Detection Of Point Sources Research Articles

A new computational model illuminates the extraordinary eyes of Phronima.

Vision in the midwater of the open ocean requires animals to perform visual tasks quite unlike those of any other environment. These tasks consist of detecting small, low contrast objects and point sources against a relatively dim and uniform background. Deep-sea animals have evolved many extraordinary visual adaptations to perform these tasks. Linking eye anatomy to specific selective pressures, however, is challenging, not least because of the many difficulties of studying deep-sea animals. Computational modelling of vision, based on detailed morphological reconstructions of animal eyes, along with underwater optics, offers a chance to understand the specific visual capabilities of individual visual systems. Prior to the work presented here, comprehensive models for apposition compound eyes in the mesopelagic, the dominant eye form of crustaceans, were lacking. We adapted a model developed for single-lens eyes and used it to examine how different parameters affect the model's ability to detect point sources and extended objects. This new model also allowed us to examine spatial summation as a means to improve visual performance. Our results identify a trade-off between increased depth range over which eyes function effectively and increased distance at which extended objects can be detected. This trade-off is driven by the size of the ommatidial acceptance angle. We also show that if neighbouring ommatidia have overlapping receptive fields, spatial summation helps with all detection tasks, including the detection of bioluminescent point sources. By applying our model to the apposition compound eyes of Phronima, a mesopelagic hyperiid amphipod, we show that the specialisations of the large medial eyes of Phronima improve both the detection of point sources and of extended objects. The medial eyes outperformed the lateral eyes at every modelled detection task. We suggest that the small visual field size of Phronima's medial eyes and the strong asymmetry between the medial and lateral eyes reflect Phronima's need for effective vision across a large depth range and its habit of living inside a barrel. The barrel's narrow aperture limits the usefulness of a large visual field and has allowed a strong asymmetry between the medial and lateral eyes. The model provides a useful tool for future investigations into the visual abilities of apposition compound eyes in the deep sea.

Reference-star differential imaging on SPHERE/IRDIS

Context. Reference-star differential imaging (RDI) is a promising technique in high-contrast imaging that is thought to be more sensitive to exoplanets and disks than angular differential imaging (ADI) at short angular separations (i.e., <0.3’). However, it is unknown whether the performance of RDI on ground-based instruments can be improved by using all the archival data to optimize the subtraction of stellar contributions. Aims. We characterize the performance of RDI on SPHERE/IRDIS data in direct imaging of exoplanets and disks. Methods. We made use of all the archival data in H23 obtained by SPHERE/IRDIS in the past 5 yr to build a master reference library and perform RDI. To avoid biases caused by limited test targets under specific conditions, 32 targets were selected to obtain the average performances of RDI under different conditions, and we compared the performances with those of ADI. Results. In the point-source detection, RDI can outperform ADI at small angular separations (<0.4’) if the observing conditions are around the median conditions of our master reference library. On average, RDI has a gain of ~0.8 mag over ADI at 0.15’ separation for observations under median conditions. We demonstrate that including more reference targets in the master reference library can indeed help to improve the performance of RDI. In disk imaging, RDI can reveal more disk features and provide a more robust recovery of the disk morphology. We resolve 33 disks in total intensity (19 planet-forming disks and 14 debris disks), and 4 of them can only be detected with RDI. Two disks are resolved in scattered light for the first time. Three disks are detected in total intensity for the first time. Conclusions. RDI is a promising imaging technique for ground-based instruments such as SPHERE. The master reference library we built in this work can be easily implemented into legacy or future SPHERE surveys to perform RDI, achieving better performance than that of ADI. To obtain optimal RDI gains over ADI, we recommend future observations be carried out under seeing conditions of 0.6’–0.8’.

