Classification Of Sources Research Articles

Automated classification of heat sources detected using SWIR remote sensing

The potential of shortwave infrared (SWIR) remote sensing to detect hotspots has been investigated using satellite data for decades. The hotspots detected by satellite SWIR sensors include very high-temperature heat sources such as wildfires, volcanoes, industrial activity, or open burning. This study proposes an automated classification method of heat source detected utilizing Landsat 8 and Sentinel-2 data. We created training data of heat sources via visual inspection of hotspots detected by Landsat 8. A scheme to classify heat sources for daytime data was developed by combining classification methods based on a Convolutional Neural Network (CNN) algorithm utilizing spatial features and a decision tree algorithm based on thematic land-cover information and our time series detection record. Validation work using 10,959 classification results corresponding to hotspots acquired from May 2017 to July 2019 indicated that the two classification results were in 79.7% agreement. For hotspots where the two classification schemes agreed, the classification was 97.9% accurate. Even when the results of the two classification schemes conflicted, either was correct in 73% of the samples. To improve the accuracy, the heat source category was re-allocated to the most probable category corresponding to the combination of the results from the two methods. Integrating the two approaches achieved an overall accuracy of 92.8%. In contrast, the overall accuracy for heat source classification during nighttime reached 79.3% because only the decision tree-based classification was applicable to limited available data. Comparison with the Visible Infrared Imaging Radiometer Suite (VIIRS) fire product revealed that, despite the limited data acquisition frequency of Landsat 8, regional tendencies in hotspot occurrence were qualitatively appropriate for an annual period on a global scale.

Non-invasive localization of the ectopic foci of focal atrial tachycardia by using ECG signal based sparse decomposition algorithm

Over 95% of supraventricular tachycardia (SVT) can be treated using the electrophysiology study (EPS) and catheter ablation procedure. As The lack of accurate tools to guide electrophysiologists to find the exact location of ectopic foci, the present study was to locate ectopic foci of focal-atrial tachycardia (FAT) via information extracted from simple and non-invasive electrocardiogram (ECG) signals. For this aim, 32 ECG signals were collected from Tehran arrhythmia clinic database, and then independent features were obtained through the independent component analysis (ICA). Subsequently, the location of arrhythmia in the right and left atria was established and classified by sparse decomposition algorithm upon developing a dictionary based on these independent features. This algorithm could represent a spatial–temporal pattern of the input in the form of a combined line of basic functions existing in the dictionary. Over the past few decades, it has been also widely utilized in image compression and restoration as well as source separation and classification. The study results demonstrated that the sparse decomposition algorithm was well able to locate arrhythmias in the right and left atria with a mean accuracy of 93.27 ± 2.78. Since FAT generally emerges in certain anatomical locations, five areas in the right atrium and four areas in the left atrium, were determined as nine classifications in this respect. The accuracy of the results for different classifications was by 70.24% on average. Therefore, it was concluded that ECG signals could estimate the location ectopic foci before EPS in an accurate manner.

Deep learning applications based on SDSS photometric data: detection and classification of sources

ABSTRACT Most astronomical source classification algorithms based on photometric data struggle to classify sources as quasars, stars, and galaxies reliably. To achieve this goal and build a new Sloan Digital Sky Survey photometric catalogue in the future, we apply a deep learning source detection network built on YOLO v4 object detection framework to detect sources and design a new deep learning classification network named APSCnet (astronomy photometric source classification network) to classify sources. In addition, a photometric background image generation network is applied to generate background images in the process of data sets synthesis. Our detection network obtains a mean average precision score of 88.02 when IOU = 0.5. As for APSCnet, in a magnitude range with 14–25, we achieve a precision of 84.1 ${{\ \rm per\ cent}}$ at 93.2 ${{\ \rm per\ cent}}$ recall for quasars, a precision of 94.5 ${{\ \rm per\ cent}}$ at 84.6 ${{\ \rm per\ cent}}$ recall for stars, and a precision of 95.8 ${{\ \rm per\ cent}}$ at 95.1 ${{\ \rm per\ cent}}$ recall for galaxies; and in a magnitude range with less than 20, we achieve a precision of 96.6 ${{\ \rm per\ cent}}$ at 94.7${{\ \rm per\ cent}}$ recall for quasars, a precision of 95.7${{\ \rm per\ cent}}$ at 97.4${{\ \rm per\ cent}}$ recall for stars, and a precision of 98.9 ${{\ \rm per\ cent}}$ at 99.2 ${{\ \rm per\ cent}}$ recall for galaxies. We have proved the superiority of our algorithm in the classification of astronomical sources through comparative experiments between multiple sets of methods. In addition, we also analysed the impact of point spread function on the classification results. These technologies may be applied to data mining of the next generation sky surveys, such as LSST, WFIRST, and CSST etc.

