The discs of active galactic nuclei (AGNs) have emerged as rich environments for the production and capture of stars and the compact objects that they leave behind. These stars produce long gamma-ray bursts (GRBs) at their deaths, while frequent interactions among compact objects form binary neutron stars and neutron star–black hole binaries, leading to short GRBs upon their merger. Predicting the properties of these transients as they emerge from the dense environments of AGN discs is key to their proper identification and to better constrain the star and compact object population in AGN discs. Some of these transients would appear unusual because they take place in much higher densities than the interstellar medium. Others, which are the subject of this paper, would additionally be modified by radiation diffusion, since they are generated within optically thick regions of the accretion discs. Here, we compute the GRB afterglow light curves for diffused GRB sources for a representative variety of central black hole masses and disc locations. We find that the radiation from radio to ultraviolet and soft X-rays can be strongly suppressed by synchrotron self-absorption in the dense medium of the AGN disc. In addition, photon diffusion can significantly delay themore »
X-ray binaries (XRBs) consist of a compact object that accretes material from an orbiting secondary star. The most secure method we have for determining if the compact object is a black hole is to determine its mass: This is limited to bright objects and requires substantial time-intensive spectroscopic monitoring. With new X-ray sources being discovered with different X-ray observatories, developing efficient, robust means to classify compact objects becomes increasingly important. We compare three machine-learning classification methods (Bayesian Gaussian Processes (BGPs), K-Nearest Neighbors (KNN), Support Vector Machines) for determining whether the compact objects are neutron stars or black holes (BHs) in XRB systems. Each machine-learning method uses spatial patterns that exist between systems of the same type in 3D color–color–intensity diagrams. We used lightcurves extracted using 6 yr of data with MAXI/GSC for 44 representative sources. We find that all three methods are highly accurate in distinguishing pulsing from nonpulsing neutron stars (NPNS) with 95% of NPNS and 100% of pulsars accurately predicted. All three methods have high accuracy in distinguishing BHs from pulsars (92%) but continue to confuse BHs with a subclass of NPNS, called bursters, with KNN doing the best at only 50% accuracy for predicting BHs. The more »
- Publication Date:
- NSF-PAR ID:
- 10368677
- Journal Name:
- The Astrophysical Journal
- Volume:
- 933
- Issue:
- 1
- Page Range or eLocation-ID:
- Article No. 116
- ISSN:
- 0004-637X
- Publisher:
- DOI PREFIX: 10.3847
- Sponsoring Org:
- National Science Foundation
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