scholarly journals X-ray study of spatial structures in Tycho’s supernova remnant using unsupervised deep learning

2019 ◽  
Vol 488 (3) ◽  
pp. 4106-4116 ◽  
Author(s):  
Hiroyoshi Iwasaki ◽  
Yuto Ichinohe ◽  
Yasunobu Uchiyama
Keyword(s):  
Spectral Analysis ◽  
Deep Learning ◽  
Spectral Data ◽  
Supernova Remnant ◽  
Rapid Development ◽  
Feature Space ◽  
Gaussian Mixture ◽  
Spatial Structures ◽  
X Ray ◽  
Unsupervised Deep Learning

ABSTRACT 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 Tycho’s 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 recognized 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 galaxy clusters. 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.

scholarly journals X-ray Observations of SNR E0102.2-72.2 in the SMC

1988 ◽  
Vol 101 ◽  
pp. 125-128
Author(s):  
John P. Hughes
Keyword(s):  
High Resolution ◽  
Emission Line ◽  
Spectral Data ◽  
Proportional Counter ◽  
High Velocity ◽  
Supernova Remnant ◽  
Optical Emission ◽  
Small Magellanic Cloud ◽  
X Ray ◽  
Best Fit

AbstractThe supernova remnant (SNR) E0102.2-72.2 is the brightest in the Small Magellanic Cloud (SMC) at X-ray wavelengths. This object, which is remarkable because of its high velocity (∼4000 km s−1) oxygen-rich optical emission, appears to be similarly remarkable at X-ray wavelengths. The high resolution imager (HRI) data can be quite well described by a thick ring with a radius of ∼19" (6 pc at a distance of 63 kpc). The imaging proportional counter (IPC) X-ray spectral data can be best fit by a single emission line of energy ∼0.9 keV. It seems likely that this is the emission from a plasma of almost pure neon.

scholarly journals Probing the effects of hadronic acceleration at the SN 1006 shock front

2013 ◽  
Vol 9 (S296) ◽  
pp. 315-319
Author(s):  
Marco Miceli ◽  
F. Bocchino ◽  
A. Decourchelle ◽  
G. Maurin ◽  
J. Vink ◽  
...  
Keyword(s):  
Particle Acceleration ◽  
Spectral Analysis ◽  
Supernova Remnants ◽  
Supernova Remnant ◽  
Ambient Medium ◽  
Cosmic Ray ◽  
X Ray ◽  
Spatially Resolved ◽  
Large Program ◽  
Post Shock

AbstractSupernova remnant shocks are strong candidates for being the source of energetic cosmic rays and hadron acceleration is expected to increase the shock compression ratio, providing higher post-shock densities. We exploited the deep observations of the XMM-Newton Large Program on SN 1006 to verify this prediction. Spatially resolved spectral analysis led us to detect X-ray emission from the shocked ambient medium in SN 1006 and to find that its density significantly increases in regions where particle acceleration is efficient. Our results provide evidence for the effects of acceleration of cosmic ray hadrons on the post-shock plasma in supernova remnants.

scholarly journals Discovery of recombining plasma associated with the candidate supernova remnant G189.6+3.3 with Suzaku

2020 ◽  
Vol 72 (5) ◽  
Author(s):  
Shigeo Yamauchi ◽  
Moe Oya ◽  
Kumiko K Nobukawa ◽  
Thomas G Pannuti
Keyword(s):  
Spectral Analysis ◽  
Supernova Remnant ◽  
Spectral Feature ◽  
Plasma Component ◽  
Ionization Equilibrium ◽  
Equilibrium Plasma ◽  
Northeast Region ◽  
X Ray ◽  
Collisional Ionization ◽  
First Time

Abstract We present the results of an X-ray spectral analysis of the northeast region of the candidate supernova remnant G189.6+3.3 with Suzaku. K-shell lines from highly ionized Ne, Mg, Si, and S were detected in the spectrum for the first time. In addition, a radiative recombining continuum (RRC) from He-like Si was clearly seen near 2.5 keV. This detection of an RRC reveals for the first time that G189.6+3.3 possesses an X-ray-emitting recombining plasma (RP). The extracted X-ray spectrum in the 0.6–10.0 keV energy band is well fitted with a model consisting of a collisional ionization equilibrium plasma component (associated with the interstellar medium) and an RP component (associated with the ejecta). The spectral feature shows that G189.6+3.3 is most likely to be a middle-aged SNR with an RP.

scholarly journals Advancing Medical Imaging Informatics by Deep Learning-Based Domain Adaptation

2020 ◽  
Vol 29 (01) ◽  
pp. 129-138 ◽  
Author(s):  
Anirudh Choudhary ◽  
Li Tong ◽  
Yuanda Zhu ◽  
May D. Wang
Keyword(s):  
Deep Learning ◽  
Medical Imaging ◽  
Large Scale ◽  
Domain Adaptation ◽  
Rapid Development ◽  
Synthetic Data ◽  
Feature Space ◽  
Imaging Informatics ◽  
Training Data ◽  
Domain Transformation

