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Physics Astrophysics Instrumentation and Methods for Astrophysics

astro-ph.IM Watch Instrumentation and Methods for Astrophysics

Wednesday 23 September

Instrumentation and Methods for Astrophysics

The shell around VY Canis Majoris in SPHEREx is likely an artifact

Viraj Karambelkar, Yashvi Sharma and Kishalay De

Recently, the presence of a large shell around the bright hypergiant star VY Canis Majoris was reported in images from the SPHEREx mission. We report similar shells around other bright stars at similar locations in SPHEREx Band 1 images, suggesting that this shell is not astrophysical but instead a detector artifact.
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Instrumentation and Methods for Astrophysics

AstroGenesis: A Domain-Specific Multi-Agent AI for Astrophysical Research

N. Sahakyan, M. Khachatryan, A. Mahabal et al. (+13 more)

Modern astrophysical research requires the integration of rapidly expanding scientific literature, heterogeneous observational data, and increasingly complex physical models. We introduce AstroGenesis (https://astrogenai.com), a domain-specific multi-agent AI framework that integrates literature retrieval, multiwavelength data access and analysis, theoretical modeling, and research ideation within a unified research environment.The current implementation focuses on blazar research, with specialized agents coordinated by a Supervisor Agent and Planner/Replanner architecture. These agents provide capabilities for retrieving and synthesizing literature, accessing and analyzing multiwavelength observations, performing physical modeling, and identifying research directions. A central component is the Theoretical Modeling Agent, which uses pretrained neural-network surrogate models to enable efficient broadband and multimessenger modeling through natural-language interaction. The framework provides natural-language access to science-ready multiwavelength observational data and retrieval-grounded literature through a multi-stage retrieval and ranking pipeline. The literature-retrieval system was evaluated using two benchmarks: a single-paper benchmark, in which each question targets one publication, and a multi-paper benchmark, in which questions may require evidence from several publications. At least one relevant publication was retrieved among the top five results for 76.6% of the single-paper questions and 79.2% of the multi-paper questions. Representative workflows demonstrate how literature, observational data, physical modeling, and hypothesis generation can be combined within traceable analyses. The framework is extensible to additional astrophysical domains, with the goal of streamlining research workflows and enabling efficient, connected, and reproducible scientific investigations.
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All authors N. Sahakyan, M. Khachatryan, A. Mahabal, D. Pasham, A. Khachatryan, V. Markosyan, N. Unkovskii, V. Vardanyan, D. Bégué, P. Giommi, G. Harutyunyan, D. Israyelyan, A. Tramacere, A. Casotto, Y. Wang, D. Li

Instrumentation and Methods for Astrophysics

Differentiable astrophysics at scale: solving and differentiating ODE ensembles on the GPU

A. Spurio Mancini

Astronomers increasingly fit their models with gradient-based methods, such as Hamiltonian Monte Carlo, which need the derivatives of the model with respect to its parameters. In many analyses a prediction requires solving a small system of ordinary differential equations (ODEs) for thousands to millions of parameter sets, and this ensemble of integrations often sets the cost of the analysis. We introduce to the astronomical community GRADSOLVE, a JAX library that moves this computation to graphics processing units (GPUs): it integrates each member of the ensemble in its own GPU thread with its own step size and returns the derivatives with respect to the parameters in the same pass. We measure the speed-up it provides in three examples from different fields of astronomy, stellar orbits in the Galactic potential, the expansion history of a dark-energy cosmology and the spin precession of binary black holes, with every code held to the same accuracy requirement. For a million trajectories GRADSOLVE runs 8.5 to 1500 times faster than the serial CPU code of each example running on all 128 cores of a CPU, and several orders of magnitude faster than on one core. On the same GPU it is also 11 to 15 times faster than DIFFRAX, the state-of-the-art ODE library in JAX, in all three examples. With GRADSOLVE a million such integrations take seconds or less on one GPU, so gradient-based analyses that need ensembles of this size become routine. The code is publicly available at https://github.com/ECLIPSE-AI4Science/gradsolve.
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Instrumentation and Methods for Astrophysics · Cross-listed from stat.AP

Multivariate Continuous-Time Autoregressive Moving Average Processes for Astronomical Multiband Time Series

Izak Schmidlkofer, Zhirui Hu, Lishan Shi et al. (+2 more)

Large-scale astronomical surveys provide unprecedented volumes of multivariate time-series observations obtained through multiple optical filters. We develop a structured multivariate continuous-time autoregressive moving average (MCARMA) framework for multi-band time series with irregular sampling, heteroscedastic measurement errors, and partially observed bands. The framework allows band-specific stochastic dynamics while modeling cross-band dependence through correlated Brownian driving processes, with state-space and spectral representations enabling likelihood-based inference and interpretation of the fitted stochastic dynamics. We develop a two-stage estimation procedure in which a numerically stabilized preliminary fit initializes subsequent maximum likelihood estimation. Simulations show that higher-order stochastic structure can be recovered when its characteristic features are adequately resolved, but can become weakly identifiable because of limited temporal resolution or near pole–zero cancellation. Joint multivariate estimation improves parameter recovery in 23 of 27 settings and spectral recovery in 26 of 27 settings relative to separate single-band fits. Three Sloan Digital Sky Survey Stripe 82 quasars, respectively favoring MCARMA(1,0), MCARMA(2,0), and MCARMA(2,1), illustrate how joint multiband modeling uses cross-band dependence to inform marginal dynamics and can yield different model-order and spectral inference. The methodology is implemented in the Python package mcarma.
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All authors Izak Schmidlkofer, Zhirui Hu, Lishan Shi, Weixiang Yu, Hyungsuk Tak

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

Neural Network Guided Parameter Space Constraints for Gravitational Wave Searches from Binary Black Holes

Chetan Verma, Amit Reza, Gurudatt Gaur et al. (+2 more)

