arXiv subjects

Cecilia Garraffo

Publications and source records attributed to Cecilia Garraffo.

At least 19 recordsLinked to original sources

arXiv2026

The Cross-Survey Decade: A Call to Action

By 2027, three flagship wide-field surveys will be operating simultaneously from ground and space, observing overlapping sky and representing more than $6 billion in US and European public investment. Together they will produce overlapping petabyte-scale datasets across thousands of square degrees. This is a different class of challenge: the observations are no longer the bottleneck; realizing their joint scientific return now depends on shared computational infrastructure and coordination. Decades of community studies show that combining these datasets does more than improve precision. For science ranging from weak lensing to transient discovery and Galactic-plane astronomy, joint processing and analysis can unlock capabilities no single survey provides alone. Yet the required infrastructure -- joint pixel-level processing, cross-calibration and validation, interoperable data access, and the people to build and sustain it -- falls outside any single mission or institution's mandate. We issue a call to action for cross-survey science infrastructure, built around four pillars: (1) joint pixel-level processing and validation; (2) an AI-ready data substrate for scientific foundation models; (3) standardized, interoperable data access across surveys, democratizing participation in astrophysical discovery; and (4) dedicated personnel and career pathways. We outline concrete steps for policymakers, agencies, observatories, universities, the research community, and philanthropy, and argue that the moment to act is now, while foundational technical choices can still be aligned at a fraction of the cost of reconciling them later.

astro-ph.IM↗

arXiv2026

Neural-Spectral Discovery of Rotating Black Holes Beyond General Relativity

Finding rotating black hole solutions in higher-curvature theories of gravity is a problem of fundamental importance. Virtually every approach to reconcile gravity with quantum mechanics predicts corrections to the Einstein-Hilbert action, yet no systematic solution-generating method exists for the stationary sector. We close this gap with {\sc Akribeia}, a novel hybrid framework that pairs physics-informed neural networks with a pseudo-spectral refinement step, yielding certified neural-field rotating black hole solutions -- continuous, globally defined functions, parametric in the coupling constants -- whose residuals against the field equations are verified to extreme precision. We apply the method to theories quadratic and cubic in the curvature and construct, for the first time, families of rotating black holes featuring multiple non-vanishing angular momenta, parametric in the new coupling constants. After validating against previously known five-dimensional spacetimes, we present new solutions in scenarios leading to a highly non-linear/non-perturbative coupled system of ordinary differential equations. Our method can be systematically adapted to other setups involving partial differential equations as well.

gr-qc↗

arXiv2026

The Chandra-Gaia Catalog of Counterparts: Resolving ambiguous Gaia matches to X-ray sources in the Chandra Source Catalog using Machine Learning

We present a framework to cross-match sources from the Chandra Source Catalog (CSC v2.1) with optical sources from Gaia Data Release 3. Unlike purely spatial approaches, we use source properties such as magnitudes, colors, and distances to identify true counterparts, detect chance coincidences, and resolve ambiguities when multiple plausible candidates exist. We define a training set of high-confidence matches using NWAY, a Bayesian cross-matching framework that accounts for positional errors and source densities. We train a gradient-boosted classifier (LightGBM) on a variety of features from both catalogs. Of the ~$254$k unique X-ray sources, we find counterparts for ~$113$k sources, of which plausible multiple counterparts are found for ~$7$k. We find no counterparts for ~$20$k sources for which separation-based cross-matching does find a match, and attribute half of these to chance coincidences. We validate the pipeline on the Chandra Orion Ultradeep Project (COUP), where the machine-learning matches reproduce 95% of NWAY cross-matches without using any positional information. We release a catalog of the ~$113$k Chandra-Gaia counterparts, together with ~$7$k alternative matches and ~$20$k ambiguous NWAY associations, supporting future population studies of sources detectable by both Chandra and Gaia. We discuss limitations and provide a generalization of the framework that is applicable in other cross-matching scenarios.

astro-ph.IM↗

arXiv2026

Plume Segmentation from MethaneSAT with Cross-Sensor Transfer Learning and Physics-Informed Postprocessing

