arXiv subjects

Argyro Sasli

Publications and source records attributed to Argyro Sasli.

17 recordsLinked to original sources

arXiv2026

AppleCiDEr. II. SpectraNet: A Spectroscopic Neural Network Classifier for Transients Demonstrated on ZTF Follow-up Data

Time-domain surveys such as the Zwicky Transient Facility have opened a new frontier in the discovery and characterization of transients. While photometric light curves provide broad temporal coverage, spectroscopic observations remain crucial for physical interpretation and source classification. However, existing spectral analysis methods, often reliant on template fitting or parametric models, are limited in their ability to capture the complex and evolving spectra characteristic of such sources, which are sometimes only available at low resolution. In this work, we introduce SpectraNet, a deep convolutional neural network designed to learn robust representations of optical spectra from transients. Our model combines multi-scale convolution kernels and pooling operations to extract features from preprocessed spectra in a hierarchical and interpretable manner. We train and validate SpectraNet on low-resolution time-series spectra obtained from the Spectral Energy Distribution Machine and other instruments, demonstrating better performance in classification compared to other known pipelines.

astro-ph.IM↗

arXiv2026

Follow-up of SN 2025wny II: Superluminous Supernova Physics at Cosmic Noon

SN 2025wny is a gravitationally lensed, hydrogen-poor superluminous supernova (SLSN-I) at z = 2.015. To date, it is the most extensively observed high-redshift core-collapse SN and has the most detailed rest-frame UV observations of any SLSN. We present densely sampled rest-frame UV-to-optical photometry and spectroscopy out to +80 d post-peak (rest frame) from several facilities, including JWST, Keck, VLT, Gemini, the Palomar 200-inch, the Fraunhofer Telescope at Wendelstein, and the Liverpool Telescope. Correcting for lensing magnification, SN 2025wny reaches a peak pseudo-bolometric luminosity of $L_{\rm peak}\gtrsim4\times10^{44}$ erg s$^{-1}$ over rest-frame 1500-4230 Å, placing it within the luminosity range of typical SLSNe-I. SN 2025wny exhibits several unusual features, including a continuum excess and sharp spectral features in the FUV from +20-60 d that coincide with an FUV light-curve plateau and higher inferred blackbody temperatures. SN 2025wny's spectra also show little to no UV line blanketing, no obvious O II absorption despite high temperatures, and evidence for C II, H$α$, and possible He I. Light-curve modeling suggests that SN 2025wny may require a hybrid or non-standard power source. This work provides some of the first detailed constraints on high-redshift SLSNe and establishes SN 2025wny as an essential spectral and photometric reference for identifying and interpreting high-redshift SLSNe discovered by Rubin and Roman.

astro-ph.CO↗

arXiv2026

Learned proposals in trans-dimensional inference are optimal at equilibrium, not during assembly

Inferring the dimension of a model - the number of components needed to explain data - jointly with the parameters is a pervasive problem, from counting sources in an image to mixture modeling, and reversible-jump Markov chain Monte Carlo solves it exactly but mixes slowly. Learned proposals are well established at fixed dimension, but whether they can accelerate the dimension-changing moves themselves has remained largely untested. We show that the answer has a structural origin: the optimal proposal for the dimension-changing birth move is a different object in different phases of the run. While the fit is being assembled it must match the current residual - a state-dependent quantity no state-independent network can represent - but at equilibrium it degenerates to the posterior's single-component marginal, which is exactly the distribution an adaptive normalizing flow learns from the sampler's own history. A learned state-independent birth proposal is therefore useless in one phase and optimal in the other. Controlled experiments confirm the attribution: applied with an exact Metropolis--Hastings correction that leaves the target invariant for any network, the learned births leave acceptance rates unchanged yet accelerate model-order mixing - in a ten-seed benchmark they meet a pre-specified stopping rule in six of ten runs, typically several times sooner, where a strong hand-tuned baseline meets it in one (one-sided p=0.03) - and an isolation experiment shows the same flow deployed within-model buys nothing. Making no domain-specific assumptions, the same sampler counts sources in a noisy image and reconstructs signals across scientific domains, including gravitational waves from ground- and space-based detectors and a scalp EEG recording. We release the method as HyperWave, an open-source package.

physics.data-an↗

arXiv2026

Catching Disguised Transients with ASTRANet: Anomaly-Aware Spectroscopic Classification and Conformal Calibration