A machine learning based approach to gravitational lens identification with the International LOFAR Telescope

ABSTRACT We present a novel machine learning based approach for detecting galaxy-scale gravitational lenses from interferometric data, specifically those taken with the International LOFAR Telescope (ILT), which is observing the northern radio sky at a frequency of 150 MHz, an angular resolution of 350 mas and a sensitivity of 90 μJy beam−1 (1σ). We develop and test several Convolutional Neural Networks to determine the probability and uncertainty of a given sample being classified as a lensed or non-lensed event. By training and testing on a simulated interferometric imaging data set that includes realistic lensed and non-lensed radio sources, we find that it is possible to recover 95.3 per cent of the lensed samples (true positive rate), with a contamination of just 0.008 per cent from non-lensed samples (false positive rate). Taking the expected lensing probability into account results in a predicted sample purity for lensed events of 92.2 per cent. We find that the network structure is most robust when the maximum image separation between the lensed images is ≥3 times the synthesized beam size, and the lensed images have a total flux density that is equivalent to at least a 20σ (point-source) detection. For the ILT, this corresponds to a lens sample with Einstein radii ≥0.5 arcsec and a radio source population with 150 MHz flux densities ≥2 mJy. By applying these criteria and our lens detection algorithm we expect to discover the vast majority of galaxy-scale gravitational lens systems contained within the LOFAR Two Metre Sky Survey.

In-situ γ-ray analysis of ground surface radioactivity using portable HPGe γ spectrometer.

As essential high-end equipment for nuclear emergency monitoring, the portable HPGe γ spectrometer currently lacks supporting in-situ measurement methods, limiting its role and value in emergency missions. For this practical problem, this paper studies the measurement of ground surface radioactivity by portable HPGe γ spectrometer in nuclear emergency monitoring in view of the particularity of nuclear emergency source items. Firstly, the detection efficiency of point sources at different horizontal distances when the spectrometer is installed at the height of 1 m from the center of the detector to the ground is calculated. Secondly, the concept of effective contribution distance is defined and analyzed. Thirdly, the point source detection efficiency is obtained using the numerical integration method of calculation. Integrate to calculate the detection efficiency of the surface source, and then calculate the radioactive surface activity of the surface. Finally, the effectiveness of the method is verified through experiments.

Luminosity functions consistent with a pulsar-dominated Galactic Center excess

A new population of millisecond pulsars is a long-standing proposed explanation for the excess of GeV-scale gamma rays emanating from the region surrounding the center of the Milky Way (the “Galactic Center excess”). We examine several simple parameterizations of possible luminosity functions for this population, as well as several benchmark luminosity functions proposed in the literature, and compare the predicted populations of resolved point sources to the Fermi 4FGL-DR2 point source catalog and a sub-population recently identified using wavelet-based methods. We provide general results that can be used to translate upper limits on the number of resolved point sources associated with the excess, and the fraction of the flux in the excess that can be attributed to resolved sources, into limits on the luminosity function parameter space. We discuss a number of important systematic uncertainties, including in the detection threshold model and the total flux attributed to the excess. We delineate regions of parameter space (containing existing benchmark models) where there is no apparent tension with current data, and the number of total pulsars needed to explain the excess is in the range of \U0001d4aa(104-5). In the future, lowered point source detection thresholds could be achieved either by new analysis methods or new data. An order-of-magnitude reduction in the sensitivity threshold (which may already be achieved by novel analyses probing sub-threshold source populations) could hope to resolve more than 30% of the flux of the excess even in pessimistic scenarios.

Constraining Brans–Dicke Cosmology with the CSST Galaxy Clustering Spectroscopic Survey

The Brans–Dicke (BD) theory is the simplest Scalar-Tensor theory of gravity, which can be considered as a candidate of modified Einstein’s theory of general relativity. In this work, we forecast the constraints on BD theory in the CSST galaxy clustering spectroscopic survey with a magnitude limit ∼23 AB mag for point-source 5σ detection. We generate mock data based on the zCOSMOS catalog and consider the observational and instrumental effects of the CSST spectroscopic survey. We predict galaxy power spectra in the BD theory from z = 0 to 1.5, and the galaxy bias and other systematical parameters are also included. The Markov Chain Monte Carlo technique is employed to find the best-fits and probability distributions of the cosmological and systematical parameters. A BD parameter ζ is introduced, which satisfies . We find that the CSST spectroscopic galaxy clustering survey can give ∣ζ∣ < 10−2, or equivalently and , under the assumption ζ = 0. These constraints are almost at the same order of magnitude compared to the joint constraints using the current cosmic microwave background, baryon acoustic oscillations and Type Ia supernova data, indicating that the CSST galaxy clustering spectroscopic survey would be powerful for constraining the BD theory and other modified gravity theories.