The detection of gravitational waves (GWs) from compact binary coalescences (CBCs) using matched filtering is computationally demanding because detector data must be correlated with many template waveforms spanning a high-dimensional intrinsic parameter space. In our previous work, we showed that a convolutional neural network (CNN) can classify noisy signals against pure noise and constrain the intrinsic parameter space of a true non-spinning binary black hole (BBH) signal, enabling a narrower matched-filter search region and reducing computational cost. Here, we extend this framework to aligned-spin BBH systems and investigate how different parameter-space representations affect CNN-based patch identification. Using IMRPhenomD waveforms over the aligned-spin BBH parameter space and Advanced LIGO design sensitivity, we show that a CNN trained solely on the aligned-spin template bank achieves more than 99.8% signal-noise classification accuracy on independently generated uniformly sampled BBH signals, indicating that additional uniformly sampled training data are unnecessary. We partition the template bank into four approximately balanced patches using a Principal Component Analysis (PCA)-based quantile scheme and evaluate five parameter-space representations for patch identification. The chirp mass-duration representation achieves the highest average accuracy (93.2%), followed by chirp mass (91.6%) and component masses (90.5%). The post-Newtonian coordinates tau_0-tau_3 and theta_0-theta_3-theta_3s yield substantially lower accuracies. These results show that the existing template bank is sufficient for training and high-accuracy signal detection, while the choice of parameter-space representation is critical for constraining the parameters of the true signal.
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All authors Chetan Verma, Amit Reza, Gurudatt Gaur, Dilip Krishnaswamy, Sarah Caudill

Instrumentation and Methods for Astrophysics

A ready-to-deploy MLOps software platform for satellite and NEO detection at meter-class ground-based observatories

Rafael Carrillo Navarro, Pablo García-Martín, René Duffard et al. (+2 more)

Ground-based astronomical observations frequently contain streaks produced by artificial satellites, space debris, and potentially Near-Earth Objects (NEOs). While machine-learning models can reliably detect these features, their practical adoption in observatory operations is often limited by the lack of integrated tools for visual inspection, validation, workflow management, and structured data storage. This paper presents the StreakMind Workbench, a framework that applies MLOps practices to bridge research-oriented machine-learning pipelines with routine observatory operations. Rather than introducing new detection algorithms, the Workbench addresses a software-engineering challenge in astronomical computing: maintaining a single authoritative source of scientific processing code while providing astronomers with an operational environment for workflow execution and result inspection. Integrated with the reference StreakMind AI model of Carrillo et al. (2026) and implemented in Python using PyQt5, the Workbench supports the complete workflow from FITS ingestion to database storage, including inference, result inspection, database exploration, training management, and Minor Planet Center formatted observations. Validation on 273 images from La Sagra Observatory demonstrates successful end-to-end workflows while maintaining consistency with the underlying StreakMind scientific code. The platform facilitates operational use of a research ML pipeline in meter-class observatories and moderate-scale campaigns, supporting Space Situational Awareness and planetary defence.
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All authors Rafael Carrillo Navarro, Pablo García-Martín, René Duffard, Juan Luis Orellana Montes de Oca, Oscar Ortega Ríos

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

Energy estimation of direct and reflected cosmic-ray events aboard balloon-borne radio detectors

Sergio Cabana-Freire, Jaime Alvarez-Muñiz and Matias Tueros

Balloon-borne radio detectors offer a highly effective approach to monitoring immense volumes of the Earth's atmosphere and ice for ultra-high-energy cosmic rays and neutrinos. Because these payloads measure radio emission from a single, highly elevated vantage point, reconstructing the air-shower energy using traditional methods is not possible. In this work, we present a comprehensive, simulation-based energy reconstruction framework tailored to the unique cosmic-ray event geometries observed by balloon payloads. For the first time, we adapt and apply this methodology to direct atmosphere-skimming cosmic-ray showers, accounting for their highly asymmetric development in rarefied air. Furthermore, we provide a major update to the energy reconstruction of reflected downward-going showers by incorporating spherical reflection and ray de-focusing mechanics directly into simulations. We rigorously validate the method across different event geometries, demonstrating a baseline energy resolution of $8-11\%$. Finally, we quantify the systematic effects of finite detector pointing resolution, unknown primary mass composition, and signal-to-noise ratio, establishing a robust framework for the analysis of cosmic-ray events in next-generation balloon missions such as PUEO and POEMMA-Balloon with Radio.
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Instrumentation and Methods for Astrophysics · Cross-listed from astro-ph.EP

Probing Exoplanetary Chemistry with Ariel: Scientific Priorities and Observational Strategies

Olivia Venot, Yamila Miguel, Robin Baeyens et al. (+30 more)

Over the past two decades, increasingly precise observations have revealed that exoplanet atmospheres are chemically diverse and often far from equilibrium, with processes such as vertical mixing, photochemistry, and atmospheric circulation producing significant departures from thermochemical expectations. As large surveys across a wide range of planets begin to uncover population-level chemical trends, a coherent interpretation of these patterns remains elusive, particularly for gas giants and Neptune-like planets where disequilibrium processes likely dominate. The ESA Ariel mission will provide the first homogeneous, statistically significant atmospheric dataset for nearly a thousand exoplanets, offering an unprecedented opportunity to investigate the origins of chemical diversity across planetary populations. This white paper highlights central scientific questions for understanding atmospheric chemistry and recognized as priorities for the community. These questions span the mechanisms driving disequilibrium chemistry, the role of sulfur- and phosphorus-bearing species, the influence of stellar activity, the formation of haze precursors, and the chemical evolution of atmospheres around stars of different ages and types. Because these topics connect chemistry, physics, and planetary evolution, they form the core focus of the Ariel Chemistry Working Group, which synthesizes current knowledge and identifies the diagnostics best addressed by Ariel's spectral capabilities. For each theme, we outline observational strategies and representative targets already included in the Mission Candidate Sample, illustrating Ariel's ability to address these questions. By linking large-scale observations to predictive atmospheric models, this work supports refinement of Ariel's target selection and enables population-level studies that will transform our understanding of planetary atmospheres.
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All authors Olivia Venot, Yamila Miguel, Robin Baeyens, Stefano Bellotti, Giuseppe Cassone, Quentin Changeat, Ryan Cloutier, Athena Coustenis, Dwaipayan Dubey, Billy Edwards, Kaustubh Hakim, Eric Hébrard, Christiane Helling, Helgi Rafn Hrodmarsson, Leoni Janssen, Adam Yassin Jaziri, Gaia Lacedelli, Panayotis Lavvas, Jorge Lillo-Box, Amy Louca, Adrien Masson, Zita Martins, Karan Molaverdikhani, Benjamín Montesinos, Harrison Nicholls, Enric Palle, Paul Rimmer, Donna Rodgers-Lee, Jonathan Tennyson, Shang-Min Tsai, Roméo Veillet, Sergey N. Yurchenko, Maria Zamyatina

