Detection Of Point Sources Research Articles

AGN Triality of Triple Mergers: Detection of Faint X-Ray Point Sources

Abstract We present results from our X-ray analysis of a systematic search for triple active galactic nuclei (AGNs) in nearby (z < 0.077) triple galaxy mergers. We analyze archival Chandra observations of seven triple galaxy mergers with baymax (Bayesian Analysis of Multiple AGNs in X-rays), fitting each observation with single, dual, and triple X-ray point-source models. In doing so, we conclude that one triple merger has one X-ray point source (SDSS J0858+1822, although it is unlikely to be an AGN), five triple mergers are likely composed of two X-ray point sources (NGC 3341, SDSS J1027+1749, SDSS J1631+2352, SDSS J1708+2153, and SDSS J2356−1016), and one system is composed of three X-ray point sources (SDSS J0849+1114). By fitting the individual X-ray spectra of each point source, we analyze the 2−7 keV luminosities, as well as the levels of obscuration associated with each potential AGN. We find that 4/5 dual X-ray point-source systems have primary and secondary point sources with bright X-ray luminosities (L 2−7kev > 1040 erg s−1), possibly associated with four new undetected dual AGNs. The dual and triple-point-source systems are found to have physical separations between 3 and 9 kpc and flux ratios between 2 × 10−3 and 0.84. A multiwavelength analysis to determine the origin of the X-ray point sources discovered in this work is presented in our companion paper (Foord et al. 2020c).

Surface-Scanning Raman Characterization of Dark Materials Based on a Digital Mirror Device (DMD)

When Raman spectroscopy is used for material analysis, a laser is focused on the sample by a lens. Dark materials have a stronger ability to absorb light and hence the laser may heat these samples to the ignition point. Thus, Raman spectroscopy of dark materials is a challenging problem. High-frequency surface scanning technology based on a digital mirror device and Lissajous pattern modulation is presented in this study. The scanning was performed according to a given path in the detection process and the sampling area was changed from point source detection to area source detection. In addition, the optical structure of the handheld Raman spectrometer was optimized by changing relevant optical components. In addition, the detection efficiency and detection limit were improved so that its performance was significantly enhanced.

Nonparametric Estimation of Galaxy Cluster Emissivity and Detection of Point Sources in Astrophysics With Two Lasso Penalties

Astrophysicists are interested in recovering the three-dimensional gas emissivity of a galaxy cluster from a two-dimensional telescope image. Blurring and point sources make this inverse problem harder to solve. The conventional approach requires in a first step to identify and mask the point sources. Instead we model all astrophysical components in a single Poisson generalized linear model. To enforce sparsity on the parameters, maximum likelihood estimation is regularized with two penalties with weights λ 1 for the radial emissivity and λ 2 for the point sources. The method has the advantage of not employing cross-validation to select λ 1 and λ 2. To judge the significance of interesting features, we quantify uncertainty with the bootstrap. We apply our method to two X-ray telescopes (XMM-Newton and Chandra) data to estimate gas emissivity. The results are more stable and seems less biased than the conventional method, in particular in the outskirt of galaxy clusters. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.

The C-Band All-Sky Survey: total intensity point-source detection over the northern sky

ABSTRACT We present a point-source detection algorithm that employs the second-order Spherical Mexican Hat wavelet filter (SMHW2), and use it on C-Band All-Sky Survey (C-BASS) northern intensity data to produce a catalogue of point sources. This catalogue allows us to cross-check the C-BASS flux-density scale against existing source surveys, and provides the basis for a source mask that will be used in subsequent C-BASS and cosmic microwave background (CMB) analyses. The SMHW2 allows us to filter the entire sky at once, avoiding complications from edge effects arising when filtering small sky patches. The algorithm is validated against a set of Monte Carlo simulations, consisting of diffuse emission, instrumental noise, and various point-source populations. The simulated source populations are successfully recovered. The SMHW2 detection algorithm is used to produce a $4.76\, \mathrm{GHz}$ northern sky source catalogue in total intensity, containing 1784 sources and covering declinations δ ≥ −10°. The C-BASS catalogue is matched with the Green Bank 6 cm (GB6) and Parkes-MIT-NRAO (PMN) catalogues over their areas of common sky coverage. From this we estimate the 90 per cent completeness level to be approximately $610\, \mathrm{mJy}$, with a corresponding reliability of 98 per cent, when masking the brightest 30 per cent of the diffuse emission in the C-BASS northern sky map. We find the C-BASS and GB6 flux-density scales to be consistent with one another to within approximately 4 per cent.