In-situ measurement of surface radioactivity in nuclear emergency response

The in-situ measurement method of ground surface radioactivity based on HPGe γ spectrometer in nuclear emergency response is studied. The key to measuring the surface activity of the ground is the calibrating of detection efficiency, which is obtained by the numerical integration method in this paper. The HPGe γ-spectrometer is set up at the height of 1 m from the ground, with the detector direction vertically downward. First, the experimental method obtains the point source detection efficiency of the spectrometer for different energies of γ-rays at different horizontal distances. Then, 95% of the maximum contribution of the count rate is defined as the effective contribution region. Finally, with the effective contribution region as the integration limit, the point source detection efficiency as the integrand, the detection efficiency of the spectrometer for surface radioactive sources is obtained by numerical integration. The surface activity is calculated based on the detection efficiency. The method is experimentally validated by stitching hexagonal radioactive surface sources with a side length of 1m. The relative deviation of the surface activity results obtained by the numerical integration method from the experimental standard value is 5.48%, which verifies the validity of the method.

Quantifying Regional Methane Emissions in the New Mexico Permian Basin with a Comprehensive Aerial Survey.

Limiting emissions of climate-warming methane from oil and gas (O&G) is a major opportunity for short-term climate benefits. We deploy a basin-wide airborne survey of O&G extraction and transportation activities in the New Mexico Permian Basin, spanning 35 923 km2, 26 292 active wells, and over 15 000 km of natural gas pipelines using an independently validated hyperspectral methane point source detection and quantification system. The airborne survey repeatedly visited over 90% of the active wells in the survey region throughout October 2018 to January 2020, totaling approximately 98 000 well site visits. We estimate total O&G methane emissions in this area at 194 (+72/-68, 95% CI) metric tonnes per hour (t/h), or 9.4% (+3.5%/-3.3%) of gross gas production. 50% of observed emissions come from large emission sources with persistence-averaged emission rates over 308 kg/h. The fact that a large sample size is required to characterize the heavy tail of the distribution emphasizes the importance of capturing low-probability, high-consequence events through basin-wide surveys when estimating regional O&G methane emissions.

On the trail of a comet’s tail: A particle tracking algorithm for comet 67P/Churyumov-Gerasimenko

Context.During the post-perihelion phase of the European Space Agency’s Rosetta mission to comet67P, the Optical, Spectroscopic, and Infrared Remote Imaging System on board the spacecraft took numerous image sequences of the near-nucleus coma, with many showing the motion of individual pieces of debris ejected from active surface areas into space.Aims.We aim to track the motion of individual particles in these image sequences and derive their projected velocities and accelerations. This should help us to constrain their point of origin on the surface, understand the forces that influence their dynamics in the inner coma, and predict whether they will fall back to the surface or escape to interplanetary space.Methods.We have developed an algorithm that tracks the motion of particles appearing as point sources in image sequences. Our algorithm employs a point source detection software to locate the particles and then exploits the image sequences’ pair-nature to reconstruct the particle tracks and derive the projected velocities and accelerations. We also constrained the particle size from their brightness.Results.Our algorithm identified 2268 tracks in a sample image sequence. Manual inspection not only found that 1187 (∼52%) of them are likely genuine, but in combination with runs on simulated data it also revealed a simple criterion related to the completeness of a track to single out a large subset of the genuine tracks without the need for manual intervention. A tentative analysis of a small (n = 89) group of particles exemplifies how our data can be used, and provides first results on the particles’ velocity, acceleration, and radius distributions, which agree with previous work.

Multi-frequency point source detection with fully convolutional networks: Performance in realistic microwave sky simulations

