Classification of low-luminosity stellar X-ray sources in the field of the Draco dwarf spheroidal galaxy

Aims. A previous study of the X-ray luminosity function of the X-ray sources in the Draco dwarf spheroidal (dSph) galaxy field indicates the presence of a population of unknown X-ray sources in the soft energy range of 0.5–2 keV. In 2015, there were twenty-six further deep XMM-Newton observations of Draco dSph, providing an opportunity for a new study of the as yet unclassified sources. Methods. We applied the classification criteria presented in our previous multi-wavelength study of the X-ray sources of the Draco dSph to the sources detected in the combined 2009 and 2015 XMM-Newton data set. These criteria are based on X-ray studies and properties of the optical, near-infrared, and mid-infrared counterparts and allows us to distinguish background active galactic nuclei (AGNs) and galaxies from other types of X-ray sources. In this work we performed X-ray spectral and timing analyses for fifteen sources in the field of Draco dSph with stellar counterparts. Results. We present the classification of X-ray sources, for which the counterpart is identified as a stellar object based on our criteria from multi-wavelength data. We identify three new symbiotic stars in the Draco dSph with X-ray luminosities between ∼3.5 × 1034 erg s−1 and 5.5 × 1034 erg s−1. The X-ray spectral analysis shows that two of the classified symbiotic stars are β-type. This is the first identification of this class of symbiotic stars in a nearby galaxy. Eight sources are classified as Galactic M dwarfs in the field of the Draco dSph. These M dwarfs are between ∼140 and 800 pc distant, with X-ray luminosities are between 1028 and 1029 erg s−1 and logarithmic ratios of X-ray to bolometric luminosity, log(LX/Lbol), between −3.4 and −2.1. The multiple observations allowed us to investigate flare activity of the M dwarfs. For 5 M dwarfs flare(s) are observed with a significance of > 3σ level of confidence. Moreover, we classified three foreground sources, located at distances of the order of ∼1–3 kpc in the field of the Draco dSph. Based on both the X-ray luminosities of these foreground sources (> 1030 erg s−1) and their optical counterparts (late type G or K stars), these X-ray sources are classified as candidates of contact binary systems. Conclusions. Our study of X-ray sources of the Draco dSph shows that accreting white dwarfs are the most promising X-ray population of dSphs, which is in line with theoretical expectations. The number of Galactic M dwarfs detected at our X-ray sensitivity limit is consistent with the expectation based on the space density of M dwarfs.

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Detection of COVID-19 cases through X-ray images using hybrid deep neural network

World Journal of Engineering ◽

10.1108/wje-10-2020-0529 ◽

2021 ◽

Vol ahead-of-print (ahead-of-print) ◽

Author(s):

Rajit Nair ◽

Santosh Vishwakarma ◽

Mukesh Soni ◽

Tejas Patel ◽

Shubham Joshi

Keyword(s):