Crossing Media Streams with Sentiment: Domain Adaptation in Blogs, Reviews and Twitter

Most sentiment analysis studies address classification of a single source of data such as reviews or blog posts. However, the multitude of social media sources available for text analysis lends itself naturally to domain adaptation. In this study, we create a dataset spanning three social media sources -- blogs, reviews, and Twitter -- and a set of 37 common topics. We first examine sentiments expressed in these three sources while controlling for the change in topic. Then using this multi-dimensional data we show that when classifying documents in one source (a target source), models trained on other sources of data can be as good as or even better than those trained on the target data. That is, we show that models trained on some social media sources are generalizable to others. All source adaptation models we implement show reviews and Twitter to be the best sources of training data. It is especially useful to know that models trained on Twitter data are generalizable, since, unlike reviews, Twitter is more topically diverse.

Mixed Near-Field and Far-Field Source Localization Based On Convolution Neural Networks via Symmetric Nested Array

In this paper, the convolution neural networks (CNN) are developed for the classification and localization of mixed near-field and far-field sources by using the geometry of symmetric nested array. We first transform the received data into frequency domain. Then, we preprocess the phase difference matrix to decouple mixed sources. Considering that the counter-diagonal elements in the phase difference matrix only contain the direction of arrival (DOA) parameter of each mixed source, we utilize the upper right elements as the input of CNN to estimate the DOA of each mixed source. In order to avoid the influence of noise, we construct a particular range vector without the estimated DOA and employ the output of autoencoder to classify the mixed sources. Finally, we further exploit the output of autoencoder and apply the CNN without bias vectors to estimate the range of near-field sources. In contrast to the traditional learning-based approaches regarding the parameter estimation as a classification problem, the proposed approach considers the parameter estimation as a regression problem and can significantly reduce the complexity of networks. Simulation results demonstrate that the proposed learning-based method can improve the precision for the mixed source localization. Moreover, the proposed method is robust to the off-grid parameters and the different numbers of mixed sources.

A galaxy cluster in the innermost Zone of Avoidance, close to the radio phoenix VLSS J2217.5+5943