Context. Point source (PS) detection is an important issue for future cosmic microwave background (CMB) experiments since they are one of the main contaminants to the recovery of CMB signal on small scales. Improving its multi-frequency detection would allow us to take into account valuable information otherwise neglected when extracting PS using a channel-by-channel approach. Aims. We aim to develop an artificial intelligence method based on fully convolutional neural networks to detect PS in multi-frequency realistic simulations and compare its performance against one of the most popular multi-frequency PS detection methods, the matrix filters. The frequencies used in our analysis are 143, 217, and 353 GHz, and we imposed a Galactic cut of 30°. Methods. We produced multi-frequency realistic simulations of the sky by adding contaminating signals to the PS maps as the CMB, the cosmic infrared background, the Galactic thermal emission, the thermal Sunyaev-Zel’dovich effect, and the instrumental and PS shot noises. These simulations were used to train two neural networks called flat and spectral MultiPoSeIDoNs. The first one considers PS with a flat spectrum, and the second one is more realistic and general because it takes into account the spectral behaviour of the PS. Then, we compared the performance on reliability, completeness, and flux density estimation accuracy for both MultiPoSeIDoNs and the matrix filters. Results. Using a flux detection limit of 60 mJy, MultiPoSeIDoN successfully recovered PS reaching the 90% completeness level at 58 mJy for the flat case, and at 79, 71, and 60 mJy for the spectral case at 143, 217, and 353 GHz, respectively. The matrix filters reach the 90% completeness level at 84, 79, and 123 mJy. To reduce the number of spurious sources, we used a safer 4σ flux density detection limit for the matrix filters, the same as was used in the Planck catalogues, obtaining the 90% of completeness level at 113, 92, and 398 mJy. In all cases, MultiPoSeIDoN obtains a much lower number of spurious sources with respect to the filtering method. The recovering of the flux density of the detections, attending to the results on photometry, is better for the neural networks, which have a relative error of 10% above 100 mJy for the three frequencies, while the filter obtains a 10% relative error above 150 mJy for 143 and 217 GHz, and above 200 mJy for 353 GHz. Conclusions. Based on the results, neural networks are the perfect candidates to substitute filtering methods to detect multi-frequency PS in future CMB experiments. Moreover, we show that a multi-frequency approach can detect sources with higher accuracy than single-frequency approaches also based on neural networks.

Attitude determination for nano-satellites \u2013 II. Dead reckoning with a multiplicative extended Kalman filter

This paper is the second part of a series of studies discussing a novel attitude determination method for nano-satellites. Our approach is based on the utilization of thermal imaging sensors to determine the direction of the Sun and the nadir with respect to the satellite with sub-degree accuracy. The proposed method is planned to be applied during the Cubesats Applied for MEasuring and LOcalising Transients (CAMELOT) mission aimed at detecting and localizing gamma-ray bursts with an efficiency and accuracy comparable to large gamma-ray space observatories. In our previous work we determined the spherical projection function of the MLX90640 infrasensors planned to be used for this purpose. We showed that with the known projection function the direction of the Sun can be located with an overall accuracy of sim 40^prime. In this paper we introduce a simulation model aimed at testing the applicability of our attitude determination approach. Its first part simulates the orbit and rotation of a satellite with arbitrary initial conditions while its second part applies our attitude determination algorithm which is based on a multiplicative extended Kalman filter. The simulated satellite is assumed to be equipped with a GPS system, MEMS gyroscopes and the infrasensors. These instruments provide the required data input for the Kalman filter. We demonstrate the applicability of our attitude determination algorithm by simulating the motion of a nano-satellite on Low Earth Orbit. Our results show that the attitude determination may have a 1sigma error of sim 30' even with a large gyroscope drift during the orbital periods when the infrasensors provide both the direction of the Sun and the Earth (the nadir). This accuracy is an improvement on the point source detection accuracy of the infrasensors. However, the attitude determination error can get as high as 25^{circ } during periods when the Sun is occulted by the Earth. We show that following an occultation period the attitude information is immediately recovered by the Kalman filter once the Sun is observed again.

An X-Band Code-Modulated Interferometric Imager

Code-modulated interferometry (CMI) enables a lens-less approach to imaging in which incoming signals are code modulated, combined, and processed through a shared hardware path; visibility functions are demodulated from an aggregate power-detected response; and an image is obtained using an inverse Fourier transform of the visibility samples. CMI allows the imager to be constructed using low-cost conventional beamforming hardware. This article presents the theory of operation of a code-modulated interferometer array intended for active imaging. This includes the selection of codes, the use of phase shifters for modulation, the demodulation of visibility functions, the necessary calibration, and the image processing. The architecture and design of an active imaging prototype is then presented, where it is created using a commercially available 16-element 8–16 GHz beamforming receiver along with a sparse antenna array that generates 169 distinct visibility samples. The imaging capabilities are demonstrated through the detection of multiple point sources at 10 GHz. Finally, the feasibility of creating a larger 64-element imager with 961 visibility samples is demonstrated through construction and measurements of a single row within that array.

