arXiv subjects

Ryan Missel

Publications and source records attributed to Ryan Missel.

9 recordsLinked to original sources

arXiv2026

Single-pulse reanalysis of the 2024 Vela glitch and new observations of PSR~J0437$-$4715 and PSR~J1644$-$4559

The Pulsar Monitoring in Argentina (PuMA) collaboration systematically monitors southern glitching pulsars, maintaining high-cadence single-pulse records of the Vela pulsar. We present a pulse-per-pulse reanalysis of the 2024 major glitch of Vela (PSR~J0835$-$4510) with the 400~MHz-bandwidth ROACH backend of the Argentine Institute of Radioastronomy, and extend our machine-learning single-pulse pipeline---Isolation Forest outlier rejection, $\beta$-Variational-AutoEncoder denoising, and Self-Organizing-Map clustering---to new observations of PSR~J1644$-$4559 and the millisecond pulsar PSR~J0437$-$4715. For Vela, the 4- and 6-cluster decompositions of the eight days bracketing the glitch reproduce, with seven times the previous bandwidth, the behavior found with the narrow-band ETTUS receivers: higher-amplitude clusters peak earlier in phase, are narrower, more skewed, and less populated. With the glitch jump and its two exponential recovery terms included in the timing solution, the mean profile is stable to 1\% across all eight days (width change $-0.5\pm0.9$\% from pre- to post-glitch); omitting the recovery terms would mimic a post-glitch broadening of up to 55\% through a folding-frequency error at the $10^{-7}$ level. The clusters of PSR~J1644$-$4559 differ almost exclusively in amplitude, as expected for a scattering-dominated profile. For PSR~J0437$-$4715, retaining the 10\% of pulses with the highest peak-dominance score doubles the signal-to-noise ratio, and a five-cluster decomposition yields narrow, phase-ordered groups a factor $3.6\pm0.4$ narrower than the average profile, suggesting a $\sim$3.5-fold improvement in cluster-based timing precision for this pulsar-timing-array target, to be confirmed in a follow-up paper.

astro-ph.HE↗

arXiv2026

CoMetaPNS: Continually Meta-learning Personalized Neural Surrogates for Cardiac Electrophysiology Simulations

Personalized virtual heart simulations face challenges in model personalization and computational cost. While neural surrogates offer state-of-the-art solutions, they typically address either efficient personalization or training generalizable models. Recent work reframes this by learning the process of personalizing a surrogate using limited subject-specific context data, through few-shot generative modeling with set-conditioned surrogates and meta-learned amortized inference. These methods, however, assume a static and diverse training distribution with known task identifiers. When new data becomes available, they require costly retraining with all prior data to avoid catastrophic forgetting - a phenomena where the model forgets earlier tasks when trained on new ones. This is a major limitation in clinical settings where often unlabeled data arrives sequentially and full retraining is infeasible. This paper presents a new continual meta-learning framework to achieve personalized neural surrogates able to not only continually integrate information but also identify whether incoming data stems from a known or unknown dynamics source. By leveraging a continual Bayesian Gaussian Mixture Model over a memory buffer, our framework can infer the identifiers and relationships of data over time - required for effective meta-learning. Empirical results on synthetic cardiac data demonstrate superior simulation forecasting, computational scalability, and resilience to catastrophic forgetting compared to existing baselines.