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

Juice-SWI during the Lunar-Earth-Gravity-Assist (LEGA) - Part 2: Instrument operations

T. Cavalié, R. Moreno, L. Rezac et al. (+17 more)

The Jupiter Icy Moons Explorer (Juice) embarked in 2023 on a 8-year interplanetary journey to Jupiter and its icy moons. The Submillimetre Wave Instrument (SWI) is one of the ten science instruments aboard the spacecraft. SWI is a sophisticated and first-of-its-kind payload visiting the outer solar system, featuring dual-band tunable receivers, two independent pointing mechanisms, and spectrometers capable of high resolution (up to a resolving power of 10^{7}). It is designed to support the diverse science objectives of the Juice mission targeting Jupiter's middle atmosphere, icy-moon's exospheres as well as near sub-surface thermophysical properties. For this purpose the Juice mission adopts a complex trajectory tour within the Jovian system, which further necessitates a sophisticated, mission-driven operations concept for SWI. This presents significant planning, operations and commanding challenges which are described in this paper in the context of the Lunar and Earth Gravity Assist (LEGA). After the development and ground calibration of the instrument, the SWI Team has designed a comprehensive calibration strategy applicable during the Cruise Phase of Juice. Among the various opportunities for calibration, including the Near-Earth Commissioning Phase and more than ten Payload Checkout Windows, the LEGA offers the means not only to improve the calibration of the instrument, but also to validate the operational strategy of future icy moon flybys.
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All authors T. Cavalié, R. Moreno, L. Rezac, F. Herpin, C. Jarchow, P. Hartogh, A. Carrasco-Gallardo, S. Goodyear, P. Mancini, A. Schulz-Ravanbakhsh, B. Dabrowski, Y. Kasai, E. Lellouch, A. Murk, D. Murtagh, M. Olberg, M. Rengel, H. Sagawa, S. Szutowicz, E. Wirström

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

Estimating redshift distributions of Fermi blazars with multiple machine-learning models

Kerui Zhu, Jiaming Chen, Yonggang Zheng et al. (+4 more)

Approximately half of the Fermi-LAT-detected blazars lack spectroscopic redshift measurements, which limits population studies and investigations of the cosmological evolution of the gamma-ray blazar population. We aim to develop a multi-model ensemble framework to systematically evaluate the performance of machine-learning models for blazar redshift estimation and to provide predictive redshift probability distributions with quantified uncertainties for all blazars without measured redshifts. We construct an ensemble of 35 machine-learning regression models in the $1/(1+z)$ space. Predictions from individual models are combined to build non-parametric redshift probability density functions (PDFs) for each source, allowing an explicit characterization of predictive uncertainty. The source-level PDFs are subsequently stacked to derive population-level redshift distributions. Across the diverse models within our framework, we find comparable predictive performance, indicating a feature-limited performance plateau. We compile a catalog of predictive redshift distributions for 1,590 blazars without measured redshifts. The stacked PDFs recover the characteristic double-peaked redshift structure of blazars without using source class labels. For blazar candidates of uncertain type, the predicted redshift distribution exhibits a bimodal structure, suggesting a slightly larger contribution from FSRQ-like sources. Our multi-model statistical framework achieves competitive predictive performance while retaining faint sources. We provide predicted redshift distributions with quantified uncertainties for 1,590 blazars without measured redshifts, offering improved support for population and evolutionary studies. More broadly, this approach highlights the practical value of uncertainty-aware multi-model regression ensembling in astronomical data analysis.
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All authors Kerui Zhu, Jiaming Chen, Yonggang Zheng, Ruixin Zhou, Shan Chang, Li Zhang, Niansheng Tang

Tuesday 22 September

Instrumentation and Methods for Astrophysics

South Pole-Aitken basin sample Return and eXploration (SPARX) Science Definition Team Report

Lauren M. Jozwiak, James T. Keane, Robert Citron et al. (+18 more)

Sample return from South Pole-Aitken Basin (SPA) on the Moon has been recognized as the highest priority lunar science mission for well over two decades, appearing as a recommended New Frontiers class mission in the two previous Planetary Science Decadal Survey documents (2003-2012 New Frontiers, 2013-2022 Visions and Voyages), and as a prioritized mission for the Lunar Discovery and Exploration Program (LDEP) in the 2023-2032 Origins, Worlds, and Life Decadal Survey. The South Pole-Aitken basin presents the ideal target destination to test nearly 60 years of lunar science hypotheses owing to a unique combination of its size, antiquity, and location on the lunar farside. Convened by NASA in response to OWL Decadal Survey recommendations, the SPARX report provides analysis on prioritized science objectives, measurement requirements, implementation architectures, and a design reference mission for a South Pole-Aitken basin sample return mission. The mission profiles presented in this report leverage key advancements in remote surface exploration technology, the U.S. commercial space industry, and the programmatic focus on sustained human presence and exploration at the lunar south polar region. The science objectives and measurements presented in this report outline a mission that would transform lunar science, would address multiple high-level Decadal science questions at the only place in the Solar System where they can be credibly addressed, would pioneer a new paradigm for planetary surface exploration in the 21st century, would leverage the burgeoning space economy to pursue ground-breaking science on a more cost-efficient basis, and would unite robotic exploration with human exploration in a model for a future sustained lunar enterprise.
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All authors Lauren M. Jozwiak, James T. Keane, Robert Citron, Natalie Curran, Steve Elardo, Caleb Fassett, Abigail Fraeman, Saira Hamid, Lena Heffern, Peter James, Daniel Moriarty, Kevin Righter, Jennifer Whitten, Hunter Vannier, Zoe Wilbur, Tomokatsu Morota, Hiroshi Nagaoka, Caroline-Emmanuelle Morisset, Patrick Hill, James Carpenter, Francesca MacDonald

Instrumentation and Methods for Astrophysics

Increasing Sensitivity to Trailed Solar System Objects in Archival JWST NIRCam Imaging with Group Differencing