Identification of point sources in gamma rays using U-shaped convolutional neural networks and a data challenge

Context. At GeV energies, the sky is dominated by the interstellar emission from the Galaxy. With limited statistics and spatial resolution, accurately separating point sources is therefore challenging. Aims. Here we present the first application of deep learning based algorithms to automatically detect and classify point sources from gamma-ray data. For concreteness we refer to this approach as AutoSourceID. Methods. To detect point sources, we utilized U-shaped convolutional networks for image segmentation and k-means for source clustering and localization. We also explored the Centroid-Net algorithm, which is designed to find and count objects. Using two algorithms allows for a cross check of the results, while a combination of their results can be used to improve performance. The training data are based on 9.5 years of exposure from The Fermi Large Area Telescope (Fermi-LAT) and we used source properties of active galactic nuclei (AGNs) and pulsars (PSRs) from the fourth Fermi-LAT source catalog in addition to several models of background interstellar emission. The results of the localization algorithm are fed into a classification neural network that is trained to separate the three general source classes (AGNs, PSRs, and FAKE sources). Results. We compared our localization algorithms qualitatively with traditional methods and find them to have similar detection thresholds. We also demonstrate the robustness of our source localization algorithms to modifications in the interstellar emission models, which presents a clear advantage over traditional methods. The classification network is able to discriminate between the three classes with typical accuracy of ∼70%, as long as balanced data sets are used in classification training. We published online our training data sets and analysis scripts and invite the community to join the data challenge aimed to improve the localization and classification of gamma-ray point sources.

Catalog of NOx emissions from point sources as derived from the divergence of the NO2 flux for TROPOMI

Abstract. We present version 1.0 of a global catalog of NOx emissions from point sources, derived from TROPOspheric Monitoring Instrument (TROPOMI) measurements of tropospheric NO2 for 2018–2019. The identification of sources and quantification of emissions are based on the divergence (spatial derivative) of the mean horizontal flux, which is highly sensitive for point sources like power plant exhaust stacks. The catalog lists 451 locations which could be clearly identified as NOx point sources by a fully automated algorithm, while ambiguous cases as well as area sources such as megacities are skipped. A total of 242 of these point sources could be automatically matched to power plants. Other NOx point sources listed in the catalog are metal smelters, cement plants, or industrial areas. The four largest localized NOx emitters are all coal combustion plants in South Africa. About 1/4 of all detected point sources are located in the Indian subcontinent and are mostly associated with power plants. The catalog is incomplete, mainly due to persisting gaps in the TROPOMI NO2 product at some coastlines, inaccurate or complex wind fields in coastal and mountainous regions, and high noise in the divergence maps for high background pollution. The derived emissions are generally too low, lacking a factor of about 2 up to 8 for extreme cases. This strong low bias results from combination of different effects, most of all a strong underestimation of near-surface NO2 in TROPOMI NO2 columns. Still, the catalog has high potential for checking and improving emission inventories, as it provides accurate and independent up-to-date information on the location of sources of NOx and thus also CO2. The catalog of NOx emissions from point sources is freely available at https://doi.org/10.26050/WDCC/Quant_NOx_TROPOMI (Beirle et al., 2020).

Gas clumping in the outskirts of the Virgo cluster

ABSTRACT Observations of the intracluster medium (ICM) in the outskirts of the Virgo cluster with Suzaku have found the gas mass fraction in the northern direction to be significantly above the expected level, indicating that there may be a very high level of gas clumping on small scales in this direction. Here we explore the XMM–Newton data in the outskirts of Virgo, dividing it into a Voronoi tessellation to separate the bulk ICM component from the clumped ICM component. As the nearest galaxy cluster, Virgo’s large angular extent allows the spatial scale of the tessellation to be much smaller than has been achieved using the same technique on intermediate-redshift clusters, allowing us to probe gas clumping on the scales down to 5 × 5 kpc2. We find that the level of gas clumping in the outskirts to the north is relatively mild, ($\sqrt{C} \lt 1.1$), suggesting that our point-source detection procedure may have excluded a significant fraction of clumps. While correcting for clumping brings the gas mass fraction at r200 into agreement with the universal gas mass fraction, the values outside r200 remain significantly above it. This may suggest that non-thermal pressure support in the outskirts to the north is significant, and we find that a non-thermal pressure support at the level of 20 per cent of the total pressure outside r200 can explain the high gas mass fraction to the north, which is in agreement with the range expected from simulations.