Time-domain surveys discover thousands of transients per year, but the spectroscopic identification of rare and physically peculiar objects remains rate-limited by closed-set classifiers that confidently assign every input to a known class -- including spectra that genuinely belong to no known class. We present the \texttt{ASTRANet} framework, a confidence-aware infrastructure for spectroscopic transient classification built around three coupled modules: a hierarchical spectral classifier that operates directly on observer-frame spectra without requiring host-galaxy redshift or spectral phase as inputs; an anomaly detection layer (\texttt{ASTRANet-Sentinel}) that non-linearly combines $16$ embedding-space anomaly scores spanning four physically motivated families; and a conformal uncertainty quantification layer (\texttt{ASTRANet-CP}). We validate the framework on a held-out evaluation set of $289$ rare and out-of-taxonomy transients spanning $11$ classes deliberately excluded from training, chosen to span the full physical diversity of the rare-anomaly population: AGN-related outliers, GRB-related events, gap transients, novae, and peculiar supernovae. Through five astrophysically distinct failure modes of closed-set classifiers, we show that classifier-internal uncertainty and embedding-based anomaly detection are structurally complementary axes of confidence rather than alternative implementations of the same estimator. We further introduce AD-stratified Mondrian conformal prediction (AD-MCP) within \texttt{ASTRANet-CP}, achieving uniform conditional coverage across anomaly-score strata where vanilla Mondrian under-covers in the operational regime. This establishes the methodological infrastructure for confidence-aware spectroscopic discovery in the Vera C.\ Rubin Observatory era.

astro-ph.IM↗

arXiv2026

Constraints on Late-Time Flaring from Luminous Fast Blue Optical Transients using the Transiting Exoplanet Survey Satellite and the Zwicky Transient Facility

The Luminous Fast Blue Optical Transient (LFBOT) AT2022tsd exhibited minutes-timescale optical flares in the tens of days following the initial transient event, likely due to a central engine -- either an accreting black hole or a magnetar. In this paper, we use data from the Transiting Exoplanet Survey Satellite (TESS) and the Zwicky Transient Facility (ZTF) to constrain the occurrence of similar flares in the 12 (of 14) known LFBOTs that had observational coverage with TESS from tens of days to thousands of days after the transient's initial emission. We find seven flare-like signals at the locations of four unique LFBOTs; all seven can likely be attributed to a solar system object (SSO) moving through the TESS aperture. Assuming all seven flares arise from SSOs, for the LFBOT AT2024qfm we rule out flaring with a similar timescale (40--65 d) and luminosity ($νL_ν\sim10^{43}$ erg s$^{-1}$) as in AT2022tsd, while for AT2022tsd itself we rule out flares between 380--430 d after the initial transient that were as luminous as the earlier flares. This observation suggests that the engine power in AT2022tsd declined or shut off on a timescale of hundreds of days. We also find that there is no late-time activity detectable in TESS thousands of days after the prototype LFBOT, AT2018cow. We discuss our constraints on the duty cycle of such flaring and then present estimates for the number of minutes-duration flares detectable with ongoing and upcoming high-cadence ($\ll1$ d) wide-field surveys.

astro-ph.HE↗

arXiv2026

Hyrax: An Extensible Framework for Rapid ML Experimentation and Unsupervised Discovery in the Era of Rubin, Roman, and Euclid

The NSF-DOE Vera C. Rubin Observatory, Roman Space Telescope, Euclid, and other next-generation surveys will deliver imaging, spectroscopic, and time-domain data at scales that increasingly shift the bottleneck in astronomical machine learning (ML) projects from model design to infrastructure. We present Hyrax, an open-source, modular, GPU-enabled Python framework that supports the full ML lifecycle in astronomy: from data acquisition and training to inference and experiment comparison, with capabilities including multimodal dataset support, integrated vector databases for similarity search, and interactive two- and three-dimensional latent-space exploration for unsupervised discovery. We demonstrate Hyrax's versatility through five representative applications on real survey data: (i) unsupervised representation learning on $\sim 4\times10^5$ Rubin Legacy Survey of Space and Time (LSST) Data Preview 1 (DP1) galaxies, surfacing new merger and low-surface-brightness candidates missing from reference Euclid and Dark Energy Survey catalogs, while also isolating imaging artifacts -- all without labeled training data; (ii) hybrid density-based clustering for identifying cluster-scale gravitational lens candidates in DP1 data; (iii) multimodal early-time transient classification in the Zwicky Transient Facility leveraging light curves, spectra, images, and metadata; (iv) supervised false-positive filtering in shift-and-stack searches for distant solar system objects in the Dark Energy Camera Ecliptic Exploration Project survey; and (v) supervised detection of semi-resolved dwarf galaxies in Hyper Suprime-Cam and LSST-like imaging using synthetic source injection. Together, these results demonstrate that Hyrax provides astronomy-specific ML infrastructure that enables systematic discovery and rapid methodological iteration across next-generation astronomical surveys.