Anthony Girmenia and Stanimir Metchev

The JWST archive contains thousands of NIRCam exposures distributed across the sky. Small solar system bodies (SSSBs) are present in many of these fields, but are often trailed and more difficult to detect. The standard pipeline produces a single image by fitting a slope to a sequence of $N$ cumulative “up-the-ramp" groups of the detector. Thus, each JWST image comprises a time series of cumulative reads. We demonstrate that transforming the ramp into a series of group difference images reduces the per-image trail length of moving sources, producing an $(N-1)$-group photometrically calibrated time series from an $N$-group integration. This enables higher-cadence trajectory-based searches using as few as a single JWST exposure. A generalized least-squares fit of the group difference images along the trajectory of a moving source recovers the source flux at higher SNR when compared to the ramp fit, with the improvement scaling with the reduction in trailing. We validate our procedure on 13 moving objects detected in archival JWST NIRCam images. To estimate our sensitivity increase, we perform an injection/recovery simulation on the JWST imagery. Trailed moving objects recovered via group differencing show higher SNRs, improving completeness to moving sources compared to standard calibrations. For typical integration lengths of $N=4$ or $6$ for archival JWST/NIRCam exposures in the ecliptic, group differencing yields sensitivity gains between $\sim 1.1$–$1.4$ mag, based on simulation results. This method significantly increases the sensitivity of archival JWST surveys to trailed SSSBs.
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Instrumentation and Methods for Astrophysics · Cross-listed from physics.ed-ph

A Simple Miniaturized Stellar Interferometer: Measuring the Angular Diameter of Venus with a Masked Telescope

Eric J. Friedman and Michelle Goman

We describe a simple, visually compelling demonstration/laboratory of astronomical interferometry using a standard planetary telescope and a thin piece of opaque plastic punched with two small holes. By observing the disappearance of interference fringes as the hole spacing is increased, students can directly estimate the angular diameter of Venus. The demonstration is a tabletop analogue of the landmark 1921 Michelson-Pease measurement of Betelgeuse and scales naturally from that measurement: where Michelson and Pease required a 6.1 m baseline to resolve a disk 47 mas (milli-arcsecond) across, a 15 mm baseline suffices for Venus, when it is approximately 10-15$''$ (arc seconds). The lab works reliably in ordinary atmospheric seeing conditions, and a side-by-side comparison of Venus (or Jupiter) with a bright star such as Sirius provides an immediate visual demonstration of the difference between extended and point sources. It works equally well as a laboratory at a variety of difficulty levels. Furthermore, it does not require advanced knowledge or skills with optical telescopes. While the underlying ideas are well known, we are unaware of any source that presents them in an accessible, unified form.
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Instrumentation and Methods for Astrophysics

Laboratory Evidence that Methanol-Rich Ice Mantles Lower Methyl Formate Binding Energies

Rachel E. Gross, Jeroen Terwisscha van Scheltinga, L. Ilsedore Cleeves et al. (+5 more)

Complex organic molecules are widely detected in star-forming regions and are important precursors to prebiotic species. Because these molecules are thought to form primarily within icy grain mantles, their desorption is assumed to be regulated by the sublimation of the surrounding H$_2$O ice matrix. However, observations increasingly reveal gas-phase abundances of methyl formate (MF) at temperatures well below the sublimation temperature of water ice (T ~ 100K), challenging this view. To investigate how ice composition and phase influence MF desorption, we present laboratory temperature-programmed desorption measurements of MF from four astrophysically relevant substrates: amorphous and crystalline H$_2$O and CH$_3$OH. Desorption kinetics were analyzed using pre-exponential factors derived from transition-state theory with leading-edge analysis and non-negative least-squares inversion to recover binding-energy distributions. We find that MF binds more weakly to CH$_3$OH ice than to H$_2$O-rich substrates, with representative binding energies of ~6247K for amorphous H$_2$O, ~6213K for crystalline H$_2$O, and ~5469K for amorphous CH$_3$OH. MF deposited on crystalline CH$_3$OH exhibits coverage-independent leading edges consistent with multilayer or island desorption, yielding an effective binding energy of 5333K. Enhanced surface mobility and reduced trapping efficiency on methanol ice, combined with weaker MF binding, provide a natural explanation for the early appearance of gas-phase MF observed in star-forming environments, with characteristic desorption temperatures of ~85K on CH$_3$OH and ~95-100K on H$_2$O. Together, these effects support a two-step thermal desorption pathway in which MF associated with CH$_3$OH-rich ice can be released prior to bulk H$_2$O sublimation.
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All authors Rachel E. Gross, Jeroen Terwisscha van Scheltinga, L. Ilsedore Cleeves, Catherine A. Dukes, Robin T. Garrod, Alexia Simon, Shane Sawyer, Adam Woodson

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

From starlight to dark matter: a stochastic interpolation approach to map dark matter from stellar density

Xiaowei Ou, Lina Necib, Carolina Cuesta-Lazaro et al. (+9 more)

The dark matter halo profile in galaxies holds key information about the nature of dark matter and galaxy formation. Constraining the dark matter profile of galaxies beyond the Milky Way traditionally requires expensive spectroscopic observations for kinematic information. In this paper, we explore a conditional generative model framework to map the dark matter profile of Milky Way-mass galaxies from stellar density profiles, more easily obtainable through large photometric imaging surveys. As a proof of concept, we train the model to learn a stochastic bridge between instrument systematics-free baryonic stellar distributions and underlying dark matter density maps from the DREAMS hydrodynamics simulation suite. We recover 2D dark matter density profiles with a typical accuracy of $\sim0.1$ dex ($\sim1.5$% of the truth in log scale). The stochastic sampling procedure provides uncertainty estimates of the predicted dark matter map, with typical values $\sim0.1$ dex. Out-of-domain tests with Milky Way-mass galaxies from IllustrisTNG and FIRE simulations show that, while the model can qualitatively be generalized to TNG50 galaxies from IllustrisTNG, the model is sensitive to the galaxy formation model, with $\sim0.2$-$0.4$ dex over-prediction for the inner profiles ($r\lesssim5$ kpc) of the FIRE test galaxies. Future work will explore training with additional suites of simulations and/or conditioning on additional information, such as multi-band images. Our results are a first step towards using generative models as a flexible, uncertainty-aware framework for turning forthcoming data from large imaging surveys into spatially resolved dark matter maps.
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All authors Xiaowei Ou, Lina Necib, Carolina Cuesta-Lazaro, Paul Torrey, Niusha Ahvazi, Alyson M. Brooks, Berthy T. Feng, Alex M. Garcia, Jiaxuan Li, Jonah C. Rose, Xuejian Shen, Mark Vogelsberger

