Instrumentation and Methods for Astrophysics
Unveiling photometric transients with the ILMT and their follow-up using DOT
Priyanshi Kumari, Bhavya Ailawadhi, Talat Akhunov et al. (+10 more)
The two 4-m class
telescopes at the Devasthal site in India are complementary: the 4-m International
Liquid Mirror
Telescope (ILMT) is dedicated to the discovery of new transient phenomena, which can be followed up using the 3.6-m Devasthal Optical
Telescope (DOT) for their optical characterisation. The ILMT continuously surveys a $22.3^\prime \times 22.3^\prime$ wide strip of the zenith sky. A dedicated transient detection and classification pipeline, PyLMT, based on optimised image subtraction and convolutional neural network (CNN) techniques, enables the discovery of numerous photometric transients with the ILMT. However, these transient discoveries still require spectroscopic confirmation to determine their types, for which spectra obtained with the DOT are essential. Here, we present a selection of photometric transients, specifically
supernova candidates. We investigate their photometric and spectroscopic properties using ILMT light curves, supplemented with ZTF data and DOT spectroscopic observations.
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All authors Priyanshi Kumari, Bhavya Ailawadhi, Talat Akhunov, Monalisa Dubey, Naveen Dukiya, Sara Filali, Paul Hickson, Gokul Singh Mehra, Kuntal Misra, Vibhore Negi, Kumar Pranshu, Jean Surdej, Sarvesh Kumar Yadav
Instrumentation and Methods for Astrophysics
Simulation and Deep Learning Inversion for Full-sky Energetic Neutral Atom Imaging at the Heliospheric Boundary
Weining Wang, Rong Lin, Jiansen He et al. (+11 more)
The distribution of energetic neutral atom (ENA) differential flux density at the heliospheric boundary is the key to revealing the interaction mechanism between the solar wind and the interstellar medium. The Helios ENA Rapid Mapper (HERMA) is a pre-research project for a new ENA detection concept and technical principle, which is designed to provide technical support for refined detection with its advantages of wide energy range coverage and high spatial resolution. To verify the high spatiotemporal resolution rapid imaging capability of the HERMA, this study restored the particle response characteristics of the HERMA (spatial modulation grid + 256-pixel linear array silicon detector) based on Geant4 simulation, and we compared the performance of the traditional inversion
algorithm with the convolutional neural network (CNN) inversion
algorithm using INCA data as the
benchmark. The results show that under different sampling times, the source distribution inverted by the CNN
algorithm has a higher degree of agreement with the ideally expected distribution, and the CNN
algorithm exhibits better stability and accuracy than the traditional
algorithm under the conditions of low signal-to-noise ratio and short staring time. This study provides core technical support for the engineering verification and on-orbit application of the HERMA instrument, fills the technical gap in wide-energy-range and high-resolution ENA detection, and helps to deepen the research on the interaction mechanism between the solar wind and the interstellar medium at the heliospheric boundary.
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All authors Weining Wang, Rong Lin, Jiansen He, Hong Zou, Yongfu Wang, Jiali Chen, Yuguang Ye, Weihong Shi, Wei Zhang, Xi Lu, Die Duan, Tianhang Chen, Linghua Wang, Qiugang Zong
Instrumentation and Methods for Astrophysics · Cross-listed from astro-ph.EP
Ultraviolet Reflected-Light Spectroscopy of Modern Earth Analogs with Starshade Observations
Zahra Ahmed, Stuart Shaklan, Mario Damiano et al. (+3 more)
Characterizing rocky, habitable zone
planets in reflected light is a key science objective for future direct imaging missions, and the ultraviolet (UV) is uniquely suited for supporting the characterization of Earth-like atmospheres. The Hartley-Huggins ozone band at $0.25 ~\mu\mathrm{m}$ remains detectable at low spectral resolution down to $0.1\%$ PAL $\mathrm{O}_2$, providing a sensitive proxy for oxygen-bearing chemistry even for Proterozoic-like atmospheres where the visible and near-infrared $\mathrm{O}_2$ features become inaccessible. This work uses an end-to-end framework for UV starshade spectroscopy of modern Earth analogs that integrates high-fidelity observation simulation, conservative post-processing, and
Bayesian spectral
retrieval to quantify ozone detection significance. The framework is applied across 108 simulated scenes spanning a range of
planet phase angles, system inclinations, and exozodiacal dust densities. The results establish that robust ozone detection with UV-only, $R=7$ reflected light spectroscopy requires a photometric $\mathrm{SNR} \sim 30$. UV starshade observations with the Habitable Worlds Observatory achieve this threshold within 3-day integrations for solar-type systems at 10 parsecs with inclinations up to $60^\circ$ at favorable phases ($\leq 90^\circ$). Above this SNR threshold, ozone detections are robust to systematic bias from imperfect exozodi calibration within $\sim30\%$, and the median retrieved ozone abundance remains accurate to within one order of magnitude of the true value.