astro-ph.IM↗

arXiv2026

EMBER: Machine-Learning Detection of Modulated Ion Acoustic Waves and Associated Core-Electron Heating in the Solar Wind with Parker Solar Probe

Modulated ion acoustic waves (IAWs) -- including triggered ion acoustic waves (TIAWs) and frequency-dispersed ion acoustic waves (FDIAWs) -- are increasingly recognized as efficient drivers of electron heating in the solar wind through nonlinear wave-particle interactions. Identification of these events in the Parker Solar Probe (PSP) FIELDS burst-mode archive has so far relied on expert visual inspection and does not scale to the full mission. We present EMBER (Electron heating from Modulated Burst-mode Event Recognition), an open-source pipeline that converts PSP FIELDS Digital Burst Memory (DBM) voltage bursts into log-scaled Fourier spectrograms and applies a multi-detector, background-only anomaly detection suite. The suite combines physics-motivated detectors, classical outlier detectors, and deep learning detectors. The EMBER ensemble recovers 93% of the anomalous events at 1% FAR (1 false positive per 100 held-out backgrounds). Coincident SWEAP/SPAN diagnostics show that flagged intervals exhibit core perpendicular electron temperatures above the adiabatic cooling expectation and elevated Te/Ti, reproducing the preferential-heating phenomenology established by prior manual studies without any use of electron temperatures in the detection step.

astro-ph.SR↗

arXiv2026

The ZTF-ULTRASAT experiment: Characterizing the non-transients in ULTRASAT's high cadence survey

The forthcoming launch of the Ultraviolet Transient Astronomy Satellite (ULTRASAT) will transform our understanding of the transient ultraviolet sky by increasing our ability to identify transients due to its unprecedented 204 deg2 field of view. While rapid (extragalactic) transients are a priority science area for the mission, flaring stars and AGN can often contaminate searches for such objects. To prepare for these challenges, the Zwicky Transient Facility (ZTF)-ULTRASAT experiment observed five fields at high cadence over three nights, in close proximity to ULTRASAT's three northern high-cadence fields. A real-time filter identified seven transient candidates, of which five were persistent variable sources and two were spurious. Periods and amplitudes derived from the ZTF Source Classification Project (SCoPe) showed that three candidates were RR Lyrae stars with short periods and high amplitudes, while the remaining two displayed flaring behavior. We demonstrate that short-timescale, high-amplitude variables can systematically mimic transient alerts in high-cadence UV surveys, and we provide a concrete strategy to this contamination using pre-existing machine learning catalogs.

astro-ph.SR↗

arXiv2026

Long GRB 250916A: an Off-axis Powerlaw Jet with Thermal Cocoon

Some gamma-ray bursts (GRBs) exhibit precursor emission episodes preceding the main emission, with a quiescent period in between. The properties of the precursor emission and the duration of the quiescent interval are related to the central engine activity and jet formation processes, thus providing insights into the physics of GRBs. We present a comprehensive analysis of the prompt emission and multi-wavelength afterglow of GRB 250916A. Using detailed afterglow modeling, we find that the broadband data are best described by a powerlaw structured jet with a relatively narrow core ($θ_c \approx 0.8^\circ$), viewed moderately off-axis at a viewing angle $θ_v \approx 2.7^\circ$. The isotropic-equivalent kinetic energy of the jet ($E_{k,iso} \approx 2.4 \times 10^{54}$ erg) is on the higher side for typical GRBs. The precursor emission is well described by a blackbody spectrum with a temperature of kT $\approx$ 13.2 keV and is separated from the main emission by a long quiescent interval of 150 s. Put together, our results indicate that the precursor is likely to be a shock breakout from a cocoon formed by the interaction of the relativistic jet with the progenitor star. The resulting cocoon pressure and shock collimation naturally lead to the launch of a narrowly collimated jet, consistent with the jet geometry inferred from afterglow observations. The long quiescent interval may imply the central engine turn-off in addition to the effect of the off-axis geometry.

astro-ph.HE↗

arXiv2026

Pre-training vision models for the classification of alerts from wide-field time-domain surveys

