, comparable performance in the infrared has largely required superconducting detectors. These must be operated at temperatures near absolute zero and require substantially more power, introducing major system-level challenges. HgCdTe avalanche photodiodes offer a semiconductor alternative by amplifying charge before readout, but dark current and tunneling effects have limited their use in the faintest regimes. Here we demonstrate single-photon-resolving operation in a megapixel-format HgCdTe linear-mode avalanche photodiode array operated at conventional cryogenic temperature. Avalanche gain raises photon-induced steps in non-destructive up-the-ramp data above the readout-noise floor while leaving the dark signal largely unamplified, with a false positive rate of approximately 2 false counts per thousand reads per pixel. These results establish LmAPDs as a promising route toward infrared photon-counting focal planes for future low-background astronomical observatories.
Read the full abstract

All authors Guillaume Huber, Pavaman Bilgi, Charles-Antoine Claveau, Shane Jacobson, Ian Baker, Daniel Owton, Vincent Isgar, Chris Maxey, Markus Loose, Michael Bottom

Instrumentation and Methods for Astrophysics · Cross-listed from astro-ph.CO

ROBIN-PIP: Robust Bayesian Field-Level Inference with Physics-Informed Priors

Ludvig Doeser, Simon Ding, Guilhem Lavaux et al. (+1 more)

Accurately extracting information from galaxy surveys, particularly at non-linear scales, requires increasingly complex and flexible forward models of the observed galaxy distribution. A key component is the galaxy bias relation, which connects the underlying matter distribution to observed galaxies and requires more expressive representations than those currently incorporated into Bayesian field-level inference. Certain parameterized models for this mapping can be overparameterized, with individual parameters carrying no direct physical interpretation, making them challenging to constrain. Our objective is to guide the inference of flexible model parameters towards physically plausible regions of parameter space through physics-informed priors. These priors are derived from high-fidelity simulations, thereby enabling the indirect use of simulations that cannot be integrated directly into gradient-based field-level inference. To this end, we introduce the ROBIN-PIP (RObust Bayesian INference with Physics-Informed Priors) framework. As a proof of concept, we jointly infer the cosmic initial conditions and the parameters of a truncated power-law galaxy bias model for a simulated universe. We integrate ROBIN-PIP into the Bayesian Origin Reconstruction from Galaxies algorithm and benchmark against inference without the simulation-based prior. With the additional prior, posterior standard deviations of the bias model parameters are reduced by up to $20\%$ and autocorrelation lengths are reduced from $730$-$870$ to $440$-$550$, increasing the effective sample sizes. We find no bias in the recovered initial conditions, demonstrating that ROBIN-PIP guides inference towards physically consistent solutions without compromising the data likelihood and highlighting its potential to incorporate overparameterized models in field-level inference.
Read the full abstract

All authors Ludvig Doeser, Simon Ding, Guilhem Lavaux, Jens Jasche

Instrumentation and Methods for Astrophysics

Measuring the linear gain performance for the OrcaQuest CMOS imaging sensor

Zain Azama, Aditya Khandelwal, Ryan Dungee et al. (+3 more)

The OrcaQuest CMOS sensor by Hamamatsu Photonics has been found to exhibit a non-linear gain curve at low exposure times. This effect is characterized through the various exposure tests and data reduction pipelines detailed in this report, and a more accurate gain characterization curve is provided. Such data can be used to provide more consistent measures of pixel gain, linearity, and high framerate images.
Read the full abstract

All authors Zain Azama, Aditya Khandelwal, Ryan Dungee, Ting S. Li, Sarik Jeram, Shaojie Chen

Instrumentation and Methods for Astrophysics · Cross-listed from cs.DL

Best practices in software citation

Phil R. Van-Lane, Floor S. Broekgaarden, Daniel S. Katz et al. (+11 more)

Software is both a foundational tool and a primary output of modern computational research, yet citation practices for software remain inconsistent, incomplete, and rarely machine-actionable. Existing infrastructure designed for paper and data citation does not adequately serve the distinct needs of software citation, leaving a gap that impedes reproducibility, misattributes scholarly credit, and obscures the labor embedded in research pipelines. Drawing on a NASA-funded community workshop held in April 2026, we present an analysis of four interconnected themes: (I)~the cultural barriers to consistent citation practice; (II)~the need for clearer community norms and conventions; (III)~gaps in existing technical infrastructure and workflow; and (IV)~the emerging challenges posed by AI-assisted research. For each theme we identify targeted interventions and assign responsibility across stakeholder groups. We conclude that meaningful progress requires simultaneous action on technical and cultural fronts. Journal editors and publishers represent the single highest-leverage point for accelerating this change, and correct citation must become the path of least resistance within researchers' existing workflows.
Read the full abstract