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All authors Zahra Ahmed, Stuart Shaklan, Mario Damiano, Armen Tokadjian, Edward W. Schwieterman, Simone D'Amico
Instrumentation and Methods for Astrophysics
Graph Learning with Multiplicative Aggregation for Solar Filament Fragment Association Modeling in Full-disk H$\alpha$ Images
GaoFei Zhu, GangHua Lin, Xiao Yang et al. (+4 more)
Solar filaments are prominent features in H$\alpha$ observations. During their
evolution, they often exhibit complex morphological and topological changes, which, due to line-of-sight projection effects, appear as fragmentation and merging. These phenomena make it difficult to reliably track solar filaments using automated methods, as the same structure may appear different over time. To address this issue, we formulate filament fragment association as a graph-based learning problem, where individual fragments are represented as nodes and candidate pairwise relationships are modeled as edges to infer whether fragments belong to the same filament. Furthermore, we propose a novel graph neural network (GNN) that incorporates multiplicative aggregation layers to enhance feature aggregation in the graph, replacing conventional additive aggregation with a multiplicative mechanism to better capture information between fragments. Experimental results demonstrate that the proposed method achieves reliable performance in filament fragment association, validating the effectiveness of the graph-based formulation and the proposed multiplicative aggregation mechanism.
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All authors GaoFei Zhu, GangHua Lin, Xiao Yang, Suo Liu, Yu Liu, ZhiSheng Gao, Cheng Zeng
Instrumentation and Methods for Astrophysics · Cross-listed from astro-ph.EP
DEMOS. II. What the full Lupus demographics truly tell us about protoplanetary disc evolution
Lorenzo A. Malanga, Giovanni P. Rosotti, Giuseppe Lodato et al. (+7 more)
Assessing whether disc
evolution is driven by viscosity or MHD winds is crucial to determine the conditions in which
planets form. The rich statistical information encoded in nearby
star-forming regions provides an excellent opportunity to probe disc
evolution. In this paper, we apply the
Bayesian inference tool DEMOS, designed to exploit the statistical properties of disc populations and infer initial conditions and model parameters, to the Lupus disc population. We consider the observed distributions of stellar mass $M_\star$, disc mass $M$ (from continuum flux), mass
accretion rate $\dot{M}$ and CO disc size $R_{90%, CO}$. We constrain all parameters for both the viscous model, with internal photoevaporation, and the MHD winds model, with unprecedented precision. The inferred initial conditions suggest that positive correlations between initial disc properties and $M_\star$ must be present. Both models yield a typical $R_0$ of $3-5$ au for a disc around a $0.3\ \text{M}_\odot$
star, with positive stellar-mass dependence. We infer $\alpha$ (or $\alpha_\text{DW}$) of $3-4 \times 10^{-4}$ for discs around $0.3\ \text{M}_\odot$
stars, with an approximately quadratic dependence on $M_\star$. For the viscous-photoevaporative model, the typical wind mass-loss rate is $\dot{M}_\text{w} \sim 10^{-9}$ M$_\odot$ yr$^{-1}$, scaling with $M_\star$ with an exponent of $\sim 2.5$. The inferred mean age is 1.6 Myr for the MHD winds model and 0.9 Myr for the viscous-photoevaporative model. Despite a slight preference for the latter,
Bayesian evidence shows that both models reproduce the data comparably well, and neither can be ruled out. However, their best-fit synthetic populations predict substantially different evolutionary pathways. Applying DEMOS to multiple populations, especially evolved ones like Upper Scorpius, will allow to place stronger constraints on disc
evolution mechanisms.