Context.Galaxy clusters grow via mergers with other clusters and groups. Extended regions of diffuse radio emission with a steep radio spectral index are thought to be indicators of such merger events. Extended radio sources with a significantly curved spectrum and a complex morphology have been found in several galaxy clusters. It has been proposed that these so-called radio phoenices are witnesses of cluster mergers and of the presence of active galactic nuclei prior to the merger. Shock fronts or turbulence induced by the mergers are believed to reenergize plasma emitted in the past active phase of a galaxy.Aims.The steep spectrum radio source VLSS J2217.5+5943 shows a complex filamentary morphology and a curved spectrum. Therefore, the source has previously been classified as a radio phoenix. However, no galaxy cluster associated with this radio source had confidently been detected until now because the source is located in the direction of the innermost zone of the Galactic plane atb = +2.4°, the innermost Zone of Avoidance (ZoA). The main aim of this work is to identify galaxies that are part of a cluster at the location of VLSS J2217.5+5943, determine their redshifts, and analyze their connection with the radio source. The confirmation of a cluster would corroborate the classification of the radio source as a radio phoenix and demonstrate that extended, diffuse radio sources are useful indicators of the presence of a galaxy cluster, in particular in the innermost ZoA.Methods.We analyzed archival observations in the near infrared and mid infrared (Spitzer) to select the galaxies in the immediate neighborhood of the radio source. A sample of 23 galaxies were selected as candidate cluster members. Furthermore, we carried out deep integral field spectroscopy covering 6450 to 10 500 Å with the red unit of theHobby-EberlyTelescope second generation low resolution spectrograph (LRS2-R). We also reanalyzed archival GMRT observations at 325 and 610 MHz.Results.We selected 23 galaxies within a radius of 2.5 arcmin, centered on RA = 22h17m​.5, Dec = +59° 43′ (J2000). Spectra were obtained for three of the brightest galaxies. For two galaxies we derived redshifts ofz = 0.165 andz = 0.161, based on NaD absorption and TiO band heads. Their spectra correspond to E-type galaxies. Both galaxies are spatially associated with VLSS J2217.5+5943. The spectrum of the third galaxy, which is slightly farther from the radio source, indicates a LINER spectral type atz = 0.042. It is apparently a foreground galaxy with respect to the cluster we identified.Conclusions.VLSS J2217.5+5943 is associated with a massive galaxy cluster at redshiftz = 0.163 ± .003, supporting its classification as a radio phoenix. The intrinsic properties of the radio source, computed for the cluster redshift, are in good agreement with those of other known radio phoenices. The identification of the galaxy cluster demonstrates that far-red spectroscopy with LRS2-R succeeds in determining the redshift of galaxies in the innermost ZoA. Moreover, it confirms that radio sources can be useful indicators of the presence of galaxy clusters in the ZoA.

Audio Edge Processing

Solving the challenges of audio source classification in the Smart Home, by Raj Senguttuvan and Vikram Shrivastava

Sources of Information about the Speech Situation of Insults When Solving Expert Problems

The article deals with a classification of sources of information about the verbal situation of an insult, which is subject to research during the production of a forensic linguistic examination. The objective side of the crimes established by Articles 319 and 297 of the Criminal Code of the Russian Federation is verbal aggression, expressed in insulting a representative of the authorities, participants in the trial, and persons involved in the administration of justice. The author examines various ways of fixing the speech situation of an insult and information about it and also puts forward a hypothesis about the possibility of carrying out a linguistic study of a conflict speech event recorded in the testimony of participants in criminal proceedings while observing a certain order of registration of a procedural document

Artificial intelligence for celestial object census: the latest technology meets the oldest science

Large surveys using modern telescopes are producing images that are increasing exponentially in size and quality. Identifying objects in the generated images by visual recognition is time-consuming and labor-intensive, while classifying the extracted radio sources is even more challenging. To address these challenges, we develop a deep learning-based radio source detector, named \textsc{HeTu}, which is capable of rapidly identifying and classifying radio sources in an automated manner for both compact and extended radio sources. \textsc{HeTu} is based on a combination of a residual network (ResNet) and feature pyramid network (FPN). We classify radio sources into four classes based on their morphology. The training images are manually labeled and data augmentation methods are applied to solve the data imbalance between the different classes. \textsc{HeTu} automatically locates the radio sources in the images and assigns them to one of the four classes. The experiment on the testing dataset shows an average operation time of 5.4 millisecond per image and a precision of 99.4\% for compact point-like sources and 98.1\% for double-lobe sources. We applied \textsc{HeTu} to the images obtained from the GaLactic and the Galactic Extragalactic All-Object Murchison Wide-field Array (GLEAM) survey project. More than 96.9\% of the \textsc{HeTu}-detected compact sources are matched compared to the source finding software used in the GLEAM. We also detected and classified 2,298 extended sources (including Fanaroff-Riley type I and II sources, and core-jet sources) above $5\sigma$. The cross-matching rates of extended sources are higher than 97\%, showing excellent performance of \textsc{HeTu} in identifying extended radio sources. \textsc{HeTu} provides an efficient tool for radio source finding and classification and can be applied to other scientific fields.