All authors Phil R. Van-Lane, Floor S. Broekgaarden, Daniel S. Katz, Bhavesh Patel, Pengyin Shan, Jonathan Starr, Samantha Teplitzky, Peter K.G. Williams, Alice Allen, Lucas M. de Sá, Andrew Fullard, Sandra Gesing, Tom Wagg, Andrea Zonca

Instrumentation and Methods for Astrophysics

Prototyping the Next-Generation Spectrograph for Space Exploration: Digital Micromirror Device-based Multi-Object Spectrograph (DMD-MOS) Calibration and Performance Assessment

Jiayi Emma Xu, Sarik Jeram, Shaojie Chen et al. (+7 more)

Abandoning the traditional approaches to multi-object spectrographs, the Digital Micro-mirror Device-based Multi-Object Spectrograph (DMD-MOS) leverages a robust, elegant method for creating multi-object spectroscopy slit masks through use of a DMD, which is an array of microscopic flat mirrors where each micro-mirror can be addressed to physically flip between two discrete angles. We have designed two optical channels for DMD-MOS - one for each of these two positions: 1) an imaging channel that can be used for multi-object target selection and 2) a spectrograph channel. Using this two-channel approach, DMD-MOS can simultaneously image the telescope field of view (FOV) with the imaging channel and actuate the specific DMD micro-mirrors to send light from chosen targets into the spectrograph channel. In this work, we present the results of our laboratory testing of DMD-MOS including spectral resolution, line spread function, wavelength coverage, and the mapping between the DMD and imaging channel planes, the latter of which enables DMD-MOS and our control software to instantaneously configure the DMD to observe chosen target(s) in the acquisition field.
Read the full abstract

All authors Jiayi Emma Xu, Sarik Jeram, Shaojie Chen, Ting S. Li, Ziang Chen, Zain Azam, Adi Khandelwal, Mark Barnet, Yixuan Cheng, Suresh Sivanandam

Instrumentation and Methods for Astrophysics

Automated multi-stressor optical fiber focal ratio degradation and throughput characterization system for future massive multiplex spectroscopic survey

Daksh Singh, Ting S. Li, Sarik Jerma et al. (+1 more)

Future spectroscopic survey facilities such as Spec-S5 and the Wide-Field Spectroscopic Telescope (WST) will contain more than 25,000 optical fibers. Characterizing the focal ratio degradation (FRD) and throughput of these fibers is essential for spectroscopic calibration, sky subtraction, and overall instrument performance, but manual testing of such large fiber populations is prohibitively time-consuming. In this work, we present an automated multi-stressor optical fiber characterization platform capable of measuring both FRD and relative throughput while systematically varying optical and mechanical stress conditions. The system enables characterization as a function of input angle, wavelength, bend radius, twist angle, and externally applied load, while also allowing combinations of stressors to be investigated simultaneously. A custom software framework automates hardware control, data acquisition, and data analysis, enabling large characterization campaigns to be performed with minimal user intervention. Validation measurements performed on a multimode optical fiber demonstrate the ability of the platform to resolve both pronounced and subtle stress-dependent variations in FRD and relative throughput. The results show the expected dependence of FRD on input angle and bend radius, reveal comparatively weak sensitivity to externally applied compressive loading under the investigated conditions, and identify a reproducible periodic dependence on fiber twist angle. In addition, relative throughput measurements indicate that the reduction in relative throughput with increasing input angle cannot be explained solely by a simple projected-area model. The automated workflow and scalability of the system provide a versatile framework for the large-scale characterization of optical fibers required by next-generation spectroscopic survey facilities.
Read the full abstract