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All authors Lorenzo A. Malanga, Giovanni P. Rosotti, Giuseppe Lodato, Alice Somigliana, Benoît Tabone, Rossella Anania, Carlo F. Manara, Leonardo Testi, Claudia Toci, Francesco Zagaria
Instrumentation and Methods for Astrophysics · Cross-listed from astro-ph.EP
DEMOS I. A Bayesian inference framework to constrain disc evolution from observed demographics
Lorenzo A. Malanga, Giovanni P. Rosotti, Giuseppe Lodato et al. (+3 more)
The mechanism driving the
evolution and dispersal of protoplanetary discs has a substantial impact on the theories of
planet formation. To date, neither the viscous paradigm nor the MHD wind-driven framework have been ruled out by the observations. The statistical richness of the observed samples provides a unique opportunity to test evolutionary models through population synthesis. However, comparisons between models and observations are normally carried out by eye, and a more statistically rigorous approach has not yet been implemented. We present DEMOS, a
Bayesian inference tool that aims to infer the model parameters and initial conditions that best reproduce the full distribution of global properties of disc populations. This framework relies on a comparison between observations and synthetic populations generated with the 1D disc population-synthesis code Diskpop. We tested the tool with both the MHD wind model and the viscous framework with internal photoevaporation. Applying DEMOS to synthetic populations, we find that, for both models, it accurately recovers the model parameters of disc populations and their associated uncertainties, including cases in which initial conditions are correlated with the stellar mass. In the optimal scenario in which disc masses,
accretion rates and disc sizes are available, the tool can also constrain the angular-momentum transport parameter. We show that, although parameter estimates are the most precise with samples of about 100 discs, the tool also yields meaningful constraints for smaller populations of 10-30 discs. We further find that the predictions obtained with DEMOS closely align with those found by previous works. We anticipate that DEMOS will be a valuable tool to infer the initial conditions of observed disc populations, to design observational programmes, and to assess which evolutionary model best reproduces the data.
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All authors Lorenzo A. Malanga, Giovanni P. Rosotti, Giuseppe Lodato, Benoît Tabone, Alice Somigliana, Leonardo Testi
Instrumentation and Methods for Astrophysics
Estimating the spectral resolution of a radio data cube from its noise properties
P. Serra, G. Mulas, K. M. Hess et al. (+4 more)
We present a new method to estimate the spectral resolution of a
radio data cube from its noise properties. We assume that the data cube is the result of the convolution of a spectrally-uncorrelated cube with a constant convolution kernel K. We demonstrate that K and the resolution, defined as K's integral, can be estimated thanks to the analytical relation between the autocorrelation of the noise spectra extracted from the cube and the power spectrum of the kernel. We discuss a number of
algorithms that make this method robust in the realistic case of a finite number of spectra and channels, real signal and artefacts, and we present the results obtained for a number of
datasets from the literature. We further highlight that this method is applicable to any data cube that meets our assumptions independently of the observing frequency or the exact nature of the cube's "third" axis, e.g., it could be used to measure the time resolution of transient-search data cubes. We make this method available as part of the open-source software tool spectre.
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All authors P. Serra, G. Mulas, K. M. Hess, P. Kamphuis, T. Westmeier, Y. Ascasibar, J. M. van der Hulst
Instrumentation and Methods for Astrophysics · Cross-listed from astro-ph.CO
Beyond the Kullback–Leibler divergence: the likelihood-ratio distribution as a detectability measure
Amirhossein Samandar, Mikel Martin Barandiaran, Linn Htat Lu et al. (+13 more)
The Kullback–Leibler (KL) divergence is the expectation value of the log-likelihood ratio between two specified models, evaluated with respect to one of the two models. It provides an average measure of the distinguishability of the models. We examine its connection to discovery potential and the accuracy of parameter estimates when one model represents a null hypothesis, and the alternative is a family of models characterized by one or more continuous parameters. For data drawn from a multivariate Gaussian distribution, we derive the log-likelihood-ratio distribution and its cumulants under two such hypotheses. We then search over the alternative model's parameter space and use peak prominence, the height of a likelihood-profile peak above its surrounding baseline, as an operational detection statistic. Repeating this search on simulated data drawn from the null model allows us to calibrate the prominence threshold for a specified false-positive rate. We study a toy Gaussian model as well as three cosmological examples involving
cosmic topology, the tensor-to-scalar ratio, and dark energy. In all these cases, detectability of a particular model depends not only on KL divergence, but also on the size of the model parameter space, the likelihood-ratio distributions, and the distance of the model from any parameter space boundaries. The relationship between KL divergence, discovery potential, and parameter estimation must therefore be calibrated for each family of models and detection statistic under consideration.