Introduction: There has been a rapid development of deep learning (DL) models for medical imaging. However, DL requires a large labeled dataset for training the models. Getting large-scale labeled data remains a challenge, and multi-center datasets suffer from heterogeneity due to patient diversity and varying imaging protocols. Domain adaptation (DA) has been developed to transfer the knowledge from a labeled data domain to a related but unlabeled domain in either image space or feature space. DA is a type of transfer learning (TL) that can improve the performance of models when applied to multiple different datasets. Objective: In this survey, we review the state-of-the-art DL-based DA methods for medical imaging. We aim to summarize recent advances, highlighting the motivation, challenges, and opportunities, and to discuss promising directions for future work in DA for medical imaging. Methods: We surveyed peer-reviewed publications from leading biomedical journals and conferences between 2017-2020, that reported the use of DA in medical imaging applications, grouping them by methodology, image modality, and learning scenarios. Results: We mainly focused on pathology and radiology as application areas. Among various DA approaches, we discussed domain transformation (DT) and latent feature-space transformation (LFST). We highlighted the role of unsupervised DA in image segmentation and described opportunities for future development. Conclusion: DA has emerged as a promising solution to deal with the lack of annotated training data. Using adversarial techniques, unsupervised DA has achieved good performance, especially for segmentation tasks. Opportunities include domain transferability, multi-modal DA, and applications that benefit from synthetic data.

scholarly journals ROSAT X-Ray Observations of the Radio Galaxy 4C46.09

1998 ◽  
Vol 188 ◽  
pp. 447-448
Author(s):  
D.A. Leahy
Keyword(s):  
Spectral Analysis ◽  
Radio Source ◽  
Supernova Remnant ◽  
Radio Galaxy ◽  
High Energy ◽  
Energy Component ◽  
X Ray ◽  
Hardness Ratio ◽  
High Energy Component

4C46.09 is the radio source that shows up as a point-like x-ray source inside the supernova remnant HB9 (Leahy, 1987). Leahy, 1987 found a 0.2-4 keV Einstein IPC flux of approximately 1.5 × 10−12erg cm−2s−1 and a significantly higher hardness ratio than the rest of HB9. Too few counts were available for any spectral analysis. Seward et al, 1991, found 4C46.09 to be a large radio galaxy at redshift 0.195 and distance 1280 Mpc (Ho = 50 km s−1Mpc−1). 4C46.09 is of further interest due to the observation of a high energy component in the spectrum of HB9 observed by GINGA (Yamauchi and Koyama, 1993). Whether this was due to HB9 or to 4C46.09 could not be determined.

scholarly journals X-ray emission from the mixed-morphology supernova remnant HB 9

2020 ◽  
Vol 72 (4) ◽  
Author(s):  
Mariko Saito ◽  
Shigeo Yamauchi ◽  
Kumiko K Nobukawa ◽  
Aya Bamba ◽  
Thomas G Pannuti
Keyword(s):  
Spectral Analysis ◽  
Central Region ◽  
Supernova Remnant ◽  
Interstellar Matter ◽  
Plasma Model ◽  
Ionization Equilibrium ◽  
Equilibrium Plasma ◽  
X Ray ◽  
Collisional Ionization ◽  
Matter Component

Abstract We present the results of a spectral analysis of the central region of the mixed-morphology supernova remnant HB 9. A prior Ginga observation of this source detected a hard X-ray component above 4 keV, and the origin of this particular X-ray component is still unknown. Our results demonstrate that the extracted X-ray spectra are best represented by a model consisting of a collisional ionization equilibrium plasma with a temperature of ∼0.1–0.2 keV (interstellar matter component) and an ionizing plasma with a temperature of ∼0.6–0.7 keV and an ionization timescale of >1 × 1011 cm−3 s (ejecta component). No significant X-ray emission was found in the central region above 4 keV. The recombining plasma model reported by a previous work does not explain our spectra.

scholarly journals X-ray spectral analysis of the neutron star in SNR 1E 0102.2−7219

2019 ◽  
Vol 491 (2) ◽  
pp. 1585-1599 ◽  
Author(s):  
Pavan R Hebbar ◽  
Craig O Heinke ◽  
Wynn C G Ho
Keyword(s):  
Spectral Analysis ◽  
Neutron Star ◽  
Supernova Remnant ◽  
Angular Resolution ◽  
Supernova Explosion ◽  
Effective Area ◽  
Magellanic Clouds ◽  
Careful Attention ◽  
Power Law Model ◽  
X Ray

ABSTRACT 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. 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 = 1012  G best fits the observed X-ray spectra. Comparing its unusually high thermal luminosity ($L_{\mathrm{ bol}} = 1.1_{-0.5}^{+1.6}\times 10^{34}$ erg 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.

Anthosterones A and B, ring A contracted steroids from the sponge Anthoracuata graceae

1988 ◽  
Vol 66 (5) ◽  
pp. 1173-1178 ◽  
Author(s):  
Mark Tischler ◽  
Stephen W. Ayer ◽  
Raymond J. Andersen ◽  
John F. Mitchell ◽  
Jon Clardy
Keyword(s):  
Spectral Analysis ◽  
Single Crystal ◽  
Diffraction Analysis ◽  
Spectral Data ◽  
Model Compound ◽  
X Ray Diffraction ◽  
Steroid Nucleus ◽  
X Ray ◽  
New Type

Four new steroids, anthosterone A (5), anthosterone B (6), and the Δ4-3,6-diketo steroids 7 and 8, have been isolated from the sponge Anthoarcuata graceae. The structures of anthosterones A (5) and B (6) were deduced from their spectral data and verified via single crystal X-ray diffraction analysis on 5. The proposed structures for steroids 7 and 8 were based on spectral analysis and confirmed by synthesis of a model compound 12. Anthosterones A and B represent the first examples of a new type of ring A contraction in a steroid nucleus.

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