Instrumentation and Methods for Astrophysics

The Simons Observatory: Commissioning of the Mid-Frequency Small Aperture Telescopes

Kathleen Harrington, Remington Gerras, Nicholas Galitzki et al. (+88 more)

The Simons Observatory (SO) is a cosmic microwave background (CMB) survey experiment observing from an altitude of 5,200 m in the Atacama Desert in Chile. The SO Small Aperture Telescopes (SATs) will cover six spectral bands between 30 and 280 GHz primarily to search for evidence of primordial gravitational waves in the large angular scale $B$-modes of the CMB polarization. The first two Mid-Frequency (MF) SATs, operating in the 90 and 150 GHz frequency bands, began commissioning observations in late 2023, and we present the results of those observations here. Each MF SAT operates with approximately 9,400 biased transition-edge sensors (TES) at 1 mm of precipitable water vapor (PWV), representing a typical 80% yield. Planet observations confirm the telescope optics performance, calibrate the instrument response, and measure the end-to-end optical efficiency of the SATs. The realized instrument efficiencies are within the range expected based on instrument model predictions. In nominal observing conditions, the noise equivalent temperatures from both telescopes combined are $2.4~\mathrm{\mu K_{CMB}\sqrt{s}}$ at 90 GHz and $3.2~\mathrm{\mu K_{CMB}\sqrt{s}}$ at 150 GHz, substantially outperforming the baseline instantaneous sensitivity targets for the observatory.
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All authors Kathleen Harrington, Remington Gerras, Nicholas Galitzki, Samuel Day-Weiss, Yoshinori Sueno, Thomas Alford, Michael J. Randall, Kyohei Yamada, Maximiliano Silva-Feaver, Kevin Crowley, Shunsuke Adachi, Alexandre E. Adler, Prakamya Agrawal, Simone Aiola, David Alonso, Kam Arnold, Susanna Azzoni, Andrew Bazarko, Sanah Bhimani, Simon Biquard, Bryce Bixler, Josh Borrow, Michael L. Brown, Erminia Calabrese, Yuji Chinone, Steve K. Choi, Nadia Dachlythra, Mark Devlin, Simon Dicker, Peter N. Dow, Shannon M. Duff, Jo Dunkley, Rolando Dunner, Daniel Dutcher, Hamza El Bouhargani, Josquin Errard, Allen Foster, Ken Ganga, John Groh, Yilun Guan, Jon E. Gudmundsson, Saianeesh Haridas, Masaya Hasegawa, Matthew Hasselfield, Shawn W. Henderson, Carlos Hervías-Caimapo, Adam D. Hincks, Renée Hložek, Johannes Hubmayr, Bradley R. Johnson, Yutaro Kasai, Brian Keating, Ben Keller, Theodore Kisner, Brian J. Koopman, Akito Kusaka, Adrian Lee, JB Lloyd, Anto Lonappan, Marius Lungu, Aashrita Mangu, Michael McCrackan, Jeff McMahon, Jenna Moore, Thomas W. Morris, Hironobu Nakata, Federico Nati, Laura Newburgh, David V. Nguyen, John Orlowski-Scherer, Lyman Page, Ioannis Paraskevakos, Tristan Pinsonneault-Marotte, Erik Rosenberg, Yuki Sakurai, Goureesankar Sathyanathan, Thomas P. Satterthwaite, Yudai Seino, Elle Shaw, Sara M. Simon, Suzanne T. Staggs, Junna Sugiyama, Junya Suzuki, Satoru Takakura, Osamu Tajima, Alex Thomas, Daniel B. Thomas, Tran Tsan, Eve Vavagiakis, Yuhan Wang, John Wilson

Instrumentation and Methods for Astrophysics

Interference Meets Inference: Bayesian Time-Series Modeling of Radio-Frequency Interference

Jade M. Ducharme, Andrei Li, Michael J. Wilensky et al. (+1 more)

Radio-frequency interference (RFI) remains a major challenge for modern radio astronomy experiments. In this work, we cast RFI detection as a one-dimensional time-series anomaly-detection problem and develop a probabilistic mixture-model framework for separating a smoothly varying astronomical background from anomalous contamination. The model jointly describes the clean and contaminated components and assigns each time sample a posterior probability of belonging to the RFI state, rather than relying solely on binary flags. This probabilistic formulation provides a measure of classification confidence, enables uncertainty propagation into derived downstream statistics, and offers additional information for investigating ambiguous events. We apply the framework to observations from the Murchison Widefield Array collected in 2014, producing "soft" classification labels and seasonal RFI trends. We perform a parallel analysis using the Sky-Subtracted Incoherent Noise Spectrum software pipeline (SSINS), which produces "hard" classification labels. Overall, the mixture model provides similar and in some cases superior classification results while providing a complementary probabilistic description of RFI contamination and its uncertainty.
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All authors Jade M. Ducharme, Andrei Li, Michael J. Wilensky, Adrian Liu

Instrumentation and Methods for Astrophysics

EPIMETHEUS: Effective PSF inferred from mosaics

Pietro Benotto, Benedetta Vulcani and Vittoria Altomonte

Accurate knowledge of the point spread function (PSF) is essential for a wide range of astronomical applications, from astrometry to precision photometry. However, deriving a robust average effective PSF (ePSF) from astronomical mosaics is particularly challenging due to the superposition of exposures taken at different epochs, under different observing conditions, and with varying position angles (PAs). To address this issue, we present EPIMETHEUS, an open-source Python library designed to manage the entire ePSF construction workflow, from initial star selection to final model generation. Built with a modular architecture, EPIMETHEUS avoids black-box methodologies, ensuring full transparency and giving the user complete control over the stellar sample to use for the ePSF building to meet specific scientific requirements. The software can process multiple images or mosaics simultaneously to retrieve the ePSF, a crucial feature when individual fields lack a sufficient number of bright, unsaturated stars. Candidate stars can be automatically filtered based on orientation and light concentration, with options for manually excluding stars through a semi-automatic visual inspection. Although EPIMETHEUS is specifically optimised to handle the roto-translation complexities inherent to multi-epoch mosaics, it is versatile and can be equally applied to derive ePSFs from simpler, single-orientation images where rotational corrections are not required. Finally, to demonstrate the software's capabilities and provide an immediate resource, we release empirical ePSFs for standard HST and JWST filters across the COSMOS, UDS, Abell 2744, and GOODS-S fields.
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Instrumentation and Methods for Astrophysics · Cross-listed from astro-ph.CO