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arXiv2025

Study of the 2024 major Vela glitch at the Argentine Institute of Radioastronomy

We report here on new results of the systematic monitoring of southern glitching pulsars at the Argentine Institute of Radioastronomy. In particular, we study in this work the new major glitch in the Vela pulsar (PSR J0835$-$4510) that occurred on 2024 April 29. We aim to thoroughly characterise the rotational behaviour of the Vela pulsar around its last major glitch and investigate the statistical properties of its individual pulses around the glitch. We characterise the rotational behaviour of the pulsar around the glitch through the pulsar timing technique. We measured the glitch parameters by fitting timing residuals to the data collected during the days surrounding the event. In addition, we study Vela individual pulses during the days of observation just before and after the glitch. We selected nine days of observations around the major glitch on 2024 April 29 and studied their statistical properties with the Self-Organizing Maps (SOM) technique. We used Variational AutoEncoder (VAE) reconstruction of the pulses to separate them clearly from the noise. We obtain a precise timing solution for the glitch. We find two recovery terms of $\sim 3~\mathrm{days}$ and $\sim 17~\mathrm{days}$. We find a correlation of high amplitude with narrower pulses while not finding notable qualitative systematic changes before and after the glitch.

astro-ph.HE↗

arXiv2024

HyPer-EP: Meta-Learning Hybrid Personalized Models for Cardiac Electrophysiology

Personalized virtual heart models have demonstrated increasing potential for clinical use, although the estimation of their parameters given patient-specific data remain a challenge. Traditional physics-based modeling approaches are computationally costly and often neglect the inherent structural errors in these models due to model simplifications and assumptions. Modern deep learning approaches, on the other hand, rely heavily on data supervision and lacks interpretability. In this paper, we present a novel hybrid modeling framework to describe a personalized cardiac digital twin as a combination of a physics-based known expression augmented by neural network modeling of its unknown gap to reality. We then present a novel meta-learning framework to enable the separate identification of both the physics-based and neural components in the hybrid model. We demonstrate the feasibility and generality of this hybrid modeling framework with two examples of instantiations and their proof-of-concept in synthetic experiments.

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arXiv2024

Unsupervised Learning of Hybrid Latent Dynamics: A Learn-to-Identify Framework

Modern applications increasingly require unsupervised learning of latent dynamics from high-dimensional time-series. This presents a significant challenge of identifiability: many abstract latent representations may reconstruct observations, yet do they guarantee an adequate identification of the governing dynamics? This paper investigates this challenge from two angles: the use of physics inductive bias specific to the data being modeled, and a learn-to-identify strategy that separates forecasting objectives from the data used for the identification. We combine these two strategies in a novel framework for unsupervised meta-learning of hybrid latent dynamics (Meta-HyLaD) with: 1) a latent dynamic function that hybridize known mathematical expressions of prior physics with neural functions describing its unknown errors, and 2) a meta-learning formulation to learn to separately identify both components of the hybrid dynamics. Through extensive experiments on five physics and one biomedical systems, we provide strong evidence for the benefits of Meta-HyLaD to integrate rich prior knowledge while identifying their gap to observed data.

cs.LG↗

arXiv2022

First results of the glitching pulsars monitoring program at the Argentine Institute of Radioastronomy

We report here on the first results of a systematic monitoring of southern glitching pulsars at the Argentine Institute of Radioastronomy that started in the year 2019. We detected a major glitch in the Vela pulsar (PSR J0835$-$4510) and two small-glitches in PSR J1048$-$5832. For each glitch, we present the measurement of glitch parameters by fitting timing residuals. We then make an individual pulses study of Vela in observations before and after the glitch. We selected 6 days of observations around the major glitch on 2021 July 22 and study their statistical properties with machine learning techniques. We use Variational AutoEncoder (VAE) reconstruction of the pulses to separate them clearly from the noise. We perform a study with Self-Organizing Maps (SOM) clustering techniques to search for unusual behavior of the clusters during the days around the glitch not finding notable qualitative changes. We have also detected and confirm recent glitches in PSR J0742$-$2822 and PSR J1740$-$3015.

astro-ph.HE↗

arXiv2022

Few-shot Generation of Personalized Neural Surrogates for Cardiac Simulation via Bayesian Meta-Learning