High-frequency monitoring of anomalous methane point sources with multispectral Sentinel-2 satellite observations

Abstract. We demonstrate the capability of the Sentinel-2 MultiSpectral Instrument (MSI) to detect and quantify anomalously large methane point sources with fine pixel resolution (20 m) and rapid revisit rates (2–5 d). We present three methane column retrieval methods that use shortwave infrared (SWIR) measurements from MSI spectral bands 11 (∼ 1560–1660 nm) and 12 (∼ 2090–2290 nm) to detect atmospheric methane plumes. The most successful is the multi-band–multi-pass (MBMP) method, which uses a combination of the two bands and a non-plume reference observation to retrieve methane columns. The MBMP method can quantify point sources down to about 3 t h−1 with a precision of ∼ 30 %–90 % (1σ) over favorable (quasi-homogeneous) surfaces. We applied our methods to perform high-frequency monitoring of strong methane point source plumes from a well-pad device in the Hassi Messaoud oil field of Algeria (October 2019 to August 2020, observed every 2.5 d) and from a compressor station in the Korpezhe oil and gas field of Turkmenistan (August 2015 to November 2020, observed every 5 d). The Algerian source was detected in 93 % of cloud-free scenes, with source rates ranging from 2.6 to 51.9 t h−1 (averaging 9.3 t h−1) until it was shut down by a flare lit in August 2020. The Turkmen source was detected in 40 % of cloud-free scenes, with variable intermittency and a 9-month shutdown period in March–December 2019 before it resumed; source rates ranged from 3.5 to 92.9 t h−1 (averaging 20.5 t h−1). Our source-rate retrievals for the Korpezhe point source are in close agreement with GHGSat-D satellite observations for February 2018 to January 2019, but provide much higher observation density. Our methods can be readily applied to other satellite instruments with coarse SWIR spectral bands, such as Landsat-7 and Landsat-8. High-frequency satellite-based detection of anomalous methane point sources as demonstrated here could enable prompt corrective action to help reduce global methane emissions.

Point source detection with fully convolutional networks

Context. Point sources are one of the main contaminants to the recovery of the cosmic microwave background signal at small scales, and their careful detection will be important for the next generation of cosmic microwave background experiments like LiteBird. Aims. We want to develop a method based on fully convolutional networks to detect sources in realistic simulations, and to compare its performance against one of the most used point source detection method in this context, the Mexican hat wavelet 2 (MHW2). The frequencies for our analysis are the 143, 217, and 353 GHz Planck channels. Methods. We produce realistic simulations of point sources at each frequency taking into account potential contaminating signals as the cosmic microwave background, the cosmic infrared background, the Galactic thermal emission, the thermal Sunyaev-Zel’dovich effect, and the instrumental and point source shot noises. We first produce a set of training simulations at 217 GHz to train the neural network that we named PoSeIDoN. Then we apply both PoSeIDoN and the MHW2 to recover the point sources in the validating simulations at all the frequencies, comparing the results by estimating the reliability, completeness, and flux density estimation accuracy. Moreover, the receiver operating characteristic (ROC) curves are computed in order to asses the methods’performance. Results. In the extra-galactic region with a 30° galactic cut, the neural network successfully recovers point sources at 90% completeness corresponding to 253, 126, and 250 mJy for 143, 217, and 353 GHz respectively. In the same validation simulations the wavelet with a 3σ flux density detection limit recovers point sources up to 181, 102, and 153 mJy at 90% completeness. To reduce the number of spurious sources, we also apply a safer 4σ flux density detection limit, the same as in the Planck catalogues, increasing the 90% completeness levels: 235, 137, and 192 mJy. In all cases PoSeIDoN produces a much lower number of spurious sources with respect to MHW2. As expected, the results on spurious sources for both techniques worsen when reducing the galactic cut to 10°. Conclusions. Our results suggest that using neural networks is a very promising approach for detecting point sources using data from cosmic microwave background experiments, providing overall better results in dealing with spurious sources with respect to the more usual filtering approaches. Moreover, PoSeIDoN gives competitive results even at the 217 GHz nearby channels where the network was not trained.