Data-driven Galaxy Population Prior for Photometric Redshifts

Nikolas Frediani, Daniel Gruen, Luca Tortorelli et al. (+1 more)

Stage-IV cosmological surveys rely on well-characterised photometric redshift distributions for constraining cosmological models, yet the projected weak lensing requirements are about an order of magnitude more accurate than current state-of-the-art methods. Any photometric redshift inference depends - explicitly or implicitly - on a prior over galaxy SEDs, luminosities, and redshifts. In this paper, we develop a purely data-driven approach for modelling the galaxy population prior based on learning the distribution of galaxy SEDs and evaluate its induced systematic uncertainties for photometric redshift calibration. Under controlled conditions, we build a template-free model for galaxy SEDs with minimal physical assumptions and assess how well it can reproduce the colour-redshift relation. We train a generative model on a realistic population of noisy mock galaxy spectra, simulated using the GalSBI-SPS galaxy population model, to learn the joint distribution of intrinsic spectra, luminosities, and redshifts. We employ a probabilistic autoencoder that compresses spectra into a low-dimensional latent space and performs neural density estimation with a normalising flow. The autoencoder can reconstruct the shape of galaxy spectra with Gaussian noise of standard deviation $\sigma$ to $\sim0.1\,\sigma$ of the ground truth, thereby building a model for noiseless SEDs from noisy spectra only. We construct colour-selected tomographic bins with a self-organising map and compare the predicted mean redshift. We find that deviations in each bin are smaller than the per-mille Stage-IV requirements, with $| \Delta \langle z \rangle |\lesssim 0.0007 (1+z)$. This work serves as a proof-of-concept that generative models can learn the prior of galaxy observations accurately enough for upcoming surveys under controlled conditions.
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All authors Nikolas Frediani, Daniel Gruen, Luca Tortorelli, Jamie McCullough

Instrumentation and Methods for Astrophysics

The second Little UV Camera (LUVCam2) mission

Emily Hu, Suresh Sivanandam, Albert Lau et al. (+12 more)

The second Little UltraViolet Camera (LUVCam2) is a 80mm-diameter dual UV-optical space telescope that will observe the UV flares around low-mass stars, with planned flight as a $\sim$1.4U payload aboard the 3U CubeSat BRNOsat in late 2027. We present the concept of operations, full system architecture, optomechanical, and electrical design for LUVCam2. LUVCam2 is the second technology demonstration for the LUVCam electronics board, with the first LUVCam (LUVCam1) still operating in low-Earth orbit (launched July 2024). The main upgrade of LUVCam2 is the use of the backside-illuminated GSENSE4040BSI CMOS sensor, promising significant improvement to UV quantum efficiency over the frontside-illuminated model used in LUVCam1. Other upgrades to LUVCam2 include the incorporation of the LUVCam on-board computer onto the readout electronics, an integrated 1W heater and thermal control system, and novel operational capability for LUVCam2 to perform on-sensor star-tracking using optical images of background stars.
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All authors Emily Hu, Suresh Sivanandam, Albert Lau, Coby Silayan, Mark Barnet, Shaojie Chen, Gavin Hay, Nadim Hatoum, Aaron Tohuvavohu, Norbert Werner, Marianna Dafčíková, Filip Münz, Patrick Nkwari, Jakub Řípa, Andras Pal

Instrumentation and Methods for Astrophysics

Testing deep learning techniques for event reconstruction in pair-production gamma-ray telescopes

Mattia Maniscalco, Valentina Fioretti, Nicolò Parmiggiani et al. (+7 more)

In the development of next-generation tracking detectors for gamma-ray space observations, the integration of advanced deep learning techniques into event reconstruction algorithms is a promising approach for improving the performance of the mission, reducing systematic uncertainties and maximizing the scientific output. In this study, we investigate the application of deep learning techniques such as Graph Neural Networks (GNNs) for the identification and reconstruction of particle tracks resulting from pair production events within the tracker of the AMEGO-X proposed concept. The goal is improving the angular resolution and the detection efficiency of the telescope, especially in the soft energy range (below 100 MeV) where multiple Coulomb scattering significantly degrades track reconstruction and consequently limits the sensitivity in the so-called MeV gap. We use the Geant4-based MEGAlib framework and a Python-based dedicated read-out and data handler processor to format the input datasets as close as possible to the real data. The simulated datasets are then used to train and evaluate two graph neural network architectures, GraphSAGE and Interaction Networks, and to compare their performance in pair-production event reconstruction in terms of Point Spread Function 68% containment radius and effective area, also with that obtained using standard reconstruction techniques. The results of this study indicate that graph neural network-based reconstruction is a promising approach for pair-production event reconstruction below 100 MeV when compared with standard reconstruction methods, with significant potential for further optimization.
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All authors Mattia Maniscalco, Valentina Fioretti, Nicolò Parmiggiani, Andrea Bulgarelli, Carolyn A. Kierans, Adrien Laviron, Gabriele Panebianco, Alessio Aboudan, Luca Castaldini, Andreas Zoglauer

Instrumentation and Methods for Astrophysics

Options for Compression of radio interferometry data: lossy compression of visibilities and lossless compression of uv-visibility grids for the MHONGOOSE survey

Richard Dodson, Jonghwan Rhee, W.J.G. de Blok et al. (+6 more)