Real Time ◽

Binary Classification ◽

Data Set ◽

Content Type ◽

X Ray ◽

Internal Parameters ◽

Average Accuracy ◽

Chest X Ray ◽

Multi Class Classification

Purpose The latest 2019 coronavirus (COVID-2019), which first appeared in December 2019 in Wuhan's city in China, rapidly spread around the world and became a pandemic. It has had a devastating impact on daily lives, the public's health and the global economy. The positive cases must be identified as soon as possible to avoid further dissemination of this disease and swift care of patients affected. The need for supportive diagnostic instruments increased, as no specific automated toolkits are available. The latest results from radiology imaging techniques indicate that these photos provide valuable details on the virus COVID-19. User advanced artificial intelligence (AI) technologies and radiological imagery can help diagnose this condition accurately and help resolve the lack of specialist doctors in isolated areas. In this research, a new paradigm for automatic detection of COVID-19 with bare chest X-ray images is displayed. Images are presented. The proposed model DarkCovidNet is designed to provide correct binary classification diagnostics (COVID vs no detection) and multi-class (COVID vs no results vs pneumonia) classification. The implemented model computed the average precision for the binary and multi-class classification of 98.46% and 91.352%, respectively, and an average accuracy of 98.97% and 87.868%. The DarkNet model was used in this research as a classifier for a real-time object detection method only once. A total of 17 convolutionary layers and different filters on each layer have been implemented. This platform can be used by the radiologists to verify their initial application screening and can also be used for screening patients through the cloud. Design/methodology/approach This study also uses the CNN-based model named Darknet-19 model, and this model will act as a platform for the real-time object detection system. The architecture of this system is designed in such a way that they can be able to detect real-time objects. This study has developed the DarkCovidNet model based on Darknet architecture with few layers and filters. So before discussing the DarkCovidNet model, look at the concept of Darknet architecture with their functionality. Typically, the DarkNet architecture consists of 5 pool layers though the max pool and 19 convolution layers. Assume as a convolution layer, and as a pooling layer. Findings The work discussed in this paper is used to diagnose the various radiology images and to develop a model that can accurately predict or classify the disease. The data set used in this work is the images bases on COVID-19 and non-COVID-19 taken from the various sources. The deep learning model named DarkCovidNet is applied to the data set, and these have shown signification performance in the case of binary classification and multi-class classification. During the multi-class classification, the model has shown an average accuracy 98.97% for the detection of COVID-19, whereas in a multi-class classification model has achieved an average accuracy of 87.868% during the classification of COVID-19, no detection and Pneumonia. Research limitations/implications One of the significant limitations of this work is that a limited number of chest X-ray images were used. It is observed that patients related to COVID-19 are increasing rapidly. In the future, the model on the larger data set which can be generated from the local hospitals will be implemented, and how the model is performing on the same will be checked. Originality/value Deep learning technology has made significant changes in the field of AI by generating good results, especially in pattern recognition. A conventional CNN structure includes a convolution layer that extracts characteristics from the input using the filters it applies, a pooling layer that reduces calculation efficiency and the neural network's completely connected layer. A CNN model is created by integrating one or more of these layers, and its internal parameters are modified to accomplish a specific mission, such as classification or object recognition. A typical CNN structure has a convolution layer that extracts features from the input with the filters it applies, a pooling layer to reduce the size for computational performance and a fully connected layer, which is a neural network. A CNN model is created by combining one or more such layers, and its internal parameters are adjusted to accomplish a particular task, such as classification or object recognition.

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A Multi-Wavelength View of the XMM-Newton Galactic Plane

Acta Polytechnica CTU Proceedings ◽

10.14311/app.2014.01.0123 ◽

2014 ◽

Vol 1 (1) ◽

pp. 123-126

Author(s):

Ada Nebot Gómez-Morán ◽

Christian Motch

Keyword(s):

Surface Density ◽

Binary Systems ◽

Galactic Plane ◽

Galactic Centre ◽

Galactic Latitude ◽

X Ray ◽

Model Predictions ◽

Science Centre ◽

Multi Wavelength ◽

Galactic Longitude

We present an X-ray survey of the Galactic Plane conducted by the Survey Science Centre of the XMM-Newton satellite. The survey contains more than 1300 X-ray detections at low and intermediate Galactic latitudes and covering 4 deg<sup>2</sup> well spread in Galactic longitude. From a multi-wavelength analysis, using optical spectra and helped by optical and infrared photometry we identify and classify about a fourth of the sources. The observed surface density of soft X-ray (<2 keV) sources decreases with Galactic latitude and although compatible with model predictions at first glance, presents an excess of stars, likely due to giants in binary systems. In the hard band (>2 keV) the surface density of sources presents an excess with respect to the expected extragalactic contribution. This excess highly concentrates towards the direction of the Galactic Centre and is compatible with previous results from Chandra observations around the Galactic Centre. The nature of these sources is still unknown.

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A survey of AGN and supermassive black holes in the COSMOS Survey

Proceedings of the International Astronomical Union ◽

10.1017/s1743921307005169 ◽

2006 ◽

Vol 2 (S238) ◽

pp. 287-290 ◽

Cited By ~ 1

Author(s):

Chris D. Impey ◽

Jon R. Trump ◽

Pat J. McCarthy ◽

Martin Elvis ◽

John P. Huchra ◽

...

Keyword(s):

Black Holes ◽

Large Scale ◽

Supermassive Black Holes ◽

Host Galaxy ◽

Data Set ◽

X Ray ◽

Cosmic Evolution ◽

Early Results ◽

Multi Wavelength ◽

History Of

AbstractThe Cosmic Evolution Survey (COSMOS) is an HST/ACS imaging survey of 2 square degrees centered on RA = 10:00:28.6, Dec = + 02:12:21 (J2000). While the primary goal of the survey is to study evolution of galaxy morphology and large scale structure, an extensive multi-wavelength data set allows for a sensitive survey of AGN. Spectroscopy of optical counterparts to faint X-ray and radio sources is being carried out with the Magallen (Baade) Telescope and the ESO VLT. By achieving ∼80 redshift completeness down to I AB = 3, the eventual yield of AGN will be ∼1100 over the whole field.Early results on supermassive black holes are described. The goals of the survey include a bolometric census of AGN down to moderate luminosities, the cosmic evolution and fueling history of the central engines, and a study of AGN environments on scales ranging from the host galaxy to clusters and superclusters.