Clinical adoption of personalized virtual heart simulations faces challenges in model personalization and expensive computation. While an ideal solution is an efficient neural surrogate that at the same time is personalized to an individual subject, the state-of-the-art is either concerned with personalizing an expensive simulation model, or learning an efficient yet generic surrogate. This paper presents a completely new concept to achieve personalized neural surrogates in a single coherent framework of meta-learning (metaPNS). Instead of learning a single neural surrogate, we pursue the process of learning a personalized neural surrogate using a small amount of context data from a subject, in a novel formulation of few-shot generative modeling underpinned by: 1) a set-conditioned neural surrogate for cardiac simulation that, conditioned on subject-specific context data, learns to generate query simulations not included in the context set, and 2) a meta-model of amortized variational inference that learns to condition the neural surrogate via simple feed-forward embedding of context data. As test time, metaPNS delivers a personalized neural surrogate by fast feed-forward embedding of a small and flexible number of data available from an individual, achieving -- for the first time -- personalization and surrogate construction for expensive simulations in one end-to-end learning framework. Synthetic and real-data experiments demonstrated that metaPNS was able to improve personalization and predictive accuracy in comparison to conventionally-optimized cardiac simulation models, at a fraction of computation.

cs.LG↗

arXiv2022

Neural State-Space Modeling with Latent Causal-Effect Disentanglement

Despite substantial progress in deep learning approaches to time-series reconstruction, no existing methods are designed to uncover local activities with minute signal strength due to their negligible contribution to the optimization loss. Such local activities however can signify important abnormal events in physiological systems, such as an extra foci triggering an abnormal propagation of electrical waves in the heart. We discuss a novel technique for reconstructing such local activity that, while small in signal strength, is the cause of subsequent global activities that have larger signal strength. Our central innovation is to approach this by explicitly modeling and disentangling how the latent state of a system is influenced by potential hidden internal interventions. In a novel neural formulation of state-space models (SSMs), we first introduce causal-effect modeling of the latent dynamics via a system of interacting neural ODEs that separately describes 1) the continuous-time dynamics of the internal intervention, and 2) its effect on the trajectory of the system's native state. Because the intervention can not be directly observed but have to be disentangled from the observed subsequent effect, we integrate knowledge of the native intervention-free dynamics of a system, and infer the hidden intervention by assuming it to be responsible for differences observed between the actual and hypothetical intervention-free dynamics. We demonstrated a proof-of-concept of the presented framework on reconstructing ectopic foci disrupting the course of normal cardiac electrical propagation from remote observations.

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arXiv2021

Vela Pulsar: Single Pulses Analysis with Machine Learning Techniques

We study individual pulses of Vela (PSR\ B0833-45\,/\,J0835-4510) from daily observations of over three hours (around 120,000 pulses per observation), performed simultaneously with the two radio telescopes at the Argentine Institute of Radioastronomy. We select 4 days of observations in January-March 2021 and study their statistical properties with machine learning techniques. We first use density based DBSCAN clustering techniques, associating pulses mainly by amplitudes, and find a correlation between higher amplitudes and earlier arrival times. We also find a weaker (polarization dependent) correlation with the mean width of the pulses. We identify clusters of the so-called mini-giant pulses, with $\sim10\times$ the average pulse amplitude. We then perform an independent study, with Self-Organizing Maps (SOM) clustering techniques. We use Variational AutoEncoder (VAE) reconstruction of the pulses to separate them clearly from the noise and select one of the days of observation to train VAE and apply it to thre rest of the observations. We use SOM to determine 4 clusters of pulses per day per radio telescope and conclude that our main results are robust and self-consistent. These results support models for emitting regions at different heights (separated each by roughly a hundred km) in the pulsar magnetosphere. We also model the pulses amplitude distribution with interstellar scintillation patterns at the inter-pulses time-scale finding a characterizing exponent $n_{\mathrm{ISS}}\sim7-10$. In the appendices we discuss independent checks of hardware systematics with the simultaneous use of the two radio telescopes in different one-polarization / two-polarizations configurations. We also provide a detailed analysis of the processes of radio-interferences cleaning and individual pulse folding.

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