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All authors Amirhossein Samandar, Mikel Martin Barandiaran, Linn Htat Lu, Andrew H. Jaffe, Deyan P. Mihaylov, Glenn D. Starkman, Cynthia Trendafilova, Yashar Akrami, Stefano Anselmi, Javier Carrón Duque, Craig J. Copi, Fernando Cornet-Gomez, Arthur Kosowsky, Anna Negro, Ricardo G. Rodrigues, Andrius Tamosiunas
Instrumentation and Methods for Astrophysics
OASIS: On-the-fly ASynchronous Image Stream classification – Transformer-based models for the early detection of strongly lensed supernovae
Satadru Bag, Raoul Canameras, Sherry H. Suyu et al. (+4 more)
Time-domain surveys often produce asynchronous image sequences across irregular observation times and photometric bands. In many applications, predictions are needed after each new observation, particularly when transient sources must be identified early to trigger follow-up. Strongly lensed
supernovae (LSNe), for which follow-up is particularly time-critical, provide a useful example. We formulate this setting as On-the-fly ASynchronous Image Stream (OASIS) classification and explore reusable
transformer architectures for causal, on-the-fly classification. We demonstrate these architectures on the lensed-transient classification problem studied by Bag et al. (2026a,b), distinguishing LSNe Ia from unlensed astrophysical transients. We compare them with the recurrent baseline adopted by Bag et al. (2026b), which combines a ConvLSTM branch for image processing with a separate LSTM branch for encoding observation times. The baseline maps asynchronous observations onto a fixed band-by-observation grid, filling unobserved bands with zeros. Using a common training setup, we compare two
transformer-based approaches to modeling interactions among spatio-temporal
tokens: factorized
attention, which separates spatial and temporal interactions, and joint
attention, which models them together. On the demonstration
dataset, factorized architectures achieve mean losses of $0.095$–$0.100$, compared with $0.128$ for the recurrent baseline and $0.107$ or higher for joint architectures. A selective readout further reduces the best loss to $0.089$ for a factorized model. These results suggest that factorized
attention suits this
dataset better, without implying universal architectural superiority. We release the architectures as reusable foundations for time-domain astronomy and other applications involving evolving spatial features that require on-the-fly predictions.
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All authors Satadru Bag, Raoul Canameras, Sherry H. Suyu, Stefan Schuldt, Stefan Taubenberger, Irham Taufik Andika, Alejandra Melo
Instrumentation and Methods for Astrophysics · Cross-listed from cs.HC
Particle Selection in Virtual Reality
Boaz Keren-Gil, James Gain, Lucia Marchetti et al. (+2 more)
Point-cloud representations of 3D data are used in many fields, from astronomical surveys of
galaxies to medical imaging and
laser scans of buildings. A task common to all of these is interactive selection: picking out a meaningful subset of points for closer analysis. The data is inherently 3D, yet the traditional workflow uses a 2D mouse on a 2D projection and inherits the limitations of a flat screen. This paper compares Virtual Reality (VR) and desktop environments for 3D particle selection, asking whether VR's six-degree-of-freedom hand interaction yields measurable gains in accuracy, speed, usability, workload and flow. In a counterbalanced within-subjects experiment (N = 34), participants selected 90 target
galaxies from a catalogue of 16,816
galaxies, using mouse-driven selection with keyboard camera control on the desktop and a hand-held spherical brush that adds or removes particles in VR. We measured task accuracy, completion time, and three standard questionnaires: the System Usability Scale (SUS), the NASA Task Load Index (NASA-TLX) and the Flow State Scale (FSS). Accuracy was comparable (91.6% VR vs. 93.1% desktop; p = 0.858), but every other measure favoured VR. Tasks were 23% faster (p = 0.001, d = 0.93), usability was higher (SUS 84.6 vs. 54.0; d = -1.78), workload was lower (NASA-TLX 20.4 vs. 34.7; d = 0.90), and flow was higher (FSS 83.2 vs. 70.2; d = -1.23). Because the underlying problem, making a 3D selection through a 2D interface, is not specific to astronomy, we expect the pattern to carry over to point-cloud selection in other fields.