Automated detection and masking of individual methane plumes from satellite imagery is important for operational emission attribution and quantification. We present a machine learning framework for plume detection from MethaneSAT retrieved column-averaged dry-air mole fractions of methane. We address two core challenges: the scarcity of labeled MethaneSAT data and the need for inference reliability across diverse atmospheric and surface conditions. We first demonstrate that Mask R-CNN with a ResNet-50 backbone outperforms U-Net semantic segmentation on both MethaneAIR (an airborne version of MethaneSAT) and MethaneSAT data, with pixel-level F1 score gains of 10.49 and 5.48 respectively. To address MethaneSAT data scarcity, we evaluate three cross-sensor transfer strategies leveraging MethaneAIR flights and synthetic plumes. Mask R-CNN with ResNet-50 fine-tuned from MethaneAIR pre-trained weights is the most effective strategy, achieving instance-level precision of 0.60 and a near-perfect recall of 0.98 at the baseline operating point. A physics-informed post-processing pipeline converts detections into two operationally distinct modes. The first is a high-sensitivity mode that applies morphological filtering and proximity-based merging for comprehensive emission screening, achieving precision of 0.71 and recall of 0.94. The second is a high-precision mode that additionally applies a distribution-based classifier for confident source attribution, achieving precision of 0.92 and recall of 0.70. Manual review of detections classified as false positives against our wavelet-based ground truth labels reveals that a meaningful fraction of cases correspond to real methane enhancements excluded by conservative labeling criteria, indicating that precision values reported are lower bounds on true detection performance... Our data and code are available at: https://doi.org/10.7910/DVN/FR959H

cs.CV↗

arXiv2026

Pushing the Limit of Asteroseismic Detection for Cool Dwarfs using TESS and Deep Learning

Asteroseismology provides a powerful probe of stellar interiors by detecting stellar oscillations, including solar-like oscillations, which are stochastically excited by near-surface convection. While thousands of solar-like oscillators have been identified in evolved stars, only a limited number of main-sequence cool dwarfs have confirmed oscillations due to the low amplitudes of their signals. In this work, we train a convolutional autoencoder on TESS two-minute light curves to automatically identify solar-like oscillation features in cool dwarf main sequence and sub-giant stars. Using catalogs of confirmed oscillators for training and validation, our network achieves a classification accuracy of 99.8% on the test set, along with Precision of 0.945, Recall of 0.998, and F1 Score of 0.971. From the Asteroseismic Target List, our model identifies 3463 potential solar-like oscillators (probability greater than 0.5). After further analysis, we find a list of 24 candidate stars that have the potential to exhibit solar-like oscillations. Notably, several of these candidates occupy regions of the color-magnitude diagram that are accessible only through more resource-intensive radial velocity observations, thereby has the potential of extending the detection frontier of TESS-based asteroseismology. Our candidate catalog provides a valuable foundation for follow-up efforts aimed at expanding the sample of cool-dwarf solar-like oscillators. This will ultimately improve our understanding of stellar structure and evolution across the lower main sequence and strengthen the evidence for using deep learning techniques to study stellar light curves.

astro-ph.SR↗

arXiv2026

The Future of Artificial Intelligence and the Mathematical and Physical Sciences (AI+MPS)

This community paper developed out of the NSF Workshop on the Future of Artificial Intelligence (AI) and the Mathematical and Physics Sciences (MPS), which was held in March 2025 with the goal of understanding how the MPS domains (Astronomy, Chemistry, Materials Research, Mathematical Sciences, and Physics) can best capitalize on, and contribute to, the future of AI. We present here a summary and snapshot of the MPS community's perspective, as of Spring/Summer 2025, in a rapidly developing field. The link between AI and MPS is becoming increasingly inextricable; now is a crucial moment to strengthen the link between AI and Science by pursuing a strategy that proactively and thoughtfully leverages the potential of AI for scientific discovery and optimizes opportunities to impact the development of AI by applying concepts from fundamental science. To achieve this, we propose activities and strategic priorities that: (1) enable AI+MPS research in both directions; (2) build up an interdisciplinary community of AI+MPS researchers; and (3) foster education and workforce development in AI for MPS researchers and students. We conclude with a summary of suggested priorities for funding agencies, educational institutions, and individual researchers to help position the MPS community to be a leader in, and take full advantage of, the transformative potential of AI+MPS.

cs.AI↗

arXiv2026

Augmenting representations with scientific papers

Astronomers have acquired vast repositories of multimodal data, including images, spectra, and time series, complemented by decades of literature that analyzes astrophysical sources. Still, these data sources are rarely systematically integrated. This work introduces a contrastive learning framework designed to align X-ray spectra with domain knowledge extracted from scientific literature, facilitating the development of shared multimodal representations. Establishing this connection is inherently complex, as scientific texts encompass a broader and more diverse physical context than spectra. We propose a contrastive pipeline that achieves a 20% Recall@1% when retrieving texts from spectra, proving that a meaningful alignment between these modalities is not only possible but capable of accelerating the interpretation of rare or poorly understood sources. Furthermore, the resulting shared latent space effectively encodes physically significant information. By fusing spectral and textual data, we improve the estimation of 20 physical variables by 16-18% over unimodal spectral baselines. Our results indicate that a Mixture of Experts (MoE) strategy, which leverages both unimodal and shared representations, yields superior performance. Finally, outlier analysis within the multimodal latent space identifies high-priority targets for follow-up investigation, including a candidate pulsating ULX (PULX) and a gravitational lens system. Importantly, this framework can be extended to other scientific domains where aligning observational data with existing literature is possible.