PACO ASDI: an algorithm for exoplanet detection and characterization in direct imaging with integral field spectrographs

Context.Exoplanet detection and characterization by direct imaging both rely on sophisticated instruments (adaptive optics and coronagraph) and adequate data processing methods. Angular and spectral differential imaging (ASDI) combines observations at different times and a range of wavelengths in order to separate the residual signal from the host star and the signal of interest corresponding to off-axis sources.Aims.Very high contrast detection is only possible with an accurate modeling of those two components, in particular of the background due to stellar leakages of the host star masked out by the coronagraph. Beyond the detection of point-like sources in the field of view, it is also essential to characterize the detection in terms of statistical significance and astrometry and to estimate the source spectrum.Methods.We extend our recent methodPACO, based on local learning of patch covariances, in order to capture the spectral and temporal fluctuations of background structures. From this statistical modeling, we build a detection algorithm and a spectrum estimation method:PACO ASDI. The modeling of spectral correlations proves useful both in reducing detection artifacts and obtaining accurate statistical guarantees (detection thresholds and photometry confidence intervals).Results.An analysis of several ASDI datasets from the VLT/SPHERE-IFS instrument shows thatPACO ASDIproduces very clean detection maps, for which setting a detection threshold is statistically reliable. Compared to other algorithms used routinely to exploit the scientific results of SPHERE-IFS, sensitivity is improved and many false detections can be avoided. Spectrally smoothed spectra are also produced byPACO ASDI. The analysis of datasets with injected fake planets validates the recovered spectra and the computed confidence intervals.Conclusions.PACO ASDIis a high-contrast processing algorithm accounting for the spatio-spectral correlations of the data to produce statistically-grounded detection maps and reliable spectral estimations. Point source detections, photometric and astrometric characterizations are fully automatized.

Multiband Probabilistic Cataloging: A Joint Fitting Approach to Point-source Detection and Deblending

Abstract Probabilistic cataloging (PCAT) outperforms traditional cataloging methods on single-band optical data in crowded fields. We extend our work to multiple bands, achieving greater sensitivity (∼0.4 mag) and greater speed (500×) compared to previous single-band results. We demonstrate the effectiveness of multiband PCAT on mock data, in terms of both recovering accurate posteriors in the catalog space and directly deblending sources. When applied to Sloan Digital Sky Survey (SDSS) observations of M2, taking Hubble Space Telescope data as truth, our joint fit on r- and i-band data goes ∼0.4 mag deeper than single-band probabilistic cataloging and has a false discovery rate less than 20% for F606W ≤ 20. Compared to DAOPHOT, the two-band SDSS catalog fit goes nearly 1.5 mag deeper using the same data and maintains a lower false discovery rate down to F606W ∼ 20.5. Given recent improvements in computational speed, multiband PCAT shows promise in application to large-scale surveys and is a plausible framework for joint analysis of multi-instrument observational data. https://github.com/RichardFeder/multiband_pcat.

Methane Mapping with Future Satellite Imaging Spectrometers

This study evaluates a new generation of satellite imaging spectrometers to measure point source methane emissions from anthropogenic sources. We used the Airborne Visible and Infrared Imaging Spectrometer Next Generation(AVIRIS-NG) images with known methane plumes to create two simulated satellite products. One simulation had a 30 m spatial resolution with ~200 Signal-to-Noise Ratio (SNR) in the Shortwave Infrared (SWIR) and the other had a 60 m spatial resolution with ~400 SNR in the SWIR; both products had a 7.5 nm spectral spacing. We applied a linear matched filter with a sparsity prior and an albedo correction to detect and quantify the methane emission in the original AVIRIS-NG images and in both satellite simulations. We also calculated an emission flux for all images. We found that all methane plumes were detectable in all satellite simulations. The flux calculations for the simulated satellite images correlated well with the calculated flux for the original AVIRIS-NG images. We also found that coarsening spatial resolution had the largest impact on the sensitivity of the results. These results suggest that methane detection and quantification of point sources will be possible with the next generation of satellite imaging spectrometers.