Generative Adversarial Networks used for latent space Optimization: a comparative study for the Classification of Partial Discharge Sources

Hydrogenerators are strategic assets for power utilities. Their reliability and availability can lead to significant benefits. For decades, monitoring and diagnosis of hydrogenerators have been at the core of maintenance strategies. The main cause of hydrogenerator breakdown comes from failure of its high voltage stator, which is a major component of hydrogenerators. In our previous study, it was shown that more than 85% of stator failure mechanisms indicate the presence of Partial Discharges (PD) activity. PD are minute sparks that occur within voids inside high voltage insulation or in the air around the insulating system. Each PD event does not cause immediate failure, but it will slowly erode the insulation system and will lead to breakdown in years to decades. PD signal can be detected from the lead of the hydrogenerator while it is running, thus allowing for on-line diagnosis. Hydro-Québec has been collecting more than 33.000 unlabeled PD measurement files over the last decades including two types of measurement instruments. One of the instruments used is the Partial Discharge Analyzer (PDA). PDs have several different sources and each source is characterized by a specific signature. It is therefore essential to be able to automatically recognize the nature of the DPs in order to anticipate possible degradation. Expert rules to characterize these PD signals exist but these rules cannot be used as an automatic classification tool. Indeed, for certain ambiguous cases (conflict between classes) or the presence of several signatures at the same time (multi-classes), the expert's judgement remains essential.
 A Variationel AutoEncoder (VAE) is used for dimension reduction and projection into a 2D latent space to analyze the training data and then classify it. The 2D latent space of the VAE allows the data space to be restructured and reorganized to anticipate the performance of the classifier. The problem is how to optimize this latent space and thus obtain the best distribution of the different sources of PDs to maximize the chances of good responses from the classifier? To our knowledge, there is currently no method in the literature that clearly answers this question. What is certain, however, is that the optimization of the VAE learning is directly related to the quality of the learning base, i.e. a large size and a perfectly balanced database (all the cases are present and sufficiently represented). The objective of this paper is to compare the quality of the latent space obtained from the experts' rules with a latent space obtained directly from the input signal in an « End-to-End » approach.
 The first method concerns an original unsupervised deep learning method for PD recognition. Instead of labelling the PD measurement files by the expert for a supervised learning process, we use the rules developed by the experts of Hydro-Québec to create a feature vector from recognizable PD signatures. Indeed, labelling a sufficient quantity of signatures for a supervised approach is very time-consuming and therefore cannot be implemented. This is a common problem in the industry where more and more operational data is available but cannot be labelled by experts, who are busy with other tasks. Expert knowledge is then injected into a characteristic and feature vector. In the second method, several Generative Adversarial Networks (GANs) associated with several types of PDs are thus used to generate artificial signals in order to increase the size of the learning base and especially to balance it. A new latent space is thus obtained from the learning of the VAE exclusively carried out on data generated by the GANs.
 Validation tests based on the 33,000 measurements with metrics for evaluating the performances of the various latent spaces are used.

Artificial Neural Network classification of 4FGL sources

ABSTRACT The Fermi-LAT DR1 and DR2 4FGL catalogues feature more than 5000 gamma-ray sources of which about one fourth are not associated with already known objects, and approximately one third are associated with blazars of uncertain nature. We perform a three-category classification of the 4FGL DR1 and DR2 sources independently, using an ensemble of Artificial Neural Networks (ANNs) to characterize them based on the likelihood of being a Pulsar (PSR), a BL Lac type blazar (BLL) or a Flat Spectrum Radio Quasar (FSRQ). We identify candidate PSR, BLL, and FSRQ among the unassociated sources with approximate equipartition among the three categories and select 10 classification outliers as potentially interesting for follow-up studies.