cs.LG↗

arXiv2026

Deep Learning for Clouds and Cloud Shadow Segmentation in Methane Satellite and Airborne Imaging Spectroscopy

Effective cloud and cloud shadow detection is a critical prerequisite for accurate retrieval of concentrations of atmospheric methane (CH4) or other trace gases in hyperspectral remote sensing. This challenge is especially pertinent for MethaneSAT, a satellite mission launched in March 2024, to fill a significant data gap in terms of resolution, precision and swath between coarse-resolution global mappers and fine-scale point-source imagers of methane, and for its airborne companion mission, MethaneAIR. MethaneSAT delivers hyperspectral data at an intermediate spatial resolution (approx. 100 x 400, m), whereas MethaneAIR provides even finer resolution (approx. 25 m), enabling the development of highly detailed maps of concentrations that enable quantification of both the sources and rates of emissions. In this study, we use machine learning methods to address the cloud and cloud shadow detection problem for sensors with these high spatial resolutions. Cloud and cloud shadows in remote sensing data need to be effectively screened out as they bias methane retrievals in remote sensing imagery and impact the quantification of emissions. We deploy and evaluate conventional techniques-including Iterative Logistic Regression (ILR) and Multilayer Perceptron (MLP)-with advanced deep learning architectures, namely U-Net and a Spectral Channel Attention Network (SCAN) method. Our results show that conventional methods struggle with spatial coherence and boundary definition, affecting the detection of clouds and cloud shadows. Deep learning models substantially improve detection quality: U-Net performs best in preserving spatial structure, while SCAN excels at capturing fine boundary details... Our data and code is publicly available at: https://doi.org/10.7910/DVN/IKLZOJ

cs.CV↗

arXiv2026

Variational Autoencoder with Normalizing flow for X-ray spectral fitting

Black hole X-ray binaries (BHBs) can be studied with spectral fitting to provide physical constraints on accretion in extreme gravitational environments. Traditional methods of spectral fitting such as Markov Chain Monte Carlo (MCMC) face limitations due to computational times. We introduce a probabilistic model, utilizing a variational autoencoder with a normalizing flow, trained to adopt a physical latent space. This neural network produces predictions for spectral-model parameters as well as their full probability distributions. Our implementations result in a significant improvement in spectral reconstructions over a previous deterministic model while performing three orders of magnitude faster than traditional methods.

cs.LG↗

arXiv2025

Hyperspectral Variational Autoencoders for Joint Data Compression and Component Extraction

Geostationary hyperspectral satellites generate terabytes of data daily, creating critical challenges for storage, transmission, and distribution to the scientific community. We present a variational autoencoder (VAE) approach that achieves x514 compression of NASA's TEMPO satellite hyperspectral observations (1028 channels, 290-490nm) with reconstruction errors 1-2 orders of magnitude below the signal across all wavelengths. This dramatic data volume reduction enables efficient archival and sharing of satellite observations while preserving spectral fidelity. Beyond compression, we investigate to what extent atmospheric information is retained in the compressed latent space by training linear and nonlinear probes to extract Level-2 products (NO2, O3, HCHO, cloud fraction). Cloud fraction and total ozone achieve strong extraction performance (R^2 = 0.93 and 0.81 respectively), though these represent relatively straightforward retrievals given their distinct spectral signatures. In contrast, tropospheric trace gases pose genuine challenges for extraction (NO2 R^2 = 0.20, HCHO R^2 = 0.51) reflecting their weaker signals and complex atmospheric interactions. Critically, we find the VAE encodes atmospheric information in a semi-linear manner - nonlinear probes substantially outperform linear ones - and that explicit latent supervision during training provides minimal improvement, revealing fundamental encoding challenges for certain products. This work demonstrates that neural compression can dramatically reduce hyperspectral data volumes while preserving key atmospheric signals, addressing a critical bottleneck for next-generation Earth observation systems. Code - https://github.com/cfpark00/Hyperspectral-VAE

cs.LG↗

arXiv2025

Hubble reveals complex multi-scale structure in the edge-on protoplanetary disk IRAS 23077+6707