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Measuring fundamental jet properties with multi-wavelength fast timing of the black hole X-ray binary MAXI J1820+070

Monthly Notices of the Royal Astronomical Society ◽

10.1093/mnras/stab820 ◽

2021 ◽

Author(s):

A J Tetarenko ◽

P Casella ◽

J C A Miller-Jones ◽

G R Sivakoff ◽

J A Paice ◽

...

Keyword(s):

Black Hole ◽

Time Domain ◽

New Technology ◽

Time Domain Analysis ◽

Jet Physics ◽

Time Lags ◽

Data Set ◽

X Ray ◽

Fast Timing ◽

Multi Wavelength

Abstract We present multi-wavelength fast timing observations of the black hole X-ray binary MAXI J1820+070 (ASASSN-18ey), taken with the Karl G. Jansky Very Large Array (VLA), Atacama Large Millimeter/Sub-Millimeter Array (ALMA), Very Large Telescope (VLT), New Technology Telescope (NTT), Neutron Star Interior Composition Explorer (NICER), and XMM-Newton. Our data set simultaneously samples ten different electromagnetic bands (radio – X-ray) over a 7-hour period during the hard state of the 2018–2019 outburst. The emission we observe is highly variable, displaying multiple rapid flaring episodes. To characterize the variability properties in our data, we implemented a combination of cross-correlation and Fourier analyses. We find that the emission is highly correlated between different bands, measuring time-lags ranging from hundreds of milliseconds between the X-ray/optical bands to minutes between the radio/sub-mm bands. Our Fourier analysis also revealed, for the first time in a black hole X-ray binary, an evolving power spectral shape with electromagnetic frequency. Through modelling these variability properties, we find that MAXI J1820+070 launches a highly relativistic ($\Gamma =6.81^{+1.06}_{-1.15}$) and confined ($\phi =0.45^{+0.13}_{-0.11}$ deg) jet, which is carrying a significant amount of power away from the system (equivalent to ∼0.6 L1 − 100keV). We additionally place constraints on the jet composition and magnetic field strength in the innermost jet base region. Overall, this work demonstrates that time-domain analysis is a powerful diagnostic tool for probing jet physics, where we can accurately measure jet properties with time-domain measurements alone.

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Classification of x-ray images for detection of childhood pneumonia using pre-trained neural networks

Revista Brasileira de Computação Aplicada ◽

10.5335/rbca.v12i3.10343 ◽

2020 ◽

Vol 12 (3) ◽

pp. 132-141

Author(s):

Nator Junior Carvalho da Costa ◽

Jose Vigno Moura Sousa ◽

Domingos Bruno Sousa Santos ◽

Francisco das Chagas Fontenele Marques Junior ◽

Rodrigo Teixeira de Melo

Keyword(s):

Neural Networks ◽

Bacterial Pneumonia ◽

Good Accuracy ◽

Viral Pneumonia ◽

Data Set ◽

X Ray ◽

Chest X Ray ◽

And Training ◽

Validation Testing

This paper describes a comparison between three pre-trained neural networks for the classification of chest X-ray images: Xception, Inception V3, and NasNetLarge. Networks were implemented using learning transfer; The database used was the chest x-ray data set, which contains a total of 5856 chest x-ray images of pediatric patients aged one to five years, with three classes: Normal Viral Pneumonia and Bacterial Pneumonia. Data were divided into three groups: validation, testing and training. A comparison was made with the work of kermany who implemented the Inception V3 network in two ways: (Pneumonia X Normal) and (Bacterial Pneumonia X Viral Pneumonia). The nets used had good accuracy, being the NasNetLarge network the best precision, which was 95.35 \% (Pneumonia X Normal) and 91.79 \% (Viral Pneumonia X Bacterial Pneumonia) against 92.80 \% in (Pneumonia X Normal) and 90.70 \% (Viral Pneumonia X Bacterial Pneumonia) from kermany's work, the Xception network also achieved an improvement in accuracy compared to kermany's work, with 93.59 \% at (Normal X Pneumonia) and 91.03 \% in (Viral Pneumonia X Bacterial Pneumonia).