Differences in source kitchens for lacustrine in-source and out-of-source hydrocarbon accumulations

Because of the differences of hydrocarbon accumulation between in-source and out-of-source oil pools, the demand for source kitchen is different. Based on the establishment of source-to-reservoir correlation in the known conventional accumulations, and the characteristics of shale oil source kitchens as well, this paper discusses the differences of source kitchens for the formation of both conventional and shale oils. The formation of conventional oil pools is a process of hydrocarbons enriching from disperse state under the action of buoyancy, which enables most of the oil pools to be formed outside the source kitchens. The source rock does not necessarily have high abundance of organic matter, but has to have high efficiency and enough amount of hydrocarbon expulsion. The TOC threshold of source rocks for conventional oil accumulations is 0.5%, with the best TOC window ranging from 1% to 3%. The oil pools formed inside the source kitchens, mainly shale oil, are the retention of oil and gas in the source rock and there is no large-scale hydrocarbon migration and enrichment process happened, which requires better quality and bigger scale of source rocks. The threshold of TOC for medium to high maturity of shale oil is 2%, with the best range falling in 3%–5%. Medium to low mature shale oil resource has a TOC threshold of 6%, and the higher the better in particular. The most favorable kerogen for both high and low-mature shale oils is oil-prone type of I–II1. Carrying out source rock quality and classification evaluation and looking for large-scale and high-quality source rock enrichment areas are a scientific issue that must be paid attention to when exploration activity changes from out-of-source regions to in-source kitchen areas. The purpose is to provide theoretical guidance for the upcoming shale oil enrichment area selection, economic discovery and objective evaluation of resource potential.

Design and Characteristics of Microfocus X-ray Source with Sealed Tube and Transmissive Target on Diamond Window

Electronic devices such as medical instruments implanted in the human body and electronic control units installed in automobiles have a large impact on human life. The electronic circuits in these devices require highly reliable operation. Radiographic testing has recently been in strong demand as a nondestructive way to help ensure high reliability. Companies that use high-density micrometer-scale circuits or lithium-ion batteries require high speed and high magnification inspection of all parts. The authors have developed a new X-ray source supporting these requirements. The X-ray source has a sealed tube with a transmissive target on a diamond window that offers advantages over X-ray sources having a sealed tube with a reflective target. The X-ray source provides high-power-density X-ray with no anode degradation and a longer shelf life. In this paper, the authors will summarize X-ray source classification relevant to electronic device inspection and will detail X-ray source performance requirements and challenges. The paper will also elaborate on technologies employed in the X-ray source including tube design implementations for high-power-density X-ray, high resolution, and high magnification simultaneously; reduced system downtime for automated X-ray inspection; and reduced dosages utilizing quick X-ray on-and-off emission control for protection of sensitive electronic devices.

Diagnostics of mechanical asphyxia – experience of foreign countries (literature review)

The aim of this study is to establish modern, global trends in the diagnosis of mechanical asphyxia.
 Materials and methods: search and bibliographic method (theoretical analysis, systematization and classification of library catalogues, printed and electronic sources on mechanical asphyxia) from open anchor databases Scopus preview, Web of Science and using information retrieval systems Google Scholar, Open Ukrainian Citation Index (OUCI), ScienceDirect on the Internet.
 Results: mechanical asphyxia, as one of the most common types of violent death, ranks first among deaths from mechanical injuries. Its study is of great interest among medical scientists in various fields, especially for resuscitators and forensic experts. Establishing the causes of asphyxia, clinical and morphological manifestations, and the consequences that unfortunately most often lead to death. With the rapid development of society, science is also developing rapidly, and the latest sensitive methods of diagnosing diseases are emerging. However, unfortunately, in the post-Soviet space, the diagnosis of most pathological conditions, including asphyxia, is determined by experience and sensory organs, which are determined using instrumental research methods in compliance with the requirements of evidence-based medicine.
 Given the peculiarities of the pathophysiological processes of the asphyxiation state and their manifestations in the body, doctors should rely primarily on general knowledge about hypoxic and asphyxiation disorders, which are determined using instrumental research methods in compliance with the requirements of evidence-based medicine.
 Conclusions: at present, the diagnosis of asphyxia consists of many morphological features. Failure to take into account the state of the body, the presence of chronic diseases, and drug or alcohol intoxication at the time of asphyxiation complicate the diagnosis. Detection of individual clinical manifestations or morphological features does not allow asserting its lifelong origin. Therefore, it is necessary to use modern research methods that should expand the possibilities of forensic diagnosis of the viability of injuries in terms of evidence-based medicine and provide forensic experts with a scientific basis for their results. One such method is immunohistochemical, which is gaining popularity and consolidating its position in the EU, China, Japan and America. In addition, this method is well-established and widespread in morphological studies of differential diagnosis of tumours. Some scientific works prove the expediency of using the immunohistochemical method to solve the problems of forensic expert practice, which is an actual scientific and practical task

