Results 21 to 30 of about 78 (56)

Regression for Astronomical Data with Realistic Distributions, Errors, and Nonlinearity

open access: yesThe Astronomical Journal
We have developed a new regression technique, the maximum likelihood (ML)–based method and its variant, the Kolmogorov–Smirnov (KS) test–based method, designed to obtain unbiased regression results from typical astronomical data. A normalizing flow model
Tao Jing, Cheng Li
doaj   +1 more source

AstroM3: A Self-supervised Multimodal Model for Astronomy

open access: yesThe Astronomical Journal
While machine-learned models are now routinely employed to facilitate astronomical inquiry, model inputs tend to be limited to a primary data source (namely images or time series) and, in the more advanced approaches, some metadata.
M. Rizhko, J. S. Bloom
doaj   +1 more source

An Information-based Metric for Observing Strategy Optimization, Demonstrated in the Context of Photometric Redshifts for LSST

open access: yesThe Astrophysical Journal Supplement Series
The observing strategy (OS) of an astronomical survey influences the degree to which its resulting data can be used to accomplish any science goal, necessitating upcoming programs, including those on the Rubin and Roman telescopes, to solicit metrics in ...
Alex I. Malz   +4 more
doaj   +1 more source

Identifying Diffuse Spatial Structures in High-energy Photon Lists

open access: yesThe Astronomical Journal, 2023
Data from high-energy observations are usually obtained as lists of photon events. A common analysis task for such data is to identify whether diffuse emission exists, and to estimate its surface brightness, even in the presence of point sources that may
Minjie Fan   +5 more
doaj   +1 more source

Statistical Properties of a Polarization Vector’s Ellipticity Angle

open access: yesThe Astrophysical Journal
The orientation of a polarization vector on the Poincaré sphere is defined by its position angle (PA) and ellipticity angle (EA). The radio emission from pulsars, magnetars, and fast radio bursts can be elliptically polarized, and measurements of the EA ...
M. M. McKinnon
doaj   +1 more source

Polka-dotted Stars: A Hierarchical Model for Mapping Stellar Surfaces Using Occultation Light Curves and the Case of TOI-3884

open access: yesThe Astrophysical Journal
We present StarryStarryProcess , a novel hierarchical Bayesian framework for mapping stellar surfaces using exoplanet transit light curves. While previous methods relied solely on stellar rotational light curves—which contain limited information about ...
Sabina Sagynbayeva   +3 more
doaj   +1 more source

ABC-SN: Attention-based Classifier for Supernova Spectra

open access: yesThe Astrophysical Journal
While significant advances have been made in photometric classification ahead of the millions of transient events and hundreds of supernovae (SNe) each night that the Vera C.
Willow Fox Fortino   +4 more
doaj   +1 more source

Properties of Flare-imminent versus Flare-quiet Active Regions from the Chromosphere through the Corona. II. Nonparametric Discriminant Analysis Results from the NWRA Classification Infrastructure (NCI)

open access: yesThe Astrophysical Journal, 2023
A large sample of active-region-targeted time-series images from the Solar Dynamics Observatory/Atmospheric Imaging Assembly (AIA), the AIA Active Region Patch database (Paper I) is used to investigate whether parameters describing the coronal ...
K. D. Leka   +3 more
doaj   +1 more source

Auto-BUQ: Uncertainty Quantification for the Boundaries of Segmented Events

open access: yesThe Astronomical Journal
We present a new method to estimate the boundary of extended sources in high-energy photon lists and to quantify the uncertainty in the boundary. This method extends the graphed seeded region growing method developed by M. Fan et al.
Jue Wang   +4 more
doaj   +1 more source

Exploring Magnetic Fields in Molecular Clouds through Denoising Diffusion Probabilistic Models

open access: yesThe Astrophysical Journal
Accurately measuring magnetic field strength in the interstellar medium, including giant molecular clouds, remains a significant challenge. We present a machine learning approach using denoising diffusion probabilistic models (DDPMs) to estimate magnetic
Duo Xu   +5 more
doaj   +1 more source

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