Modern wide-field time-domain surveys facilitate the study of transient, variable and moving phenomena by conducting image differencing and relaying alerts to their communities. Machine learning tools have been used on data from these surveys and their precursors for more than a decade, and convolutional neural networks (CNNs), which make predictions directly from input images, saw particularly broad adoption through the 2010s. Since then, continually rapid advances in computer vision have transformed the standard practices around using such models. It is now commonplace to use standardized architectures pre-trained on large corpora of everyday images (e.g., ImageNet). In contrast, time-domain astronomy studies still typically design custom CNN architectures and train them from scratch. Here, we explore the effects of adopting various pre-training regimens and standardized model architectures on the performance of alert classification. We find that the resulting models match or outperform a custom, specialized CNN like what is typically used for filtering alerts. Moreover, our results show that pre-training on galaxy images from Galaxy Zoo tends to yield better performance than pre-training on ImageNet or training from scratch. We observe that the design of standardized architectures are much better optimized than the custom CNN baseline, requiring significantly less time and memory for inference despite having more trainable parameters. On the eve of the Legacy Survey of Space and Time and other image-differencing surveys, these findings advocate for a paradigm shift in the creation of vision models for alerts, demonstrating that greater performance and efficiency, in time and in data, can be achieved by adopting the latest practices from the computer vision field.

astro-ph.IM↗

arXiv2026

Beyond Gaussian Assumptions: A new robust statistical framework for gravitational-wave data analysis

Many traditional algorithms applied in gravitational-wave astronomy rely on the assumption of Gaussian noise, a condition not always met. To meet this need, this study extends a robust statistical framework, advancing previous work on heavy-tailed likelihoods, that adapts the hyperbolic likelihood method for full frequency domain applications. The framework is designed to maintain high performance under ideal conditions while improving robustness against non-Gaussian noise and outliers in real-world data. We demonstrate the efficacy of this approach through two key case studies. The first case study analyzes a massive black hole binary merger in simulated Laser Interferometer Space Antenna (LISA) data with Gaussian noise, showing that the extended hyperbolic likelihood method performs comparably to the more commonly used Whittle likelihood. The second case study examines a stellar-mass black hole binary merger using real ground-based gravitational-wave data containing non-Gaussian noise or overlapping signals, where our framework exhibits increased robustness and yields more accurate parameter estimations. Our results show that the hyperbolic likelihood better captures the true noise distribution, providing a flexible and physically motivated alternative for GW data analysis across current and future detectors.

gr-qc↗

arXiv2025

Likelihood-free inference for gravitational-wave data analysis and public alerts

Rapid and reliable detection and dissemination of source parameter estimation data products from gravitational-wave events, especially sky localization, is critical for maximizing the potential of multi-messenger astronomy. Machine learning based detection and parameter estimation algorithms are emerging as production ready alternatives to traditional approaches. Here, we report validation studies of AMPLFI, a likelihood-free inference solution to low-latency parameter estimation of binary black holes. We use simulated signals added into data from the LIGO-Virgo-KAGRA's (LVK's) third observing run (O3) to compare sky localization performance with BAYESTAR, the algorithm currently in production for rapid sky localization of candidates from matched-filter pipelines. We demonstrate sky localization performance, measured by searched area and volume, to be equivalent with BAYESTAR. We show accurate reconstruction of source parameters with uncertainties for use distributing low-latency coarse-grained chirp mass information. In addition, we analyze several candidate events reported by the LVK in the third gravitational-wave transient catalog (GWTC-3) and show consistency with the LVK's analysis. Altogether, we demonstrate AMPLFI's ability to produce data products for low-latency public alerts.

gr-qc↗

arXiv2025

Applying multimodal learning to Classify transient Detections Early (AppleCiDEr) I: Data set, methods, and infrastructure

Modern time-domain surveys like the Zwicky Transient Facility (ZTF) and the Legacy Survey of Space and Time (LSST) generate hundreds of thousands to millions of alerts, demanding automatic, unified classification of transients and variable stars for efficient follow-up. We present AppleCiDEr (Applying Multimodal Learning to Classify Transient Detections Early), a novel framework that integrates four key data modalities (photometry, image cutouts, metadata, and spectra) to overcome limitations of single-modality classification approaches. Our architecture introduces (i) two transformer encoders for photometry, (ii) a multimodal convolutional neural network (CNN) with domain-specialized metadata towers and Mixture-of-Experts fusion for combining metadata and images, and (iii) a CNN for spectra classification. Training on ~ 30,000 real ZTF alerts, AppleCiDEr achieves high accuracy, allowing early identification and suggesting follow-up for rare transient spectra. The system provides the first unified framework for both transient and variable star classification using real observational data, with seamless integration into brokering pipelines, demonstrating readiness for the LSST era.

astro-ph.IM↗

arXiv2025

LIGO/Virgo/KAGRA neutron star merger candidate S250206dm: Zwicky Transient Facility observations