Atmospheric ammonia point source detection technique at regional scale using high resolution satellite imagery and deep learning

Ammonia (NH3) is a precursor of atmospheric aerosols. The lack of real-time updated and high spatial resolution atmospheric NH3 point source information is one of the critical problems in the study of atmospheric pollution. Satellite-based NH3 observation data provide a new approach to NH3 point source detection. However current spatial resolution of NH3 concentration satellite data is still not sufficient to extract NH3 point sources at regional scale with high accuracy. Based on the traditional oversampling method to improve the spatial resolution of NH3 satellite data, this paper used high-resolution satellite image data with the geometric morphological features of NH3 point sources to detect the NH3 point sources at regional scale under deep learning techniques. The high-precision NH3 point source location and category information at regional scale is extracted concurrently, solving the current problem that multiple point sources cannot be detected in a single satellite NH3 concentration hotspot. After analyzing a priori knowledge, the industrial sources were further subdivided. The atmospheric ammonia point source detection technique in this paper were used in Northeast China, where the number and types of NH3 emission sources are abundant. The detection results significantly increased the number of NH3 potential point sources detected in Northeast China compared with the traditional oversampling method. The deep learning-based technology combines NH3 concentration remote sensing products with high resolution satellite image data to effectively detect the spatial distribution and types of NH3 point sources at regional scale, providing powerful technical support for NH3 emission regulation and control.

Characterization of extragalactic point-sources on E- and B-mode maps of the CMB polarization

Although interesting in themselves, extragalactic sources emitting in the microwave range (mainly radio-loud active galactic nuclei and dusty galaxies) are also considered a contaminant from the point of view of Cosmic Microwave Background (CMB) experiments. These sources appear as unresolved point-like objects in CMB measurements because of the limited resolution of CMB experiments. Amongst other issues, point-like sources are known to obstruct the reconstruction of the lensing potential, and can hinder the detection of the Primordial Gravitational Wave Background for low values of r. Therefore, extragalactic point-source detection and subtraction is a fundamental part of the component separation process necessary to achieve some of the science goals set for the next generation of CMB experiments. As a previous step to their removal, in this work we present a new filter based on steerable wavelets that allows the characterization of the emission of these extragalactic sources. Instead of the usual approach of working in polarization maps of the Stokes' Q and U parameters, the proposed filter operates on E- and B-mode polarization maps. In this way, it benefits from the lower intensity that, both, the CMB, and the galactic foreground emission, present in B-modes to improve its performance. To demonstrate its potential, we have applied the filter to simulations of the future PICO satellite, and we predict that, for the regions of fainter galactic foreground emission in the 30 GHz and 155 GHz bands of PICO, our filter will be able to characterize sources down to a minimum polarization intensity of, respectively, 125 pK and 14 pK. Adopting a Π=0.02 polarization degree, these values correspond to 169 mJy and 288 mJy intensities.

Probing ultradiffuse galaxies out to the virial radius of the Coma cluster with XMM–Newton

ABSTRACT We probe the formation scenarios and the active galactic nucleus (AGN) occupation fraction of ultradiffuse galaxies (UDGs) in the nearby Coma cluster by utilizing XMM–Newton observations of 779 out of 854 UDG candidates identified by the Subaru survey. Their origin is probed by measuring the dark matter halo mass of the stacked sample of UDGs and the population of low-mass X-ray binaries residing in globular clusters. Our measurements suggest that the average UDG population does not have a substantial amount of hot gas or a large number of globular clusters. This supports the formation scenario, in which UDGs are puffed-up dwarf galaxies, agreeing with that obtained for 404 Coma cluster UDGs using Chandra. We also determine AGN occupation fraction of UDGs by cross-correlating the position of UDGs with the detected point sources in Coma. We detect three X-ray sources with detection significance σ ≥ 5 that could be off-centre AGNs within 5 arcsec from the centre of the UDG 317, UDG 432, and UDG 535. We identify an optical counterpart for the X-ray source associated with the UDG 317, suggesting that this source is more likely an off-centre AGN. Based on the current data, however, we cannot conclusively constrain whether the detected AGN is residing in the Coma cluster or not.