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Latest articles for The Astrophysical Journal
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Explainable Machine Learning Classification of Chandra X-Ray Sources: SHAP Analysis of Multiwavelength Features
Extensive astronomical surveys, like those conducted with the Chandra X-ray Observatory, detect hundreds of thousands of unidentified cosmic sources. Machine learning (ML) methods offer an efficient, probabilistic approach to classifying them, which can be useful for making discoveries and conducting deeper studies. In earlier work, we applied the LightGBM (ML model) to classify 277,069 Chandra point sources into eight categories: active galactic nuclei (AGNs), X-ray emitting stars, young stellar objects (YSO), high-mass X-ray binaries, low-mass X-ray binaries, ultraluminous X-ray sources, cataclysmic variables, and pulsars. In this work, we present the classification table of 54,770 robustly classified sources (over 3σ confidence), including 14,066 sources at >4σ significance. To ensure classification reliability and gain a deeper insight, we investigate the multiwavelength feature relationships learned by the LightGBM model, focusing on AGNs, stars, and YSOs. We employ explainable artificial intelligence (XAI) techniques, specifically, Shapley Additive Explanations, to quantify the contribution of individual features and their interactions to the predicted classification probabilities. Among other things, we find infrared-optical and X-ray decision boundaries for separating AGN/stars, and infrared-X-ray boundaries for YSOs. These results are crucial for estimating object classes even with limited multiwavelength data. This study represents one of the earliest applications of XAI to large-scale astronomical data sets, demonstrating ML models’ potential for uncovering physically meaningful patterns in data in addition to classification. Finally, our publicly available, extensive, and interactive catalog will be helpful to explore the contributions of features and their combinations in greater detail in the future.
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Improving Radio Source Count Estimation Using Kernel Density Estimation
Radio source counts provide a fundamental census of cosmic radio emission, yet their estimation is usually based on coarse histograms that suffer from bin-choice bias, boundary effects, and survey incompleteness. We apply and rigorously evaluate kernel density estimation (KDE) as a nonparametric alternative to the conventional binned method for estimating differential radio source counts. Using simulated flux-limited samples derived from an input luminosity function model, we compare the performance of standard KDE, adaptive KDE, and traditional binning methods. Our results show that KDE-based approaches yield more accurate and stable estimates, particularly in the high-flux regime where data are sparse and conventional methods struggle. We also apply the adaptive KDE method to real observational data from the LOFAR Two-Metre Sky Survey Deep Fields. Our analysis robustly confirms the pronounced “drop and bump” feature at sub-mJy flux densities, but also reveals that a secondary, modest bump seen in the binned data at ∼10 mJy is likely a binning artifact. We also demonstrate the flexibility of KDE in addressing observational incompleteness through weighted estimation, which applies weights continuously at the level of individual sources rather than averaging them in discrete bins. These strengths make KDE a powerful tool for source-count analyses in current and future radio surveys and, more broadly, in analogous studies at other wavelengths. All computations in this study are implemented with AstroKDE, a Python package we have developed for astronomical applications.
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Probing the Physical and Chemical Characteristics of an Extremely Early Class 0 Protostar in G204.4-11.3A2-NE
We have observed the low-mass molecular cloud core G204.4-11.3A2-NE (G204NE) in the direction of Orion B giant molecular cloud with the Atacama Large Millimeter/submillimeter Array in Band 6. The 1.3 mm continuum images and visibilities unveil a compact central structure with a radius of ∼12 au, while showing no signature of binarity down to 18 au. The bolometric temperature and luminosity of this source are derived to be ∼33 K and ∼1.15 L⊙, respectively. Chemical stratification is observed in dense gas tracers, with C18O emission peaking at the continuum position surrounded by the spatially extended emission of N2D+ and DCO+. This implies that the core is in a very early evolutionary stage in which CO depletion occurs in most regions except for a small area heated by the central source. The envelope kinematics indicate a rotating and infalling structure with a central protostar mass of 0.08–0.1 M⊙. The protostar drives a collimated outflow traced by CO, SiO, SO, and H2CO, with misaligned blueshifted and redshifted lobes exhibiting a pair of bow-like patterns. High-velocity jets, extending up to 720 au, are detected in CO, SiO, and SO lines. The jet launching region is likely within twice of the dust sublimation zone. The absence of a binary signature suggests the outflows and jets are driven by a single protostar, although a close binary cannot be ruled out. The observed deflection of the outflows and jet is likely due to turbulent accretion in a moderately magnetized core.
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DESI Spectroscopy of HETDEX Emission-line Candidates. I. Line Discrimination Validation
The Hobby–Eberly Dark Energy Experiment (HETDEX) is an untargeted spectroscopic galaxy survey that uses Lyα-emitting galaxies (LAEs) as tracers of 1.9 < z < 3.5 large-scale structure. Most detections consist of a single emission line, whose identity is inferred via a Bayesian analysis of ancillary data. To determine the accuracy of these line identifications, HETDEX detections were observed with the Dark Energy Spectroscopic Instrument (DESI). In two DESI pointings, high-confidence spectroscopic redshifts are obtained for 1157 sources, including 982 LAEs. The DESI spectra are used to evaluate the accuracy of the HETDEX object classifications and tune the methodology to achieve the HETDEX science requirement of ≲2% contamination of the LAE sample by low-redshift emission-line galaxies, while still assigning 96% of the true Lyα emission sample with the correct spectroscopic redshift. We compare emission-line measurements between the two experiments assuming a simple Gaussian line fitting model. Fitted values for the central wavelength of the emission line, the measured line flux, and line widths are consistent between the surveys within uncertainties. Derived spectroscopic redshifts, from the two classification pipelines, when both agree as an LAE classification, are consistent to within 〈Δz/(1 + z)〉 = 6.9 × 10−5 with an rms scatter of 3.3 × 10−4. Data are available at https://data.desi.lbl.gov/desi/public/dr1/vac/dr1/hetdex.
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Modeling the Energy Release in Solar Eruptive Events
Magnetic reconnection in a flare current sheet is widely believed to be the main energy release process powering solar flares and coronal mass ejections (CMEs). Modeling this process and determining the channels for the energy release, mass motions, and heating has long been a major goal in space science. We present results from a two-fluid magnetohydrodynamic simulation of an eruptive flare/CME using a newly developed version of the Space Weather Modeling Framework that incorporates two major advances in numerical capability. First, we use the STatistical InjecTion of Condensed Helicity formalism for the energy buildup, so that we start with a potential-field minimum-energy state and slowly form a sheared filament channel over a polarity inversion line as is observed on the Sun. Second, we use a new formulation of the plasma energetics that is explicitly energy conserving while calculating separate electron and ion temperatures and separate parallel and perpendicular pressures, as desired. For this first simulation with our new model, we opted for the nonadiabatic heating to go solely into the protons and for an isotropic pressure. We discuss the resulting energetics of the reconnection and, in particular, the plasma heating in the reconnecting current sheets, mass acceleration, and shock formation. We also discuss the implications of our results for flare/CME observations and for understanding solar eruptions in general.