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Register a new userEngaging the Public with Supernova and Supernova Remnant Research Using Virtual Reality
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On 21 April 2018, the citizens of Wako, Japan, interacted in a novel way with research being carried out at the Astrophysical Big Bang Laboratory (ABBL) at RIKEN. They were able to explore a model of a supernova and its remnant in an immersive three-dimentional format by using virtual reality (VR) technology. In this article, we explain how this experience was developed and delivered to the public, providing practical tips for and reflecting on the successful organisation of an event of this kind.
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Supernova 1604 is the last Galactic supernova for which historical records exist. Johannes Keplers name is attached to it, as he published a detailed account of the observations made by himself and European colleagues. Supernova 1604 was very likely a Type Ia supernova, which exploded 350 pc to 750 pc above the Galactic plane. Its supernova remnant, known as Keplers supernova remnant, shows clear evidence for interaction with nitrogen-rich material in the north/northwest part of the remnant, which, given the height above the Galactic plane, must find its origin in mass loss from the supernova progenitor system. The combination of a Type Ia supernova and the presence of circumstellar material makes Keplers supernova remnant a unique object to study the origin of Type Ia supernovae. The evidence suggests that the progenitor binary system of supernova 1604 consisted of a carbon- oxygen white dwarf and an evolved companion star, which most likely was in the (post) asymptotic giant branch of its evolution. A problem with this scenario is that the companion star must have survived the explosion, but no trace of its existence has yet been found, despite a deep search. 1 Introduction; 2 The supernova remnant, its distance and multiwavelength properties; 2.1 Position, distance estimates and SN1604 as a runaway system; 2.2 X-ray imaging spectroscopy and SN1604 as a Type Ia supernova 2.3 The circumstellar medium as studied in the optical and infrared; 3 The dynamics of Keplers SNR; 3.1 Velocity measurements; 3.2 Hydrodynamical simulations; 4 The progenitor system of SN 1604; 4.1 Elevated circumstellar nitrogen abundances, silicates and a single degenerate scenario for SN1604; 4.2 Problems with a single degenerate Type Ia scenario for SN 1604; 4.3 Was SN 1604 a core-degenerate Type Ia explosion?; 4.4 What can we learn from the historical light curve of SN 1604? ; 5 Conclusions
Societys view of astronomers has changed over time and from culture to culture. This review discusses some of the many ways that astronomers have been perceived by their societies and suggests ways that astronomers can influence public perception of ourselves and our profession in the future.
G1.9+0.3 is the youngest known Galactic supernova remnant (SNR), with an estimated supernova (SN) explosion date of about 1900, and most likely located near the Galactic Center. Only the outermost ejecta layers with free-expansion velocities larger than about 18,000 km/s have been shocked so far in this dynamically young, likely Type Ia SNR. A long (980 ks) Chandra observation in 2011 allowed spatially-resolved spectroscopy of heavy-element ejecta. We denoised Chandra data with the spatio-spectral method of Krishnamurthy et al., and used a wavelet-based technique to spatially localize thermal emission produced by intermediate-mass elements (IMEs: Si and S) and iron. The spatial distribution of both IMEs and Fe is extremely asymmetric, with the strongest ejecta emission in the northern rim. Fe Kalpha emission is particularly prominent there, and fits with thermal models indicate strongly oversolar Fe abundances. In a localized, outlying region in the northern rim, IMEs are less abundant than Fe, indicating that undiluted Fe-group elements (including 56Ni) with velocities larger than 18,000 km/s were ejected by this SN. But in the inner west rim, we find Si- and S-rich ejecta without any traces of Fe, so high-velocity products of O-burning were also ejected. G1.9+0.3 appears similar to energetic Type Ia SNe such as SN 2010jn where iron-group elements at such high free-expansion velocities have been recently detected. The pronounced asymmetry in the ejecta distribution and abundance inhomogeneities are best explained by a strongly asymmetric SN explosion, similar to those produced in some recent 3D delayed-detonation Type Ia models.
We investigate six supernova remnant (SNR) candidates --- G51.21+0.11, G52.37-0.70, G53.07+0.49, G53.41+0.03, G53.84-0.75, and the possible shell around G54.1-0.3 --- in the Galactic Plane using newly acquired LOw-Frequency ARray (LOFAR) High-Band Antenna (HBA) observations, as well as archival Westerbork Synthesis Radio Telescope (WSRT) and Very Large Array Galactic Plane Survey (VGPS) mosaics. We find that G52.37-0.70, G53.84-0.75, and the possible shell around pulsar wind nebula G54.1+0.3 are unlikely to be SNRs, while G53.07+0.49 remains a candidate SNR. G51.21+0.11 has a spectral index of $alpha=-0.7pm0.21$, but lacks X-ray observations and as such requires further investigation to confirm its nature. We confirm one candidate, G53.41+0.03, as a new SNR because it has a shell-like morphology, a radio spectral index of $alpha=-0.6pm0.2$ and it has the X-ray spectral characteristics of a 1000-8000 year old SNR. The X-ray analysis was performed using archival XMM-Newton observations, which show that G53.41+0.03 has strong emission lines and is best characterized by a non-equilibrium ionization model, consistent with an SNR interpretation. Deep Arecibo radio telescope searches for a pulsar associated with G53.41+0.03 resulted in no detection, but place stringent upper limits on the flux density of such a source if it is beamed towards Earth.
Recent rapid development of deep learning algorithms, which can implicitly capture structures in high-dimensional data, opens a new chapter in astronomical data analysis. We report here a new implementation of deep learning techniques for X-ray analysis. We apply a variational autoencoder (VAE) using a deep neural network for spatio-spectral analysis of data obtained by Chandra X-ray Observatory from Tychos supernova remnant (SNR). We established an unsupervised learning method combining the VAE and a Gaussian mixture model (GMM), where the dimensions of the observed spectral data are reduced by the VAE, and clustering in feature space is performed by the GMM. We found that some characteristic spatial structures, such as the iron knot on the eastern rim, can be automatically recognised by this method, which uses only spectral properties. This result shows that unsupervised machine learning can be useful for extracting characteristic spatial structures from spectral information in observational data (without detailed spectral analysis), which would reduce human-intensive preprocessing costs for understanding fine structures in diffuse astronomical objects, e.g., SNRs or clusters of galaxies. Such data-driven analysis can be used to select regions from which to extract spectra for detailed analysis and help us make the best use of the large amount of spectral data available currently and arriving in the coming decades.
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