All authors Daksh Singh, Ting S. Li, Sarik Jerma, Shaojie Chen

Instrumentation and Methods for Astrophysics

NASA ASTRA White Paper : A Chandra Successor in the 2030s

C. S. Reynolds, E. Hodges-Kluck, E. A. Kara et al. (+19 more)

Chandra established that soft X-ray spectral imaging with approximately 1 arcsec angular resolution is an essential tool for almost every area of astrophysics, from the early Universe to ice chemistry in protoplanetary disks. While Chandra continues to deliver groundbreaking science, it lacks the sensitivity to be the counterpart to JWST, Roman, Rubin, LISA, ngVLA, ALMA, IceCube, NewAthena, ELT, and other upcoming large facilities. Here we highlight the scientific potential of a Chandra successor with >10x higher sensitivity and >50x survey speed. While the scientific reach of such a mission is very broad, we showcase four high-priority science questions that require these capabilities – How did supermassive black holes form?, How does stellar and AGN feedback work in galaxies?, How does high-energy stellar activity affect exoplanet atmospheres? and, How do black holes merge? A Chandra successor in the 2030s would also demonstrate the X-ray mirror technology for a future flagship and sustain the US high energy astrophysics workforce until that flagship launches. We outline the science-instrument trade space by sketching three missions based on the NASA Probe concept study report for the Advanced X-ray Imaging Satellite (AXIS): AXIS-Deep, AXIS-Classic, and AXIS-Fast, highlighting a breakthrough science case for each and listing others that drive the design.
Read the full abstract

All authors C. S. Reynolds, E. Hodges-Kluck, E. A. Kara, M. Koss, K. Madsen, E. Miller, B. Williams, S. Allen, M. Bautz, K.Burdge, N. Cappelluti, B.Cenko, L. Corrales, A.Falcone, A. Foord, D. Haggard, L. Lopez, R. Mushotzky, A. Ptak, H.Russell, S. Safi-Harb, K.Stassun

Instrumentation and Methods for Astrophysics

Inferring radio beams from sparse sampling patterns: a case study with the Murchison Widefield Array

Michael J. Wilensky and Aman Chokshi

Accurate primary-beam models are essential for high-dynamic-range radio experiments, particularly searches for the redshifted 21-cm signal from the Epoch of Reionisation, where beam errors can couple bright foreground emission into the faint cosmological measurement. Using reprocessed satellite observations we infer the 137-MHz, zenith-pointed power beams of Murchison Widefield Array receiving elements. We develop two complementary Bayesian approaches for quantifying departures from nominal electromagnetic simulations: a physically motivated model of complex variations among individual dipoles and a hierarchical spline model for flexible direction-dependent structure. The measurements reveal shallower and displaced nulls together with coherent sidelobe deformations. The dipole model captures much of this broad structure, whereas the spline recovers additional localised features while remaining largely robust to isolated pass-shaped artefacts. Despite their sparse and distinct sampling patterns, four independent 20-day subsets, each containing approximately 20% of the full dataset, recover consistent null and sidelobe morphology across the visible hemisphere. These results demonstrate that opportunistic satellite transmissions can support practical, repeatable beam reconstruction, while motivating future chromatic, phase-referenced measurements of the full Jones response.
Read the full abstract

Instrumentation and Methods for Astrophysics · Cross-listed from astro-ph.HE

XClass: An Automated Multiwavelength Machine-Learning Pipeline for Classification of Extragalactic X-ray Sources. I. Pipeline Description

Blagoy Rangelov, Nicholas Moore, Haley Dutton et al. (+1 more)

Most X-ray sources detected in nearby galaxies by the Chandra X-ray Observatory lack astrophysical classifications. We present XClass (X-ray Classifier for extragalactic sources), an end-to-end machine-learning pipeline that classifies extragalactic X-ray point sources into seven classes: AGN, LMXBs, HMXBs, CVs, low-mass and high-mass foreground stars, and supernova remnants. The key challenge is photometric heterogeneity: training sources are predominantly Galactic with photometry from wide-field surveys (PanSTARRS and 2MASS), while extragalactic targets require Hubble Space Telescope (HST) imaging in a disjoint filter system. We address this through a spectral energy distribution (SED) translation that fits class-appropriate spectral models to each training source and convolves the best-fit model through HST filter curves, producing synthetic magnitudes in a common feature space. The classifier uses an asymmetric two-stage Random Forest: Stage 1 separates broad categories (AGN, X-ray binaries, SNRs, stars) and Stage 2 resolves X-ray binaries into LMXBs and HMXBs using an augmented feature vector that includes Stage 1 probabilities. The training set is assembled from ten Galactic catalogs and extragalactic SNR catalogs, cross-matched with the Chandra Source Catalog v2.1. Features include X-ray hardness ratios, SED-translated HST colors, and X-ray-to-optical flux ratios. We restrict the training set to sources with at least one optical magnitude, avoiding imputation artifacts; the pipeline achieves 99.6% accuracy and balanced accuracy of 0.90 on the resulting 11,374-source optical baseline, with excellent calibration (ECE = 0.002). XClass is modular, generalizable to any HST filter configuration, and will be applied to M31 and M33 in a companion paper.
Read the full abstract