Next generation radio astronomy telescopes are challenging existing data reduction paradigms. With ever more antennas, larger bandwidths, and sometimes multiple primary beams, they often generate more observed data products than can readily be stored long-term. Thus, data storage becomes a major cost driver and processing constraint. In this paper, we test two methods of addressing this problem: grid-stacking, a two-stage lossless compression solution; and the lossy compression of the raw visibilities before traditional processing. To demonstrate these solutions we utilised a deep imaging pipeline based on software for the ASKAP telescope, ASKAPSoft, but applied to a strong source (NGC1566) from the deep MeerKAT HI spectral line project, MHONGOOSE. The grid-stacking solution reproduces the spectrum from traditional processing to within better than 0.7%, and also allows for the reconstruction of other weighting scales without significant computing costs. In comparison, image-stacking also reproduces the spectrum from the traditional processing, to within better than 3% but with worse image residuals in the cube. The lossy compression, even at a near ten-fold reduction in file size, reproduces the spectra almost perfectly (to better than ~0.01% in all cases). Thus both compression methods are promising solutions, and we discuss considerations for their application.
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All authors Richard Dodson, Jonghwan Rhee, W.J.G. de Blok, Martin Meyer, Alexander Williamson, Daniel Mitchell, Kristóf Rozgonyi, María J. Rioja, Pascal J. Elahi

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

A Multi-Mission Detection Framework for High-Energy Transients: Application to a 13-year Fermi/GBM and Swift/BAT Sample

Ariel Perera, Barak Zackay and Tejaswi Venumadhav

The detection of faint high-energy transients is becoming increasingly important in time-domain and multi-messenger astronomy, particularly for identifying electromagnetic counterparts to binary neutron star mergers, high-energy neutrino events, and other weak transient phenomena. At present, the sensitivity of gamma-ray and hard X-ray searches is often limited by the detection threshold of individual instruments, leaving a population of faint transients inaccessible to single-mission analyses. To address this limitation, we develop a joint detection framework that combines trigger information from multiple detectors and assigns each candidate a joint association probability, $p_{\text{joint}}$, as a function of the individual detector signal-to-noise ratios. We apply this framework to archival Fermi/GBM and Swift/BAT rates data from 2013-2025. The method effectively separates coincident signals from accidental background associations, suppressing inconsistent coincidences while enhancing the significance of faint events observed by both instruments. We find that, under optimal conditions, a two-mission search can increase the accessible volume for short gamma-ray bursts by up to $60\%$. This framework provides a practical foundation for generalized multi-mission searches, enabling more sensitive exploration of the faint high-energy transient sky and improving the prospects for future multi-messenger discoveries.
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Instrumentation and Methods for Astrophysics

Fitting Moving Objects in Up-The-Ramp Data with Applications to the Roman Space Telescope and JWST

Timothy D. Brandt

A moving object breaks the fundamental property of constant per-pixel count rates in an astronomical image read out up-the-ramp. In this paper, we show how to fit a moving object's path across a detector as that detector is read out nondestructively. We write the full likelihood function for every pixel subject to a constant count rate plus a time-dependent count rate due to a moving source. Assuming the moving source to be point-like and assuming the effective point-spread function to be known, we are left with four parameters that enter the likelihood nonlinearly: two for position and two for velocity. All remaining parameters can be optimized using closed-form expressions. Our approach extracts maximal information on a moving source's position and speed and enables the source to be accurately removed from the image. We investigate the dependence of flux, position, and velocity precision on the target's speed and the readout pattern. We also find a small, positive bias on the recovered flux due to the need to fit for an uncertain position and speed. Our approach can be used for space-based images with minor Solar system bodies in the foreground, e.g.~from Roman and JWST, or for ground-based observations with satellites in the foreground. We demonstrate the promise of our method with a fit to an asteroid track observed serendipitously by the NIRISS instrument on JWST, comparing it to the performance of the JWST pipeline. Python code implementing our approach is available at https://github.com/t-brandt/moving_source. The total computational cost to fit the track of a moving object is $\sim$1 second on a 2023 Macbook Pro.
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Instrumentation and Methods for Astrophysics · Cross-listed from astro-ph.CO

HierArchical Wavelet Coefficients on the Sphere (HAWCS): the Scattering Transform on Spherical Maps

Arefe Abghari, Lukas T. Hergt, Douglas Scott et al. (+1 more)

Extracting Gaussian information from data is well understood, but characterizing non-Gaussianity is challenging. We introduce HAWCS, a healpy-based Python package for efficiently computing wavelet scattering transform coefficients from full-sky maps. These coefficients provide compact summary statistics that are sensitive to higher-order structure and interactions across angular scales. We test the implementation on controlled Gaussian and non-Gaussian fields and demonstrate its computational efficiency. We then apply HAWCS to thermal Sunyaev-Zeldovich maps derived from Planck observations and four simulations, quantifying and comparing their higher-order statistical properties. We also use the method to expose the signatures of gravitational lensing in cosmic microwave background (CMB) temperature maps. These results demonstrate that HAWCS is a practical tool for validating component-separation pipelines and cosmological simulations, and a promising summary statistic for characterizing non-Gaussian structures beyond the power spectrum. These coefficients could also be used to constrain or generate simulated maps that reproduce the same statistical features of real data.
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All authors Arefe Abghari, Lukas T. Hergt, Douglas Scott, Raelyn M. Sullivan

Instrumentation and Methods for Astrophysics · Cross-listed from math.DS

Identifiable Uniqueness of Impulsively Forced, Inert, and Stabilized Body Trajectories

Bo Pieter Johannes Andrée

An unusual object in the sky raises two related questions: what kind of object it is and what physical process drives its motion. Departures from a gravity-only trajectory can provide evidence, but the same apparent acceleration may be generated by distinct dynamical mechanisms. We formulate this as an identification problem and establish conditions for identifiable uniqueness among three reference classes: inert dynamics, impulsive forcing, and active stabilization such as a controlled spacecraft. The distinction rests on how each class responds to disturbances. Although different mechanisms can produce similar average trajectories, the specified reference models imply different stochastic responses around those trajectories. We show when these differences uniquely determine the dynamical class from the observed trajectory and when several classes remain observationally compatible.
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Instrumentation and Methods for Astrophysics · Cross-listed from astro-ph.EP

Possible Observational Survey Strategies to Maximize Exoplanet Yields: A Report from the Survey Strategies Task Group in the Exoplanet Science Yields Working Group

Natasha Latouf, Corey Spohn, Kevin Fogarty et al. (+12 more)