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All authors Boaz Keren-Gil, James Gain, Lucia Marchetti, Patrick Marais, Alexander Sivitilli
Instrumentation and Methods for Astrophysics
Extinction by non-spherical particles with the Nanoparticle Transition Matrix Code: perspectives for interstellar medium and exoplanetary atmospheres
G. La Mura, G. Mulas, M. A. Iatì et al. (+4 more)
The Transition
Matrix formalism (T-
matrix) offers an optimal solution to the problem of radiation scattering and absorption by particles characterized by a high degree of chemical and structural complexity. However, its application to particle models composed by large numbers of constituents has been classically limited by the substantial computational burden of the method. As a consequence, the investigation of light scattering and absorption processes, in cases where particle asymmetry and non-homogeneity play an important role, typically need to rely on simplifying assumptions which affect the correct interpretation of transmission spectra and extinction curves. This problem has a huge impact on many fields of scientific application. In particular, it has a critical role in astrophysics, because the ability to model highly non homogeneous particles is a fundamental requirement to understand the role of extinction in different phases of the interstellar medium (ISM), as well as to probe the physics of light transmission in planetary atmospheres within the Solar System and beyond. In this work we describe the application of a new implementation of the T-
matrix formalism, based on the framework originally developed by Borghese, Denti & Saija and named Nano-particle Transition
Matrix code, which leverages the power of modern hardware architectures to reduce the computational costs related to the solution of relevant particle models for the investigation of dust in the ISM and of hot-Jupyter class
planets atmospheric aerosols. This new implementation of the T-
matrix formalism can handle calculations of increasing complexity with a direct impact in the interpretation of the transmission spectra of transiting
exoplanets produced by the James Webb Space
Telescope (JWST) and of those expected from the Atmospheric Remote-sensing Infrared
Exoplanet Large survey (ARIEL).
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All authors G. La Mura, G. Mulas, M. A. Iatì, C. Cecchi-Pestellini, G. Aresu, S. Rezaei, R. Saija
Instrumentation and Methods for Astrophysics · Cross-listed from astro-ph.HE
An end-to-end experimental pipeline for automated track identification for paleo-detectors
Claudio Galelli, Alessandro Testi and Lorenzo Caccianiga
Paleo-detectors aim to exploit the damage tracks left in natural minerals by
cosmic messengers over geological times to reconstruct past fluxes of energetic particles. For
cosmic rays, the expected signal is abundant, and the tracks are micron-scale, so they can be revealed by etching and read out with conventional optical microscopy; what remains challenging is turning a bulk sample into a calibrated track-length spectrum that can be quantitatively confronted with theoretical predictions. In this work we present the experimental pipeline we developed for this purpose within the INFN PRImuS experiment, from sample preparation and automated multi-focus microscopy to track recognition with OptimusPrimus, a two-stage machine-learning
algorithm, and to a forward-folding model that propagates
geometric sectioning, etching and the measured detection response into simulated spectra. The pipeline was built and validated on chemically etched obsidian where the recovered track-length distribution is statistically compatible with expert annotation. In parallel, we developed and characterized a dry argon-
plasma etching protocol for halite, an evaporite mineral of particular interest as a low-background
cosmic-ray paleo-detector. Since every stage of the pipeline is mineral-agnostic, with only the trained networks and the detection response to be re-derived from annotated data, we are now applying it to
plasma-etched halite with the aim of identifying
cosmic-ray-induced tracks.
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Instrumentation and Methods for Astrophysics · Cross-listed from astro-ph.GA
A Quest for Changing-State AGNs
Kasra Mokhtarpour and Vicki Sarajedini
We use Gaia Data Release 3 (DR3) light curves to identify photometrically selected changing-state active galactic
nucleus (CSAGN) candidates. We employ unsupervised deep learning and anomaly detection methods to identify unusual patterns of variability in the light curves of 872,228 AGNs with known variability. We find that 8,504 light curves show anomalous behavior. Among these, 682 sources show evidence for two-state photometric behavior during the Gaia DR3 observing baseline. These 682 CSAGN candidates were selected using an automated statistical pattern recognition approach based on Gaussian mixture models (GMMs) to identify sources whose magnitude distributions are consistent with two preferred brightness levels, substantially reducing the need for human visual inspection in selecting the CSAGN candidates. The candidates tend to have fainter absolute magnitudes and lower
redshifts than the non-anomalous Gaia variable AGN sample, suggesting that some may be accreting at lower rates. Our approach builds a large candidate sample with less reliance on human visual selection. Follow-up observations of these sources can help identify what drives changing-state transitions and clarify their connection to
accretion-disk physics and supermassive black hole (SMBH) fueling.