All authors Blagoy Rangelov, Nicholas Moore, Haley Dutton, Erika Marentes

Instrumentation and Methods for Astrophysics · Cross-listed from astro-ph.GA

Water enhances the formation of refractory sulfur in interstellar ices

Carlos del Burgo Olivares, Hector Carrascosa, Guillermo M. Muñoz Caro et al. (+3 more)

Sulfur is the tenth most abundant element in space, yet it is strongly depleted in the gas phase of dense interstellar regions, suggesting sequestration in dust grain components that are difficult to detect. We study whether UV irradiation and thermal processing of H2S-containing interstellar-ice analogues can produce refractory sulfur species, and whether water-dominated ice enhances their formation. We performed UV irradiation and warm-up experiments on H2S-containing ice analogues with and without water, and the resulting refractory residues were analysed ex situ. Refractory sulfur species, including the sulfur allotropes S6-S8, are formed during UV irradiation and thermal processing of H2S-containing interstellar-ice analogues. Water enhances the formation of these sulfur allotropes by two orders of magnitude. We propose a formation mechanism in which ionic intermediates stabilised by the water-ice matrix promote chain growth and cyclisation, yielding predominantly S8. The combined detection in our residues of sulfur allotropes and polythionic species, also reported in meteorites and samples returned from asteroids, supports their formation in pre-cometary ice mantles covering dust grains. Because these species can incorporate multiple sulfur atoms per molecule, they constitute an efficient solid-phase reservoir for the missing sulfur in dense cloud environments where young stars are born. Refractory sulfur can therefore serve as a tracer of water-ice environments in space.
Read the full abstract

All authors Carlos del Burgo Olivares, Hector Carrascosa, Guillermo M. Muñoz Caro, Rafael Lebron-Aguilar, Jose E. Quintanilla-Lopez, Yao-Jen Chen

Instrumentation and Methods for Astrophysics

Two bits about lossy compression: On the limits of compression in cosmology

Hurum Maksora Tohfa and Matthew McQuinn

Astronomy is in an era of enormous sky surveys and of the large simulation suites needed to interpret them, both costly to store and slow to share. Simulation outputs are stored as 32-bit floats, yet numerical noise and astrophysical uncertainties make better than percent-level pixel accuracy unnecessary. Because cosmological fields are statistically homogeneous with nearly Gaussian mode amplitudes, classic rate-distortion results apply directly, and non-Gaussian structure permits further compression. We use the scientific compression package SZ3, and a neural compressor that fixes the quantization and learns the probability of each quantization bin with an autoregressive transformer. SZ3 beats the Gaussian approach only where the field is smooth on the pixel scale or its values pile up at a single point, while the neural approach matches or beats SZ3 on every field we consider and is 9 to 27% below the Gaussian-optimal coder at fixed distortion, with the largest margin where the field is most non-Gaussian. Because the quantizer, not the network, sets the error, a poorly trained model can waste bits but never cost accuracy. A model trained only on weak lensing convergence maps transfers to $N$-body density fields without retraining, suggesting it has learned generic properties of cosmological structure rather than features of one dataset. Eulerian grids need only 1-4 bits per pixel (bpp) at percent-level accuracy, and particle displacements and velocities 5-6 at the precisions they require. The approach carries over to observational data: on Rubin Observatory Data Preview 1 coadds, with the quantization step set to a quarter of the background noise, the fine-tuned transformer needs 4.0 bpp, 27% fewer than the Gaussian coder.
Read the full abstract