We present the searches conducted with the Zwicky Transient Facility (ZTF) in response to S250206dm, a bona fide event with a false alarm rate of one in 25 years, detected by the International Gravitational Wave Network (IGWN). Although the event is significant, the nature of the compact objects involved remains unclear, with at least one likely neutron star. ZTF covered 68% of the localization region, though we did not identify any likely optical counterpart. We describe the ZTF strategy, potential candidates, and the observations that helped rule out candidates, including sources circulated by other collaborations. Similar to Ahumada et al. 2024, we perform a frequentist analysis, using simsurvey, as well as Bayesian analysis, using nimbus, to quantify the efficiency of our searches. We find that, given the nominal distance to this event of 373$\pm$104 Mpc, our efficiencies are above 10% for KNe brighter than $-17.5$ absolute magnitude. Assuming the optical counterpart known as kilonova (KN) lies within the ZTF footprint, our limits constrain the brightest end of the KN parameter space. Through dedicated radiative transfer simulations of KNe from binary neutron star (BNS) and black hole-neutron star (BHNS) mergers, we exclude parts of the BNS KN parameter space. Up to 35% of the models with high wind ejecta mass ($M_{\rm wind} \approx 0.13$ M$_{\odot}$) are ruled out when viewed face-on ($\cosθ_{\rm obs} = 1.0$). Finally, we present a joint analysis using the combined coverage from ZTF and the Gravitational Wave Multimessenger Dark Energy Camera Survey (GW-MMADS). The joint observations cover 73% of the localization region, and the combined efficiency has a stronger impact on rising and slowly fading models, allowing us to rule out 55% of the high-mass KN models viewed face-on.

astro-ph.HE↗

arXiv2024

Characterization of non-Gaussian stochastic signals with heavier-tailed likelihoods

Future Gravitational Wave observatories will give us the opportunity to search for stochastic signals of astrophysical, or even cosmological origins. However, parameter estimation and search will be challenging, mostly due to the overlap of multiple signal components, as well as the potentially partially unknown properties of the instrumental noise. In this work, we propose a robust statistical framework based on heavier-tailed likelihoods for the characterization of stochastic gravitational-wave signals. In particular, we use the symmetric hyperbolic likelihood, which allows us to probe the signal spectral properties and simultaneously test for any departures from Gaussianity. We demonstrate this methodology with synthetic data from the future LISA mission, where we estimate the potential non-Gaussianities induced by the unresolved Ultra Compact Galactic Binaries.

gr-qc↗

arXiv2023

Exploring the Potential for Detecting Rotational Instabilities in Binary Neutron Star Merger Remnants with Gravitational Wave Detectors

We explore the potential for detecting rotational instabilities in the post-merger phase of binary neutron star mergers using different network configurations of upgraded and next-generation gravitational wave detectors. Our study employs numerically generated post-merger waveforms, which reveal the re-excitation of the $l=m=2$ $f$-mode at a time of $O(10{\rm})$ms after merger. We evaluate the detectability of these signals by injecting them into colored Gaussian noise and performing a reconstruction as a sum of wavelets using Bayesian inference. Computing the overlap between the reconstructed and injected signal, restricted to the instability part of the post-merger phase, we find that one could infer the presence of rotational instabilities with a network of planned 3rd-generation detectors, depending on the total mass and distance to the source. For a recently suggested high-frequency detector design, we find that the instability part would be detectable even at 200 Mpc, significantly increasing the anticipated detection rate. For a network consisting of the existing HLV detectors, but upgraded to twice the A+ sensitivity, we confirm that the peak frequency of the whole post-merger gravitational-wave emission could be detectable with a network signal-to-noise ratio of 8 at a distance of 40Mpc.

gr-qc↗

arXiv2023

Heavy-tailed likelihoods for robustness against data outliers: Applications to the analysis of gravitational wave data

In recent years, the field of Gravitational Wave Astronomy has flourished. With the advent of more sophisticated ground-based detectors and space-based observatories, it is anticipated that Gravitational Wave events will be detected at a much higher rate in the near future. One of the future data analysis challenges is performing robust statistical inference in the presence of detector noise transients or non-stationarities, as well as in the presence of stochastic Gravitational Wave signals of possible astrophysical and/or cosmological origin. The incomplete knowledge of the total noise of the observatory can introduce challenges in parameter estimation of detected sources. In this work, we propose a heavy-tailed, Hyperbolic likelihood, based on the Generalized Hyperbolic distribution. With the Hyperbolic likelihood we obtain a robust data analysis framework against data outliers, noise non-stationarities, and possible inaccurate modeling of the noise power spectral density. We apply this methodology to examples drawn from gravitational wave astronomy, and in particular to synthetic data sets from the planned LISA mission.

gr-qc↗