Program Documents as a Source on the History of the Anarchist Movement in Ukraine in the Early 20th Century

The article considers programs and program documents as a historical source on the history of the anarchist movement in Ukraine in the early 20th century. Without a doubt, the historical conditions prevailing in Ukraine and Russia in 1903–1914 influenced the formation and nature of sources. In particular, the absence of a central body and the denial of the movement as a whole affected the core of the source base, marked by a relatively small number of party-organizational documents with particular characteristics. The all-Russian character of anarchist activity also had an impact on the nature of sources. They were distributed in Ukraine, but their contents did not contain any references to specific activities in its territory.The article provides a classification of sources, which are divided into such groups: programs and statutes, congress resolutions, party press, activists’ memoirs and observations, private correspondence. The article describes the programs and program statements of anarchists. The specifics of the program-organizational sources of anarchists are also determined. They are characterized according to the views of anarchists on the organization itself and the party leadership. Not recognizing any organization, considering any leadership a sort of violence against an individual, anarchists advocated full autonomy and independence of groups, interaction on a federal basis.The article characterizes the programs and program statements of anarchists as a historical source. The historical value of documents is determined; their specificity is defined. The research of documents establishes ideological principles, tactics of struggle, ideas about the role of some social groups in the revolution. The studied documents allow establishing the territory of anarchist activity in Ukraine at the beginning of the 20th century.

Demonstrating the influence of sediment source in dredged sediment recovery for brick and tile production

The most important raw materials for the building industry are natural quarried rocks. Preservation of this non-renewable resource needs valorizing products, the vast majority of which is currently considered as waste. Among these, dredged sediments have gained much attention and should play a key role in non-renewable natural resource preservation.The aim of this paper is to achieve a resource-efficient evaluation for the re-use of fine-grained dredged sediments in the heavy clay industry by investigating relationship between sediment provenance and industrial properties. 86 spot samples from French dam reservoirs and estuaries were studied, focusing on grain-size and mineralogy. Industrial technological tests were performed on 53 samples representative of varied lithological sources. Following PCA analysis, source fingerprinting in four categories is proposed, allowing for direct evaluation of sediment for brick-tile making. Best technological scores, close to industrial values, were obtained for source category (3) mixture of sedimentary, plutonic and metamorphic rocks and low scores for two source categories, (1) sedimentary rocks (high carbonate content), (2) plutonic and metamorphic rocks (lack melting compound). Extrusion failed for samples from category (4) plutonic or metamorphic rocks (lack clay minerals). Consistency between PCA fingerprinting categories, MIF (Mineralogical Industrial Framework) and technological tests shows that even though the sediment source classification is simplified, it constitutes a reference to predict the recovery of dredged sediment for tile or brick making. Insights from this study can be particularly useful to the heavy clay industry managers when making decision regarding the fossil resource preservation by substitution with dredged sediments.

The ZTF Source Classification Project. I. Methods and Infrastructure

Abstract The Zwicky Transient Facility (ZTF) has been observing the entire northern sky since the start of 2018 down to a magnitude of 20.5 (5σ for 30 s exposure) in the g, r, and i filters. Over the course of two years, ZTF has obtained light curves of more than a billion sources, each with 50–1000 epochs per light curve in g and r, and fewer in i. To be able to use the information contained in the light curves of variable sources for new scientific discoveries, an efficient and flexible framework is needed to classify them. In this paper, we introduce the methods and infrastructure that will be used to classify all ZTF light curves. Our approach aims to be flexible and modular and allows the use of a dynamical classification scheme and labels, continuously evolving training sets, and the use of different machine-learning classifier types and architectures. With this setup, we are able to continuously update and improve the classification of ZTF light curves as new data become available, training samples are updated, and new classes need to be incorporated.