Instrumentation and Methods for Astrophysics · Cross-listed from astro-ph.SR

MAGiDiff: Sampling the Photospheric Vector Field from UV/EUV Filtergrams

Ruoyu Wang and David Fouhey

Photospheric vector magnetic fields are foundational to modeling, understanding, and forecasting solar activity. These data are usually produced by inverting and disambiguating the full Stokes vector at multiple passbands, which is demanding. Here, we investigate how well we can estimate photospheric vector magnetograms from UV/EUV filtergrams. This problem is challenging and intrinsically ambiguous without polarization information, as the mapping from UV/EUV intensity to the magnetic field is indirect and ill-posed. We introduce MAGiDiff, a machine-learning-based method that uses denoising diffusion models to estimate vector magnetograms from UV/EUV filtergrams. As input, MAGiDiff takes a stack of filtergrams from the Solar Dynamics Observatory (SDO) / Atmospheric Imaging Assembly (AIA); as output, it is trained to estimate the disambiguated vector magnetogram as seen by Hinode / Solar Optical Telescope-Spectro-Polarimeter (SOT-SP). We show that MAGiDiff can accurately mimic the Hinode ground-truth. Additionally, we probe MAGiDiff's understanding of the physical structure and magnetic connectivity. On full-disk, we show that it produces plausible structures for active regions. MAGiDiff generalizes across solar cycles despite hemispheric polarity reversal, and can be fine-tuned to other EUV instruments including STEREO/EUVI and GOES-R/SUVI. While clearly not a substitute for a dedicated instrument, MAGiDiff opens the door to new capabilities.
Read the full abstract

Instrumentation and Methods for Astrophysics · Cross-listed from astro-ph.EP

Project CERES: Nuclear Thermal Transportation and In-Situ Propellant Production A Foundational Architecture for Solar System Logistics

S.P. Worden and F.G. Kennedy

Project CERES proposes a reusable deep-space transportation architecture centered on the dwarf planet Ceres, combining nuclear thermal propulsion (NTP) with in-situ production of water, hydrogen, oxygen and ammonia. Its first flight, a Pathfinder that surveys Ceres and returns samples, also bears on the question of life: Dawn found the ingredients of prebiotic chemistry there, and returned material would show just how far that chemistry went. We survey every direct Earth-Ceres opportunity from 2032 through 2042 with ephemeris-based Lambert analysis and compare chemical, gravity-assisted, nuclear-electric and nuclear thermal vehicles under one parametric mass model. Impulsive requirements vary from 10.2 to 14.1 km/s across the decade. A 900 s NTP stage delivering 35 t closes near 220 t in low Earth orbit and does so at every surveyed window; chemical delivery is marginal and sensitive to opportunity and staging, and nuclear-electric propulsion requires multi-megawatt power to match one-to-two-year trip times. Refueling at Ceres reduces the launch mass of an Earth-Ceres-Earth sortie by 61%, and the specific impulse of the propellant the node produces decides whether Ceres water becomes cargo or tanker mass. We conclude that Ceres uniquely combines abundant volatiles, exceptionally low gravity and a strategic Main Belt position, and that nuclear thermal propulsion is the only propulsion class that closes mass, time and the return leg together. Together they convert deep-space exploration from independent expeditions into a network operating on a timetable.
Read the full abstract

Instrumentation and Methods for Astrophysics

Toward the Italian Astronomical Community's Contribution to HWO: Science, Technology, and Instrument Concepts

A. Fontana, F. Annibali, N. Antonietti et al. (+101 more)

This paper summarizes the interest of the Italian astronomical community in the possible participation in the development of the Habitable Worlds Observatory (HWO). It is largely based on the outcome of a dedicated meeting held in Rome in July 2025, and it includes further revisions and new suggestions that have arisen in the following months. It includes a succinct description of the main scientific interests and of the key technologies that the Italian community can contribute to the development of the mission. It also includes a short section dedicated to the instrument concepts that we deem important to develop for potential adoption on HWO, and that the Italian community is interested in developing. Finally, it includes a concise presentation of the main instruments for ground-based telescopes (VLT and ELT) and space missions that will be operational before HWO and in which the Italian community has leading positions. They will be invaluable to settle the scientific landscape for the mission and to prepare the Italian scientists to make optimal use of HWO. This paper is primarily aimed at paving the way to discussions and collaborations with worldwide scientists and agencies interested in the development of the mission.
Read the full abstract