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Spectra and fast multi-wavelength variability of compact jets powered by internal shocks

Proceedings of the International Astronomical Union ◽

10.1017/s1743921315002112 ◽

2014 ◽

Vol 10 (S313) ◽

pp. 159-163

Author(s):

Julien Malzac ◽

Samia Drappeau

Keyword(s):

Binary Systems ◽

Flicker Noise ◽

Spectral Energy Distribution ◽

Lorentz Factor ◽

Spectral Energy ◽

Noise Power ◽

X Ray ◽

Infrared Spectral ◽

Hard State ◽

Multi Wavelength

AbstractThe emission of steady compact jets observed in the hard spectral state of X-ray binaries is likely to be powered by internal shocks caused by fluctuations of the outflow velocity. The dynamics of the internal shocks and the resulting spectral energy distribution (SED) of the jet is very sensitive to the shape of the Power Spectral Density (PSD) of the fluctuations of the jet Lorentz factor. It turns out that Lorentz factor fluctuations injected at the base of the jet with a flicker noise power spectrum (i.e. P(f) ∝1/f) naturally produce the canonical flat SED observed from radio to IR band in X-ray binary systems in the hard state. This model also predicts a strong, wavelength dependent, variability that resembles the observed one. In particular, strong sub-second variability is predicted in the infrared and optical bands. The assumed fluctuations of the jet Lorentz factor are likely to be triggered by the variability of the accretion flow which is best traced by the X-ray emission. In the case of GX339-4 for which high quality and simultaneous multi-wavelength data are available, we performed simulations assuming that the fluctuation of the jet Lorentz factor have the same PSD as the observed X-ray PSD. The synthetic SED calculated under this assumption provides a remarkable match to the observed radio to IR SED. In this case the model also produces strong mid-infrared spectral variability that is comparable to that reported in this source.

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A VLT/FLAMES survey for massive binaries in Westerlund 1

Astronomy and Astrophysics ◽

10.1051/0004-6361/201834245 ◽

2019 ◽

Vol 623 ◽

pp. A83 ◽

Cited By ~ 5

Author(s):

J. S. Clark ◽

F. Najarro ◽

I. Negueruela ◽

B. W. Ritchie ◽

C. González-Fernández ◽

...

Keyword(s):

Mass Transfer ◽

Binary Systems ◽

Model Atmosphere ◽

Detailed Examination ◽

Single Star ◽

X Ray ◽

Cluster Membership ◽

Blue Stragglers ◽

Multi Wavelength ◽

Velocity Changes

Context. Recent observational studies indicate that a large number of OB stars are found within binary systems which may be expected to interact during their lifetimes. Significant mass transfer or indeed merger of both components is expected to modify evolutionary pathways, facilitating the production of exceptionally massive stars which will present as blue stragglers. Identification and characterisation of such objects is crucial if the efficiency of mass transfer is to be established; a critical parameter in determining the outcomes of binary evolutionary channels. Aims. The young and coeval massive cluster Westerlund 1 hosts a rich population of X-ray bright OB and Wolf–Rayet stars where the emission is attributed to shocks in the wind collision zones of massive binaries. Motivated by this, we instigated a study of the extremely X-ray luminous O supergiants Wd1-27 and -30a. Methods. We subjected a multi-wavelength and -epoch photometric and spectroscopic dataset to quantitative non-LTE model atmosphere and time-series analysis in order to determine fundamental stellar parameters and search for evidence of binarity. A detailed examination of the second Gaia data release was undertaken to establish cluster membership. Results. Both stars were found to be early/mid-O hypergiants with luminosities, temperatures and masses significantly in excess of other early stars within Wd1, hence qualifying as massive blue stragglers. The binary nature of Wd1-27 remains uncertain but the detection of radial velocity changes and the X-ray properties of Wd1-30a suggest that it is a binary with an orbital period ≤10 days. Analysis of Gaia proper motion and parallactic data indicates that both stars are cluster members; we also provide a membership list for Wd1 based on this analysis. Conclusions. The presence of hypergiants of spectral types O to M within Wd1 cannot be understood solely via single-star evolution. We suppose that the early-B and mid-O hypergiants formed via binary-induced mass-stripping of the primary and mass-transfer to the secondary, respectively. This implies that for a subset of objects massive star-formation may be regarded as a two-stage process, with binary-driven mass-transfer or merger yielding stars with masses significantly in excess of their initial “birth” mass.