We present high-resolution ($\lesssim 0.1''$) Hubble Space Telescope (HST)/Wide Field Camera 3 (WFC3) imaging of the near edge-on ($i{\sim}80^\circ$) protoplanetary disk IRAS 23077+6707 ("Dracula's Chivito") obtained across six broadband filters spanning $0.4-1.6\,μ$m. These observations unveil the scattered light from this unusually large disk (${\sim}14''$, or ${\sim}4200\,\mathrm{au}$ at $300\,\mathrm{pc}$) in remarkable detail, revealing a rich tapestry of substructures, including brightness asymmetries and signatures of dynamical activity. Extended filaments are detected extending ${\sim}10''$ from the northern edges of both nebulae, while no comparable southern features are observed. In addition to large-scale asymmetries, the disk exhibits prominent wispy features that extend well above the midplane and are visible in all filters, suggesting a complex, possibly turbulent outer disk atmosphere shaped by infall, dynamical stirring, or gravitational instability. The central dark lane narrows from optical to near-IR wavelengths, and high-resolution millimeter data reveal compact midplane emission. Although our radiative transfer simulations show that the current data cannot yet distinguish between dust settling and no-settling scenarios, they underscore the need for resolved mid-infrared observations of this unique system. IRAS 23077+6707 thus represents a rare and valuable laboratory for studying the vertical structure, asymmetries, and evolutionary state of protoplanetary disks.

astro-ph.EP↗

arXiv2025

Parameter Inference of Black Hole Images using Deep Learning in Visibility Space

Using very long baseline interferometry, the Event Horizon Telescope (EHT) collaboration has resolved the shadows of two supermassive black holes. Model comparison is traditionally performed in image space, where imaging algorithms introduce uncertainties in the recovered structure. Here, we develop a deep learning framework to perform parameter inference in visibility space, directly using the data measured by the interferometer without introducing potential errors and biases from image reconstruction. First, we train and validate our framework on synthetic data derived from general relativistic magnetohydrodynamics (GRMHD) simulations that vary in magnetic field state, spin, and $R_\mathrm{high}$. Applying these models to the real data obtained during the 2017 EHT campaign, and only considering total intensity, we do not derive meaningful constraints on either of these parameters. At present, our method is limited both by theoretical uncertainties in the GRMHD simulations and variation between snapshots of the same underlying physical model. However, we demonstrate that spin and $R_\mathrm{high}$ could be recovered using this framework through continuous monitoring of our sources, which mitigates variations due to turbulence. In future work, we anticipate that including spectral or polarimetric information will greatly improve the performance of this framework.

astro-ph.GA↗

arXiv2025

Unravelling the role of merger histories in the population of In situ stars: linking IllustrisTNG cosmological simulation to H3 survey

We undertake a comprehensive investigation into the distribution of in~situ stars within Milky Way-like galaxies, leveraging TNG50 simulations and comparing their predictions with data from the H3 survey. Our analysis reveals that 28% of galaxies demonstrate reasonable agreement with H3, while only 12% exhibit excellent alignment in their profiles, regardless of the specific spatial cut employed to define in~situ stars. To uncover the underlying factors contributing to deviations between TNG50 and H3 distributions, we scrutinise correlation coefficients among internal drivers(e.g., virial radius, star formation rate [SFR]) and merger-related parameters (such as the effective mass-ratio, mean distance, average redshift, total number of mergers, average spin-ratio and maximum spin alignment between merging galaxies). Notably, we identify significant correlations between deviations from observational data and key parameters such as the median slope of virial radius, mean SFR values, and the rate of SFR change across different redshift scans. Furthermore, positive correlations emerge between deviations from observational data and parameters related to galaxy mergers. We validate these correlations using the Random Forest Regression method. Our findings underscore the invaluable insights provided by the H3 survey in unravelling the cosmic history of galaxies akin to the Milky Way, thereby advancing our understanding of galactic evolution and shedding light on the formation and evolution of Milky Way-like galaxies in cosmological simulations.

astro-ph.GA↗

arXiv2025

Denoising Hamiltonian Network for Physical Reasoning

Machine learning frameworks for physical problems must capture and enforce physical constraints that preserve the structure of dynamical systems. Many existing approaches achieve this by integrating physical operators into neural networks. While these methods offer theoretical guarantees, they face two key limitations: (i) they primarily model local relations between adjacent time steps, overlooking longer-range or higher-level physical interactions, and (ii) they focus on forward simulation while neglecting broader physical reasoning tasks. We propose the Denoising Hamiltonian Network (DHN), a novel framework that generalizes Hamiltonian mechanics operators into more flexible neural operators. DHN captures non-local temporal relationships and mitigates numerical integration errors through a denoising mechanism. DHN also supports multi-system modeling with a global conditioning mechanism. We demonstrate its effectiveness and flexibility across three diverse physical reasoning tasks with distinct inputs and outputs.

