Black holes in dwarf/bulgeless galaxies play a crucial role in studying the co-evolution of galaxies and their central black holes. Identifying massive black holes in dwarf galaxies suggests that the growth of black holes could precede that of galaxies. However, some of the most intriguing candidate active galactic nuclei (AGN) in small galaxies have such low luminosities that the sample is vulnerable to contamination by other sources, such as supernova remnants. We re-analysed Chandra X-ray Observatory observations of candidate AGN in Henize 2-10 and NGC 4178, considering the potential signals of emission lines in the minimally-binned X-ray spectra. We find that hot plasma models, which are typical of supernova remnants, explain the observed spectra much better than simple power-law models, which are appropriate for AGN. We identify clear signals of X-ray lines in the faint X-ray source identified with the radio source in Henize 2-10 by Reines et al. 2016. Combining our work with the MUSE measurement of the ionization parameter in this region by Cresci et al. 2017 indicates that this radio and X-ray source is more likely a supernova remnant than an AGN. A similar analysis of the low-count X-ray spectrum of a candidate AGN in NGC 4178 shows that a hot plasma model is about seventeen times more probable than a simple power-law model. Our results indicate that investigation of X-ray spectra, even in a low-count regime, can be a crucial tool to identify thermally-dominated supernova remnants among AGN candidates.

We re-analysed numerous archival Chandra X-ray observations of the bright supernova remnant (SNR) 1E 0102.2-7219 in the Small Magellanic Cloud, to validate the detection of a neutron star (NS) in the SNR by Vogt et al. (2018). Careful attention to the background is necessary in this spectral analysis. We find that a blackbody + power-law model is a decent fit, suggestive of a relatively strong B field and synchrotron radiation, as in a normal young pulsar, though the thermal luminosity would be unusually high for young pulsars. Among realistic NS atmosphere models, a carbon atmosphere with B = 10 12 G best fits the observed X-ray spectra. Comparing its unusually high thermal luminosity (L bol = 1.1 +1.6 −0.5 × 10 34 ergs s −1 ) to other NSs, we find that its luminosity can be explained by decay of an initially strong magnetic field (as in magnetars or high B-field pulsars) or by slower cooling after the supernova explosion. The nature of the NS in this SNR (and of others in the Magellanic Clouds) could be nicely confirmed by an X-ray telescope with angular resolution like Chandra, but superior spectral resolution and effective area, such as the Lynx concept.

The Cadmium Zinc Telluride Imager on AstroSat has proven to be a very effective all-sky monitor in the hard X-ray regime, detecting over three hundred GRBs and putting highly competitive upper limits on X-ray emissions from gravitational wave sources and fast radio bursts. We present the algorithms used for searching for such transient sources in CZTI data, and for calculating upper limits in case of non-detections. We introduce CIFT: the CZTI Interface for Fast Transients, a framework used to streamline these processes. We present details of 88 new GRBs detected by this framework that were previously not detected in CZTI.

Redback millisecond pulsars (MSPs) typically show pronounced orbital variability in their X-ray emission due to our changing view of the intrabinary shock (IBS) between the pulsar wind and stellar wind from the companion. Some redbacks (“transitional” MSPs) have shown dramatic changes in their multiwavelength properties, indicating a transition from a radio pulsar state to an accretion-powered state. The redback MSP 47 Tuc W showed clear X-ray orbital variability in the Chandra ACIS-S observations in 2002, which were not detectable in the longer Chandra HRC-S observations in 2005–06, suggesting that it might have undergone a state transition. However, the Chandra observations of 47 Tuc in 2014–15 show similar X-ray orbital variability as in 2002. We explain the different X-ray light-curves from these epochs in terms of two components of the X-ray spectrum (soft X-rays from the pulsar, vs. harder X-rays from the IBS), and different sensitivities of the X-ray instruments observing in each epoch. However, when we use our best-fit spectra with HRC response files to model the HRC light-curve, we expect a more significant and shorter dip than that observed in the 2005–06 Chandra data. This suggests an intrinsic change in the IBS of the system. We use the ICARUS stellar modelling software, including calculations of heating by an IBS, to model the X-ray, optical, and UV light-curves of 47 Tuc W. Our best-fitting parameters point towards a high-inclination system ($i \sim 60 \deg$), which is primarily heated by the pulsar radiation, with an IBS dominated by the companion wind momentum.

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Modern X-ray telescopes have detected hundreds of thousands of X-ray sources in the universe. However, current methods to classify these sources using the X-ray data themselves suffer problems—detailed X-ray spectroscopy of individual sources is too time consuming, while hardness ratios often lack accuracy, and can be difficult to use effectively. These methods fail to use the power of X-ray CCD detectors to identify X-ray emission lines and distinguish line-dominated spectra (from chromospherically active stars, supernova remnants, etc.) from continuum-dominated ones (e.g., compact objects or active galactic nuclei, AGN). In this paper, we probe the use of artificial neural networks (ANN) in differentiating Chandra spectra of young stars in the Chandra Orion Ultradeep Project (COUP) survey from AGN in the Chandra Deep Field South (CDFS) survey. We use these surveys to generate 100,000 artificial spectra of stars and AGN, and train our ANN models to separate the two kinds of spectra. We find that our methods reach an accuracy of ∼92% in classifying simulated spectra of moderate-brightness objects in typical exposures, but their performance decreases on the observed COUP and CDFS spectra (∼91%), due in large part to the relatively high background of these long-exposure data sets. We also investigate the performance of our methods with changing properties of the spectra such as the net source counts, the relative contribution of background, the absorption column of the sources, etc. We conclude that these methods have substantial promise for application to large X-ray surveys.