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From SN 2010da to NGC 300 ULX-1: Ten Years of Observations of an Unusual High Mass X-Ray Binary in NGC 300

Galaxies ◽

10.3390/galaxies8010017 ◽

2020 ◽

Vol 8 (1) ◽

pp. 17

Author(s):

Breanna A. Binder ◽

Stefania Carpano ◽

Marianne Heida ◽

Ryan Lau

Keyword(s):

Neutron Star ◽

Binary System ◽

South African ◽

High Mass ◽

Nearby Galaxy ◽

Transient Event ◽

X Ray ◽

Amateur Astronomer ◽

Multi Wavelength ◽

Optical Transient

In May 2010, an intermediate luminosity optical transient was discovered in the nearby galaxy NGC 300 by a South African amateur astronomer. In the decade since its discovery, multi-wavelength observations of the misnamed “SN 2010da” have continually reshaped our understanding of this high mass X-ray binary system. In this review, we present an overview of the multi-wavelength observations and attempt to understand the 2010 transient event, and later, the reclassification of this system as NGC 300 ULX-1: a red supergiant + neutron star ultraluminous X-ray source.

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Multi-wavelength observations of the Galactic X-ray binaries IGR J20155+3827 and Swift J1713.4−4219

Monthly Notices of the Royal Astronomical Society ◽

10.1093/mnras/stab315 ◽

2021 ◽

Author(s):

F Onori ◽

M Fiocchi ◽

N Masetti ◽

A F Rojas ◽

A Bazzano ◽

...

Keyword(s):

Lower Energy ◽

Gamma Ray ◽

Physical Characterization ◽

Archival Data ◽

X Ray ◽

Photometric Observations ◽

Multi Wavelength ◽

X Ray Binaries

Abstract In recent years, thanks to the continuous surveys performed by INTEGRAL and Swift satellites, our knowledge of the hard X-ray/soft gamma-ray sky has greatly improved. As a result it is now populated with about 2000 sources, both Galactic and extra-galactic, mainly discovered by IBIS and BAT instruments. Many different follow-up campaigns have been successfully performed by using a multi-wavelength approach, shedding light on the nature of a number of these new hard X-ray sources. However, a fraction are still of a unidentified nature. This is mainly due to the lack of lower energy observations, which usually deliver a better constrained position for the sources, and the unavailability of the key observational properties, needed to obtain a proper physical characterization. Here we report on the classification of two poorly studied Galactic X-ray transients IGR J20155+3827 and Swift J1713.4−4219, for which the combination of new and/or archival X-ray and Optical/NIR observations have allowed us to pinpoint their nature. In particular, thanks to XMM-Newton archival data together with new optical spectroscopic and archival Optical/NIR photometric observations, we have been able to classify IGR J20155+3827 as a distant HMXB. The new INTEGRAL and Swift data collected during the 2019 X-ray outburst of Swift J1713.4−4219, in combination with the archival optical/NIR observations, suggest a LMXB classification for this source.

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Transfer Deep Learning for Dental and Maxillofacial Imaging Modality Classification: A Preliminary Study

Journal of Clinical Pediatric Dentistry ◽

10.17796/1053-4625-45.4.3 ◽

2021 ◽

Vol 45 (4) ◽

pp. 233-238

Author(s):

Lazar Kats ◽

Marilena Vered ◽

Johnny Kharouba ◽

Sigalit Blumer

Keyword(s):

Deep Learning ◽

Imaging Modality ◽

Daily Practice ◽

Automatic Classification ◽

Training Dataset ◽

Small Data ◽

Data Set ◽

X Ray ◽

Tel Aviv

Objective: To apply the technique of transfer deep learning on a small data set for automatic classification of X-ray modalities in dentistry. Study design: For solving the problem of classification, the convolution neural networks based on VGG16, NASNetLarge and Xception architectures were used, which received pre-training on ImageNet subset. In this research, we used an in-house dataset created within the School of Dental Medicine, Tel Aviv University. The training dataset contained anonymized 496 digital Panoramic and Cephalometric X-ray images for orthodontic examinations from CS 8100 Digital Panoramic System (Carestream Dental LLC, Atlanta, USA). The models were trained using NVIDIA GeForce GTX 1080 Ti GPU. The study was approved by the ethical committee of Tel Aviv University. Results: The test dataset contained 124 X-ray images from 2 different devices: CS 8100 Digital Panoramic System and Planmeca ProMax 2D (Planmeca, Helsinki, Finland). X-ray images in the test database were not pre-processed. The accuracy of all neural network architectures was 100%. Following a result of almost absolute accuracy, the other statistical metrics were not relevant. Conclusions: In this study, good results have been obtained for the automatic classification of different modalities of X-ray images used in dentistry. The most promising direction for the development of this kind of application is the transfer deep learning. Further studies on automatic classification of modalities, as well as sub-modalities, can maximally reduce occasional difficulties arising in this field in the daily practice of the dentist and, eventually, improve the quality of diagnosis and treatment.

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