All authors A. Fontana, F. Annibali, N. Antonietti, S. Bianchi, L. Biasiotti, K. Biazzo, E. Bisesi, A. S. Bonomo, V. Bozza, R. Briguglio, M. Brogi, D. Busonero, A. Cabras, C. Cecchi Pestellini, G. Cescutti, A. Ciaravella, G. Cresci, A. J. Corso, S. Covino, F. D'Ammando, A. De Rosa, P. D'Incecco, V. D'Odorico, V. D'Orazi, S. Desidera, P. Di Marcantonio, M. P. Di Mauro, N. Elias-Rosa, S. Fineschi, E. Fiorellino, C. Fiorini, D. Fulvio, A. Garufi, G. L. Granato, N. Greco, G. Guilluy, S. Ivanovski, L. Izzo, A. Jimenez-Escobar, N. La Rocca, M. Landoni, C. Lazzoni, E. Liistro, F. Z. Majidi, L. Mancini, A. Mangione, F. Mannucci, M. Marcellino, A. Marconi, M. Maris, D. Massari, A. Melis, D. Mesa, M. Messa, G. Micela, D. Milaković, S. Monai, M. Moresco, E. Munari, G. Murante, L. Napolitano, L. Naponiello, V. Nascimbeni, I. Pagano, M. E. Palumbo, G. Pareschi, P. Pari, F. Pedichini, M. G. Pelizzo, L. Pentericci, C. Pezzotti, F. Piazzese, M. Pilia, L. Pino, G. Piotto, S. Piranomonte, D. Polychroni, R. Ragazzoni, E. Rigliaco, F. Romanato, G. Ruffato, G. Sacco, F. Salvestrini, P. Saracco, G. Scandariato, L. Schreiber, P. Severgnini, L. Silva, P. M. Simonetti, A. Sozzetti, R. Spinelli, L. Testi, A. Tozzi, A. Traficante, M. Tsantaki, R. G. Urso, M. Uslenghi, E. Vanzella, D. Vassallo, C. Vignali, G. Vladilo, A. Vogliardi, A. Zanella, T. Zingales

Instrumentation and Methods for Astrophysics · Cross-listed from gr-qc

MADGRAV: a multilevel anomaly-detection pipeline for gravitational-wave searches applied to LIGO data

Gianluca Inguglia, Huw Haigh, Ulyana Dupletsa et al. (+1 more)

We present the results of MADGRAV, a deep-learning-based search for high-mass compact binary coalescences, applied to the data collected by the LIGO interferometers during the third observing run and during the first and second part of the fourth observing run. The MADGRAV pipeline consists of a series of sequential convolutional neural networks that perform anomaly detection, glitch classification, coherence testing, and signal ranking. Data from the Hanford and Livingston LIGO detectors are studied (both individually and in coherence) by way of 1 second Q-transform windows. Of the candidates that survive every stage of the pipeline, 48 reach the significance threshold, and we report 47 gravitational wave detections characterised by a false alarm rate below $1\,{\rm yr}^{-1}$ with a probability of astrophysical origin $p_{\rm astro}>0.9$. Of the 47 detections, 44 are shared with the minimally modelled coherent WaveBurst search. The observed total source-frame masses, extracted from official gravitational wave transient catalogues, are in the $14-236 M_{\odot}$ range with a median of $69 M_{\odot}$, and a median SNR of 16. We note that the recovered fraction of confident detections rises with mass: for LIGO detectors network SNR $>10$ the pipeline recovers $8.1\%$ of confident catalog events below $30 M_{\odot}$, $39.8\%$ between $30$ and $100 M_{\odot}$, and $53.3\%$ above $100 M_{\odot}$, corresponding to $33.3\%$, $45.5\%$ and $53.3\%$ of the events detected by coherent WaveBurst in the same bins. These results suggest that anomaly detection pipelines can serve as an independent detection channel complementary to matched filtering in the high-mass high-SNR regime.
Read the full abstract

All authors Gianluca Inguglia, Huw Haigh, Ulyana Dupletsa, Alessandro Longo

Instrumentation and Methods for Astrophysics

ArxSP: A Python-Based Modular Application for the Reduction of Digitized Archival Spectra

