arXiv subjects

Logan Sizemore

Publications and source records attributed to Logan Sizemore.

5 recordsLinked to original sources

arXiv2026

Probabilistic neural network approach to determining parameters of eclipsing binaries

Eclipsing binaries provide one of the most direct mechanisms for measuring stellar properties such as mass and radius, but historically, determining these properties has been non-trivial and computationally prohibitive. As such, only a small fraction of all eclipsing binaries for which data have been available have been fully characterized. To improve computational efficiency, we construct an uncertainty-aware neural network which can ingest phase-folded light curves in any of 50 commonly used passbands, combined with phase-folded radial velocity measurements for both primary and secondary, as well as fluxes across the spectral energy distribution to predict stellar and orbital parameters of eclipsing binaries. The model was trained to be agnostic to the presence of third light, spots (both cool and hot), and incomplete data. As the model is operating in a probabilistic framework, it is also capable of outputting uncertainties in all of the parameters. The model was trained on synthetic data, and applied to a set of $\sim$200 previously solved real eclipsing binaries to demonstrate its performance. The model is capable of determining masses and radii of eclipsing binaries with precision of $\lesssim$20\% and $T_{\rm eff}$ with precision of $\sim$500 K in only a fraction of the time it takes the more traditional solvers. Although the resulting uncertainties are larger than what is possible to produce using more boutique analysis of individual stars, in the era of large photometric surveys, this approach allows to identify the most interesting systems, and it provides a starting point of the distributions in all of the parameters that these solvers could improve upon.

astro-ph.SR↗

arXiv2025

Gaia Net: Towards robust spectroscopic parameters of stars of all evolutionary stages

We present a new processing of XP spectra for 220 million stars released in Gaia DR3. The new data model is capable of handling objects with Teff between 2000 and 50,000 K, and with log g between 0 and 10, including objects of multitude of masses and evolutionary stages. This includes for the first time ever robust processing of spectroscopic parameters for pre-main sequence stars, with log g sensitivity towards their age. Through this analysis we examine the distribution of young low mass stars with ages of up to 20 Myr in the solar neighborhood, and we identify a new massive (>1000 stars) population, Ophion, which is found east of Sco Cen. This population appears to be fully disrupted, with negligible kinematic coherence. Nonetheless, due its young age it appears to still persist as a spacial overdensity. Through improved determination of ages of the nearby stars, it may be possible to better recover star forming history of the solar neighborhood outside of the moving groups.

astro-ph.SR↗

arXiv2024

Emulating the Global Change Analysis Model with Deep Learning

The Global Change Analysis Model (GCAM) simulates complex interactions between the coupled Earth and human systems, providing valuable insights into the co-evolution of land, water, and energy sectors under different future scenarios. Understanding the sensitivities and drivers of this multisectoral system can lead to more robust understanding of the different pathways to particular outcomes. The interactions and complexity of the coupled human-Earth systems make GCAM simulations costly to run at scale - a requirement for large ensemble experiments which explore uncertainty in model parameters and outputs. A differentiable emulator with similar predictive power, but greater efficiency, could provide novel scenario discovery and analysis of GCAM and its outputs, requiring fewer runs of GCAM. As a first use case, we train a neural network on an existing large ensemble that explores a range of GCAM inputs related to different relative contributions of energy production sources, with a focus on wind and solar. We complement this existing ensemble with interpolated input values and a wider selection of outputs, predicting 22,528 GCAM outputs across time, sectors, and regions. We report a median $R^2$ score of 0.998 for the emulator's predictions and an $R^2$ score of 0.812 for its input-output sensitivity.

econ.GN↗

arXiv2024

A self-consistent data-driven model for determining stellar parameters from optical and near-IR spectra

Data-driven models, which apply machine learning to infer physical properties from large quantities of data, have become increasingly important for extracting stellar properties from spectra. In general, these methods have been applied to data in one wavelength regime or another. For example, APOGEE Net has been applied to near-IR spectra from the SDSS-V APOGEE survey to predict stellar parameters (Teff, log g, and [Fe/H]) for all stars with Teff from 3,000 to 50,000 K, including pre-main sequence stars, OB stars, main sequence dwarfs, and red giants. The increasing number of large surveys across multiple wavelength regimes provides the opportunity to improve data-driven models through learning from multiple datasets at once. In SDSS-V, a number of spectra of stars will be observed not just with APOGEE in near-IR, but also with BOSS in optical regime. Here we aim to develop a complementary model, BOSS Net, that will replicate the performance of APOGEE Net in these optical data through label transfer. We further improve the model by extending it to brown dwarfs, as well as white dwarfs, resulting in a comprehensive coverage between 1700<Teff<100,000 K and 0<log g<10, to ensure BOSS Net can reliably measure parameters of most of the commonly observed objects within this parameter space. We also update APOGEE Net to achieve a comparable performance in the near-IR regime. The resulting models provide a robust tool for measuring stellar evolutionary states, and in turn, enable characterization of the star forming history of the Galaxy.

astro-ph.SR↗

arXiv2023

Proposal-based Few-shot Sound Event Detection for Speech and Environmental Sounds with Perceivers

Many applications involve detecting and localizing specific sound events within long, untrimmed documents, including keyword spotting, medical observation, and bioacoustic monitoring for conservation. Deep learning techniques often set the state-of-the-art for these tasks. However, for some types of events, there is insufficient labeled data to train such models. In this paper, we propose a region proposal-based approach to few-shot sound event detection utilizing the Perceiver architecture. Motivated by a lack of suitable benchmark datasets, we generate two new few-shot sound event localization datasets: "Vox-CASE," using clips of celebrity speech as the sound event, and "ESC-CASE," using environmental sound events. Our highest performing proposed few-shot approaches achieve 0.483 and 0.418 F1-score, respectively, with 5-shot 5-way tasks on these two datasets. These represent relative improvements of 72.5% and 11.2% over strong proposal-free few-shot sound event detection baselines.

eess.AS↗