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Instrumentation and Methods for Astrophysics
Initial Performance Tests of MASTA for Wide-Field Narrowband Imaging
Mingxuan Xu, Jiang-Tao Li, Li Ji et al. (+2 more)
Wide-field narrowband imaging provides an efficient approach for mapping low-surface-brightness line emission over large angular scales. We present initial performance tests of Multi-Application Survey
Telescope Array (MASTA) for narrowband imaging. Using commissioning data obtained with the QHY461 and COSMOS-8K cameras, we characterize the basic reduction procedure, photometric calibration, image quality, and point-source detection limits. The measured surface-brightness and point-source detection limits become deeper with increasing exposure time, roughly following the expected background-limited scaling. Under the current observing and reduction conditions, the COSMOS-8K camera generally reaches deeper surface-brightness limits, particularly in the red filters, while QHY461 has smaller pixel size and provides better sampling of the
telescope point spread function (PSF). As demonstrations, we perform continuum subtraction for the nearby
galaxy M106 and the
supernova remnant Cygnus Loop using adjacent narrowband filters and reveal the H{\alpha} and [O III] structures in M106 and filamentary structures in the Cygnus Loop. We further compare the MASTA surface brightness H{\alpha} map of M106 with an independent SITELLE measurement. After accounting for [N II] contamination and the wavelength-dependent response of the 6544 Å filter in the f /1.7 converging beam, the corrected median ratio is 0.973. These tests indicate that MASTA is a promising facility for wide-field narrowband imaging. Further improvements in calibration frames, including flux calibration, field-dependent bandpass characterization, and spatially varying PSF modeling and matching will be required for quantitative studies of diffuse ionized gas.
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All authors Mingxuan Xu, Jiang-Tao Li, Li Ji, Chen Zhang, Yantian Zhu
Instrumentation and Methods for Astrophysics
Institutional broadening of the Japanese astronomy and astrophysics research community during 1996-2025
Hideaki Fujiwara
Using bibliometric data from publication records, we investigate the institutional broadening of Japanese astronomy and astrophysics over 1996-2025. The number of Japan-affiliated publications increased from 588 to 2,156, and the number of unique participating Japanese institutions increased from 123 to 227, indicating a nominal broadening of the institutional base. However, institution-publication links remained concentrated among leading institutions, and the effective number of institutions, a frequency-weighted diversity measure, increased more gradually than the number of unique participating institutions. These results show that Japanese astronomy and astrophysics broadened institutionally, but not through a complete equalization of institutional participation. The observed change is best interpreted as an expansion of the participating institutional base around a persistent core of leading institutions.
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Instrumentation and Methods for Astrophysics · Cross-listed from stat.ML
$\texttt{NFDiag}$: High-dimensional MCMC Convergence Diagnosis with Normalizing Flows
Kutay Nazl{\i} and Elena Sellentin
Accurately assessing the convergence of Monte Carlo Markov Chains is crucial for ensuring their validity in
Bayesian inference problems. However, convergence assessment in high dimensions is known to be difficult, due to most convergence diagnostics becoming insensitive beyond a few dozen parameters. Conventional convergence statistics analyze marginal distributions which in high dimensions neglects increasing levels of information from the joint and conditional probability distributions. Thus we propose $\texttt{NFDiag}$, which taps into the information of the full multivariate joint posterior with a normalizing flow (NF). Our convergence diagnostic thereby also analyzes information from conditional distributions. In other words, by working in the full multivariate space, it is sensitive with respect to the geometry of the chain in high dimensions. Our diagnostic utilizes that NFs assume independent and identically distributed (iid) training samples, which is a testable assumption that will be violated by a non-converged or strongly correlated chain. Our diagnostic trains NFs on separate parts of a chain, and then assesses whether these correspond to the same multivariate target distribution. We test our diagnostic in up to 250 dimensions, and demonstrate it is universal and unbiased, and detects problematic chain quality also when Gelman-Rubin $\hat{R}$ and the multivarite effective sample size do not.