cs.LG↗

arXiv2024

On Convective Turnover Times and Dynamos In Low-Mass Stars

The relationship between magnetic activity and Rossby number is one way through which stellar dynamos can be understood. Using measured rotation rates and X-ray to bolometric luminosity ratios of an ensemble of stars, we derive empirical convective turnover times based on recent observations and re-evaluate the X-ray activity-Rossby number relationship. In doing so, we find a sharp rise in the convective turnover time for stars in the mass range of $0.35-0.4\ \rm M_{\odot}$, associated with the onset of a fully convective internal stellar structure. Using $\texttt{MESA}$ stellar evolution models, we infer the location of dynamo action implied by the empirical convective turnover time. The empirical convective turnover time is found to be indicative of dynamo action deep within the convective envelope in stars with masses $0.1-1.2\ \rm M_{\odot}$, crossing the fully convective boundary. Our results corroborate past works suggesting that partially and fully convective stars follow the same activity-Rossby relation, possibly owing to similar dynamo mechanisms. Our stellar models also give insight into the dynamo mechanism. We find that empirically determined convective turnover times correlate with properties of the deep stellar interior. These findings are in agreement with global dynamo models that see a reservoir of magnetic flux accumulate deep in the convection zone before buoyantly rising to the surface.

astro-ph.SR↗

arXiv2024

Faster Bayesian inference with neural network bundles and new results for $f(R)$ models

In the last few years, there has been significant progress in the development of machine learning methods tailored to astrophysics and cosmology. We have recently applied one of these, namely, the neural network bundle method, to the cosmological scenario. Moreover, we showed that in some cases the computational times of the Bayesian inference process can be reduced. In this paper, we present an improvement to the neural network bundle method that results in a significant reduction of the computational times of the statistical analysis. The novel aspect consists of the use of the neural network bundle method to calculate the luminosity distance of type Ia supernovae, which is usually computed through an integral with numerical methods. In this work, we have applied this improvement to the Hu-Sawicki and Starobinsky $f(R)$ models. We also performed a statistical analysis with data from type Ia supernovae of the Pantheon+ compilation and cosmic chronometers. Another original aspect of this work is the different treatment we provide for the absolute magnitude of type Ia supernovae during the inference process, which results in different estimates of the distortion parameter than the ones obtained in the literature. We show that the statistical analyses carried out with our new method require lower computational times than the ones performed with both the numerical and the neural network method from our previous work. This reduction in time is more significant in the case of a difficult computational problem such as the ones addressed in this work.

astro-ph.CO↗

arXiv2024

Dracula's Chivito: discovery of a large edge-on protoplanetary disk with Pan-STARRS

We report the serendipitous discovery of a large edge-on protoplanetary disk associated with the infrared source IRAS 23077+6707. The disk's apparent size in the Pan-STARRS (PS1) images is ~11", making this one of the largest known disks on the sky. It is likely a young system, still surrounded by the envelope which is very faint but still visible in the PS1 images in the northern part (alternatively this structure could be filaments from the disk itself). We use the PS1 magnitudes and other available photometric data to construct the spectral energy distribution (SED) of the disk. An optical spectrum indicates that the obscured star is hot, most likely late A. We adopt a distance of 300 pc for this object based on Gaia DR3 extinctions. We model the system using the HOCHUNK3D radiative transfer software and find that the system is consistent with a hot star of effective temperature 8000 K surrounded by a disk of size 1650 AU and mass 0.2 M_solar at inclination 82 degrees.

astro-ph.SR↗

arXiv2024

Rapid Spectral Parameter Prediction for Black Hole X-Ray Binaries using Physicalised Autoencoders

Black hole X-ray binaries (BHBs) offer insights into extreme gravitational environments and the testing of general relativity. The X-ray spectrum collected by NICER offers valuable information on the properties and behaviour of BHBs through spectral fitting. However, traditional spectral fitting methods are slow and scale poorly with model complexity. This paper presents a new semi-supervised autoencoder neural network for parameter prediction and spectral reconstruction of BHBs, showing an improvement of up to a factor of 2,700 in speed while maintaining comparable accuracy. The approach maps the spectral features from the numerous outbursts catalogued by NICER and generalises them to new systems for efficient and accurate spectral fitting. The effectiveness of this approach is demonstrated in the spectral fitting of BHBs and holds promise for use in other areas of astronomy and physics for categorising large datasets.

astro-ph.IM↗