We present a variety of possible observational sequences from planet detection to characterization (i.e., survey strategies) for the initial exoEarth detection and characterization survey phase of the Habitable Worlds Observatory. An array of survey strategies were collated by the HWO Exoplanet Survey Strategies Task Group; by investigating and comparing expected resultant exoEarth yields for multiple survey strategies, we can better understand the most efficient strategy to characterize multiple planets in unconventional ways. We present eight varied survey strategies, presented as steps of Detection, Photometry, Orbit Measurement, and/or Characterization, including discussion of possible shortcomings and benefits of each strategy. We present exoEarth yield calculations from the Altruistic Yield Optimizer (AYO) and the Exoplanet Open-Source Imaging Mission Simulator (EXOSIMS) for select strategies, with initial considerations of thermal emission and detector noise. Herein, a yield is the expectation value given the astrophysical assumptions of the number of planets that meet the observational criteria during the survey. We find that AYO and EXOSIMS find the highest yields for H2O at 0.9 microns, although the calculated yields vary. When allowing for wavelength optimization within AYO, we find that H2O is most optimal to observe at 0.9 microns when given the full range of VIS and NIR values, with an expected exoEarth yield of 29.1. H2O and O2 dual characterization is possible at the expense of yield planet loss, with a calculated possible yield of 20.9 in the VIS. The characterization of CH4 and CO2 in the NIR result in the lowest exoEarth yields at 19.9 and 6.4 possible expected planets, respectively. Future work updating AYO and EXOSIMS is necessary to explore further survey strategies that follow different observation procedures, some of which is currently underway.
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All authors Natasha Latouf, Corey Spohn, Kevin Fogarty, Chris Stark, Dmitry Savransky n=and Eleonora Alei, Anthony Boccaletti, Gagandeep Kaur, Avi Mandell, Rhonda Morgan, Aki Roberge, Jean-Baptiste Ruffio, Jean Schneider, Edward Schwieterman, Armen Tokadijan, Daniel Viudez-Moreiras

Monday 21 September

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

1I/'Oumuamua-like objects's SFDs with older, current and future surveys

Rodrigo Albornoz-Montenegro and César Fuentes

Interstellar Objects (ISOs) may provide direct evidence of planetesimals ejected from early-stage solar systems. Studying these objects and predicting their detection through various surveys is vital for enhancing our understanding of planetary formation. We show how ISO size distribution (SFD), f_D~D^-q, controls expected detections in current and future surveys. We focus on TESS and the Vera Rubin Observatory LSST while analyzing Pan-STARRS and ATLAS. TESS is expected to detect ~1 ISO from shallow SFDs (q<2.5). On the other hand, LSST is likely to detect ~0.2-100 ISO yr^-1 over its lifetime. ATLAS is likely to detect one non-cometary object for populations with q<3. We further estimate ~0-8 detections of cometary like-3I/ATLAS objects by LSST in 10 years and ~1 cometary object detected by TESS over its mission time, independent of the nucleus size distribution. By considering both 1I/'Oumuamua and 3I/ATLAS from the same SFD, our results suggest that more asteroid-like objects are produced rather than comets during the early stages of planetary formation. Imminent detections of ISOs by LSST will help constrain the slopes of the SFDs for asteroid-like and cometary ISOs better, and, if applicable, differentiate between them.
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Instrumentation and Methods for Astrophysics

Convolutional Neural Network for Extraction of $n = 1$ Photon Ring of Black Holes

Courtney Duong, Frank Myhre and Joseph R. Farah

Very-long-baseline interferometry (VLBI) may enable direct imaging of fine, event-horizon scale black hole structures such as the photon ring. In particular, the $n=1$ photon subring encodes information about black hole spin in its radial profile and azimuthal brightness modulation, making it a key proxy for spacetime properties. However, the $n=1$ subring overlaps with emission from the $n=0$ subring in observations, requiring isolation of the $n=1$ subring for measurements. We present a convolutional neural network (CNN) capable of extracting the $n=1$ subring from composite images containing $n=0$ and $n=1$ subrings. We simulate $111,000$ $m$-ring images with eht-imaging to train and validate the CNN. The CNN accurately recovers the radial and angular brightness profiles of the $n=1$ subring with a mean normalized cross correlation (NXcorr) of $\sim0.99$. The CNN provides a detection capability by predicting no false positives when input images lack a $n=1$ subring. We perform feature extraction with ringfit to show that the CNN-predicted $n=1$ subring accurately recovers the ground truth radial profiles and azimuthal brightness modulation, demonstrating the CNN's capability to extract spin-sensitive features. We further test the CNN on black hole images generated by KerrBAM and general relativistic magnetohydrodynamic (GRMHD) simulations, finding that it recovers the overall $n=1$ subring radial profiles while exhibiting discrepancies in the recovered intensity profiles. These results demonstrate the potential of deep learning methods to isolate the $n=1$ subring from overlapping emission, providing a framework for analyzing future high resolution black hole images from proposed VLBI missions such as the Black Hole Explorer (BHEX).
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Instrumentation and Methods for Astrophysics · Cross-listed from astro-ph.EP

A Very Fast Symplectic Integrator for Planetary Systems: WHFast512 in x86 Assembly

Rishit Dagli and Hanno Rein

Long-term integrations of planetary systems are used in a variety of astrophysical applications, for example to determine a system's stability. In this paper, we describe significant speed and accuracy improvements to the WHFast512 integrator. By completely rewriting WHFast512 in x86 assembly, we gain fine-grained control over CPU instructions and are able to keep the entire simulation state in CPU registers, only writing to memory when an output is required. We also reduce the number of branches in the code, improve the Kepler solver's speed and accuracy with a convergence check, switch the Hamiltonian splitting to Jacobi coordinates, implement symplectic correctors, and add support for ejection and collision detection. As a benchmark we run simulations of the Solar System with all eight planets and general relativistic corrections for 5 Gyr. With our improvements, we can integrate this system in 9 hours, more than 8x faster than the standard version of WHFast. This makes WHFast512 by far the fastest N-body integrator for this kind of simulation. We run extensive tests to verify the integrator's accuracy, with a particular focus on removing any long-term bias, and show that the accuracy is now five orders of magnitude better for typical setups compared to the earlier version of WHFast512. The new WHFast512 integrator is freely available in the REBOUND integrator package.
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