Ildana Izmailova, Adel Umirbayeva, Manas Khassanov et al. (+2 more)

We present a methodology for the reduction of archival spectral data together with the description of a newly developed Python-based software package featuring an interactive graphical interface. The work is primarily aimed at processing spectra obtained with electron-optical converters (EOCs), which are characterized by geometric distortions induced by the magnetic field of the registration system. Such data are preserved, in particular, in the archive of the Fesenkov Astrophysical Institute (FAI), which contains about 10,000 photographic plates. These distortions, along with the need to transform the optical density of the photographic material into relative intensity, cannot be corrected by standard astronomical packages such as IRAF and therefore require a dedicated approach. Historically, reductions at FAI were performed using a program written in the Microsoft QuickC language for computing platforms of the 1990s, rendering it incompatible with modern operating systems. The new package is implemented with the PyQt5 framework, retaining the logic of the original code while extending its functionality. The implemented algorithms include image rotation and cropping, geometric distortion correction, construction of the characteristic curve linking optical density and intensity, and direct conversion of pixel values in object spectra. The developed software ensures reproducible reduction of archival spectra and provides a cross-platform environment with potential for further extensions.
Read the full abstract

All authors Ildana Izmailova, Adel Umirbayeva, Manas Khassanov, Laura Aktay, Saule Shomshekova

Instrumentation and Methods for Astrophysics

FAIR-Compliant Architecture for Heterogeneous Astronomical Data: KazVO Framework

Ildana Izmailova, Denis Yurin, Yerlan Aimuratov et al. (+17 more)

This paper presents the architecture of the Kazakhstani National Virtual Observatory (KazVO) - an International Virtual Observatory Alliance (IVOA) compliant node that unifies the heterogeneous observational datasets of the Fesenkov Astrophysical Institute (FAI). The architectural foundation of the system is built upon a partitioned Data Lake, a German Astrophysical Virtual Observatory (GAVO) Data Center Helper Suite (DaCHS) publishing backend, and a PostgreSQL database that maps heterogeneous metadata to the unified IVOA ObsCore standard. We follow Open Science by introducing metadata-only model featuring based on a four-class data embargo mechanism. Machine-to-machine services deployed via IVOA protocols are listed in the global Registry of Registers, enabling analysis of Kazakhstani observational assets within external clients such as Tool for OPerations on Catalogues And Tables (TOPCAT), Aladin, and PyVO. As a result, KazVO framework provides a scalable platform driven by a developed end-to-end pipeline that bridges two fundamentally distinct data types - digitized historical glass-plate heritage (1950-1997) and live operational photometric and spectroscopic digital streams from telescopes at the Assy-Turgen and Tien-Shan Observatories - opening FAI's combined data datasets to the global scientific and time-domain astrophysics community.
Read the full abstract

All authors Ildana Izmailova, Denis Yurin, Yerlan Aimuratov, Maxim Makukov, Aleksander Serebryanskiy, Saule Shomshekova, Vitaliy Kim, Chingis Omarov, Adel Umirbayeva, Laura Aktay, Dana Kuvatova, Anton Gluchshenko, Gulnara Suliyeva, Maxim Krugov, Nadezhda Vaidman, Daulet Anarbek, Inna Reva, Gauhar Aimanova, Rashit Valiullin, Raushan Kokumbayeva

Instrumentation and Methods for Astrophysics

The AI crisis for teaching: "Train your own neural network!"

Douglas Scott

Everyone seems to be thinking about so-called artificial intelligence (AI) these days. As scientists, we understand that we're not about to have a conscious computer intelligence taking over the world, but we're nevertheless concerned about how "large language models" (LLMs) and AI "agents" are impacting the way that we do science. This concern has generated several recent essays about the effects of AI on research, including in my own field of astronomy and astrophysics. These opinion pieces tend to miss the biggest current concern for those whose jobs combine teaching with research - although the threat to future scientific research as we know it is probably a bit concerning, the effect on teaching is an existential crisis right now! We need to hang on to the parts of education that we think are at its core - so that students coming out of degree programmes are actually able to think for themselves, "like an astrophysicist". This means emphasising to students that they should use AI tools to support their education and not to replace it. One way of explaining the importance of this principle is to point out that students should be training their own neural networks.
Read the full abstract