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Instrumentation and Methods for Astrophysics
Deep learning methods for gamma event selection in TAIGA-IACT image analysis in stereo-mode
E. O. Gres, A. P. Kryukov, P. A. Volchigov et al. (+6 more)
In astrophysical complex TAIGA there are four Imaging Atmospheric Cherenkov
telescopes (IACTs), located at a distance of 300-500 m from each other, due to which extensive air showers (EAS) generated by
cosmic and gamma radiation are recorded by a single
telescope or by multiple
telescopes simultaneously (stereo-mode). The analysis of EAS images from two or more Cherenkov
telescopes together is of particular interest, since for this approach the reconstruction of physical parameters of the primary gamma ray is much more accurate than in mono-observations. However, the gamma-ray event reconstruction strongly depends on the quality of the
hadron background suppression of the classifier, therefore, the gamma event selection stage is one of the important ones in the further analysis of IACT data. We propose and study several neural network methods for gamma event selection in joint TAIGA-IACT image analysis. Neural network methods we suggested involves a single-stage analysis of IACT images from two
telescopes, while the second method considers the formation of autoencoder-derived essential features that contain the EAS physical parameters, including the type of primary particle.
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All authors E. O. Gres, A. P. Kryukov, P. A. Volchigov, A. P. Demichev, J. J. Dubenskaya, D. P. Zhurov, S. P. Polyakov, E. B. Postnikov, A. J. Razumov
Instrumentation and Methods for Astrophysics · Cross-listed from astro-ph.HE
Equation of State Classification of Core-Collapse Supernovae with a Network of Gravitational-Wave Detectors
A. Akhmetali, Y. S. Abylkairov, J. A. Font et al. (+2 more)
Gravitational waves from core-collapse
supernovae may provide a unique probe of the equation of state (EOS) of dense nuclear matter. In previous work, we assessed the potential of machine-learning methods for EOS classification for rapidly rotating
supernovae using individual gravitational-wave detectors. Here, we extend the framework to detector networks and investigate the impact of detector configuration, source distance, arrival time, and sky location on the classification. Using convolutional neural networks, we perform 10-class EOS classification directly from noisy detector data for both second- and third-generation observatories. We find that detector geometry strongly influences classification performance. For Galactic events, current detector networks can achieve promising classification performance, but the reach depends sensitively on the detector network
antenna response. Adding detectors improves time-averaged performance and produces more uniform classification across the sky. Third-generation observatories extend the classification reach to 100 kpc for favorable source orientations but do not remove the dependence on detector geometry.
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All authors A. Akhmetali, Y. S. Abylkairov, J. A. Font, M. Zanolin, E. Abdikamalov
Instrumentation and Methods for Astrophysics · Cross-listed from astro-ph.GA
TractorJAX: GPU-Accelerated SPHEREx Forced Photometry and the Trade-offs of Regularization and External Priors
Hyeonguk Bahk and Ho Seong Hwang
We present TractorJAX, an open-source GPU reimplementation of the catalog-forced mode of The Tractor. We use it to test whether regularization or external flux priors enable reliable fitting of reference catalogs that extend below the depth directly constrained by undersampled SPHEREx images. For full-depth regularized fits, TractorJAX fits $100\times100$-pixel cutouts at 11.9 ms each on a single NVIDIA H100 GPU. In simulated images of the Abell 2537
galaxy-cluster field, extending the fitted catalog from $m_z<21$ to the full DESI Legacy Imaging Surveys DR10 catalog increases the scatter in fractional flux residuals by 34%, even with regularization. We also test the methods using SPHEREx observations in nine fields, assessing the number of reliably measured sources and photometric-
redshift accuracy. Requiring S/N $>5$ in more than half of the 102 channels, we find that the regularized full-depth fit yields 40% fewer sources than the truncated fit. For sources meeting this requirement in both fits, the normalized median absolute deviation of the photometric
redshifts, $\sigma_{\rm NMAD}$, is 17% larger in the full-depth fit. External flux priors recover nearly as many sources as the $m_z<21$ fit, but increase $\sigma_{\rm NMAD}$ by 0.0015 for the common calibrated sample. Neither regularization nor the external flux priors tested here simultaneously preserve the sample size and photometric-
redshift accuracy achieved by the truncated fit. Our results suggest that the optimal fitted-catalog depth cannot be determined from image-level performance alone, but should be selected through end-to-end tests tailored to the intended science measurement.
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