A deep XMM–Newton observation of the X-Persei-like binary system CXOU J225355.1+624336

We report on the follow-up XMM–Newton observation of the persistent X-ray pulsar CXOU J225355.1+624336, which was discovered with the CATS@BAR project on archival Chandra data. The source was detected at fX(0.5−10 keV) = 3.4 × 10−12 erg cm−2 s−1, a flux level that is fully consistent with previous observations performed with ROSAT, Swift, and Chandra. When compared with previous measurements, the measured pulse period P = 46.753(3) s implies a constant spin down at an average rate of Ṗ = 5.3 × 10−10 s s−1. The pulse profile is energy dependent, showing three peaks at low energy and a less structured profile above about 3.5 keV. The pulsed fraction slightly increases with energy. We described the time-averaged EPIC spectrum with four different emission models: a partially covered power law, a cutoff power law, and a power law with an additional thermal component (either a black body or a collisionally ionised gas). In all cases we obtained equally good fits, so it was not possible to prefer or reject any emission model on a statistical basis. However, we disfavour the presence of thermal components since their modeled X-ray flux, resulting from a region larger than the neutron star surface, would largely dominate the X-ray emission from the pulsar. The phase-resolved spectral analysis showed that a simple flux variation cannot explain the source variability and proved that there is a spectral variability along the pulse phase. The results of the XMM–Newton observation confirmed that CXOU J225355.1+624336 is a Be X-ray binary (BeXB) with a low luminosity (LX ∼ 1034−35 erg s−1), limited variability, and a constant spin down. Therefore, these results reinforce its source classification as a persistent BeXB.

GW190814 follow-up with the optical telescope MeerLICHT

Context. The Advanced LIGO and Virgo gravitational wave observatories detected a signal on 2019 August 14 during their third observing run, named GW190814. A large number of electromagnetic facilities conducted follow-up campaigns in the search for a possible counterpart to the gravitational wave event, which was made especially promising given the early source classification of a neutron star-black hole merger. Aims. We present the results of the GW follow-up campaign taken with the wide-field optical telescope MeerLICHT, located at the South African Astronomical Observatory Sutherland site. We use our results to constrain possible kilonova models. Methods. The MeerLICHT telescope observed more than 95% of the probability localisation each night for over a week in three optical bands (u, q, i) with our initial observations beginning almost two hours after the GW detection. We describe the search for new transients in MeerLICHT data and investigate how our limiting magnitudes can be used to constrain an AT2017gfo-like kilonova. Results. A single new transient was found in our analysis of MeerLICHT data, which we exclude from being the electromagnetic counterpart to GW190814 owing to the existence of a spatially unresolved source at the coordinates of the transient in archival data. Using our limiting magnitudes, the confidence with which we can exclude the presence of an AT2017gfo-like kilonova at the distance of GW190814 was low (< 10−4).

A Morphological Classification Model to Identify Unresolved PanSTARRS1 Sources. II. Update to the PS1 Point Source Catalog

We present an update to the PanSTARRS-1 Point Source Catalog (PS1 PSC), which provides morphological classifications of PS1 sources. The original PS1 PSC adopted stringent detection criteria that excluded hundreds of millions of PS1 sources from the PSC. Here, we adapt the supervised machine learning methods used to create the PS1 PSC and apply them to different photometric measurements that are more widely available, allowing us to add ∼144 million new classifications while expanding the the total number of sources in PS1 PSC by ∼10%. We find that the new methodology, which utilizes PS1 forced photometry, performs ∼6%–8% worse than the original method. This slight degradation in performance is offset by the overall increase in the size of the catalog. The PS1 PSC is used by time-domain surveys to filter transient alert streams by removing candidates coincident with point sources that are likely to be Galactic in origin. The addition of ∼144 million new classifications to the PS1 PSC will improve the efficiency with which transients are discovered.