Potential of next-generation imaging spectrometers to detect and quantify methane point sources from space

Abstract. We examine the potential for global detection of methane plumes from individual point sources with the new generation of spaceborne imaging spectrometers (EnMAP, PRISMA, EMIT, SBG, CHIME) scheduled for launch in 2019–2025. These instruments are designed to map the Earth's surface at high spatial resolution (30 m×30 m) and have a spectral resolution of 7–10 nm in the 2200–2400 nm band that should also allow useful detection of atmospheric methane. We simulate scenes viewed by EnMAP (10 nm spectral resolution, 180 signal-to-noise ratio) using the EnMAP end-to-end simulation tool with superimposed methane plumes generated by large-eddy simulations. We retrieve atmospheric methane and surface reflectivity for these scenes using the IMAP-DOAS optimal estimation algorithm. We find an EnMAP precision of 3 %–7 % for atmospheric methane depending on surface type. This allows effective single-pass detection of methane point sources as small as 100 kg h−1 depending on surface brightness, surface homogeneity, and wind speed. Successful retrievals over very heterogeneous surfaces such as an urban mosaic require finer spectral resolution. We tested the EnMAP capability with actual plume observations over oil/gas fields in California from the Airborne Visible/Infrared Imaging Spectrometer – Next Generation (AVIRIS-NG) sensor (3 m×3 m pixel resolution, 5 nm spectral resolution, SNR 200–400), by spectrally and spatially downsampling the AVIRIS-NG data to match EnMAP instrument specifications. Results confirm that EnMAP can successfully detect point sources of ∼100 kg h−1 over bright surfaces. Source rates inferred with a generic integrated mass enhancement (IME) algorithm were lower for EnMAP than for AVIRIS-NG. Better agreement may be achieved with a more customized IME algorithm. Our results suggest that imaging spectrometers in space could play an important role in the future for quantifying methane emissions from point sources worldwide.

Detecting Planets from Direct-imaging Observations Using Common Spatial Pattern Filtering

Abstract We present the application of common spatial pattern (CSP) filtering to the detection of point sources in high-contrast astronomical images. The data are preprocessed in two different ways: one copy of the data set keeps the point source in the same spatial location over many images through time, while a second copy of the data set moves the point source azimuthally through the image as in angular differential imaging. The differences between these two data sets are exploited via CSP filtering, highlighting the point source. We develop a forward model for this new process, which is then used to predict the result of applying this method to data with a point source at a particular spatial location. We validate the forward model numerically and use the prediction as template in a matched filter with the actual CSP modes. We present results for multiple sets of observational data and show that the new CSP forward model matched filter (FMMF) can compare favorably to the state-of-the-art Karhunen–Loève image processing FMMF reduction in terms of signal-to-noise ratio and unfavorably in other metrics.

Multiple testing of local maxima for detection of peaks on the (celestial) sphere

We present a topological multiple testing scheme for detecting peaks on the sphere under isotropic Gaussian noise, where tests are performed at local maxima of the observed field filtered by the spherical needlet transform. Our setting is different from the standard Euclidean large domain asymptotic framework, yet highly relevant to realistic experimental circumstances for some important areas of application in astronomy, namely point-source detection in cosmic Microwave Background radiation (CMB) data. Motivated by this application, we shall focus on cases where a single realization of a smooth isotropic Gaussian random field on the sphere is observed, and a number of well-localized signals are superimposed on such background field. The proposed algorithms, combined with the Benjamini–Hochberg procedure for thresholding p-values, provide asymptotic control of the False Discovery Rate (FDR) and power consistency as the signal strength and the frequency of the needlet transform get large.

Point source detection and false discovery rate control on CMB maps

Abstract We discuss the STEM (Smoothing and Testing Multiple hypotheses) procedure to search for point sources in Cosmic Microwave background maps; in particular, we aim at controlling the so-called False Discovery Rate, which is defined as the expected value of false discoveries among pixels which are labeled as contaminated by point sources. STEM is based on the following four steps: (1) needlet filtering of the observed CMB maps, to improve the signal to noise ratio; (2) selection of candidate peaks, i.e., the local maxima of filtered maps; (3) computation of p-values for local maxima; (4) implementation of the multiple testing procedure, by means of the so-called Benjamini–Hochberg method. These procedures are also implemented on the latest release of Planck CMB maps.

Probing the unidentified Fermi blazar-like population using optical polarization and machine learning

ABSTRACT The Fermi γ-ray space telescope has revolutionized our view of the γ-ray sky and the high-energy processes in the Universe. While the number of known γ-ray emitters has increased by orders of magnitude since the launch of Fermi, there is an ever increasing number of, now more than a thousand, detected point sources whose low-energy counterpart is to this day unknown. To address this problem, we combined optical polarization measurements from the RoboPol survey as well as other discriminants of blazars from publicly available all-sky surveys in machine learning (ML, random forest and logistic regression) frameworks that could be used to identify blazars in the Fermi unidentified fields with an accuracy of >95 per cent. Out of the potential observational biases considered, blazar variability seems to have the most significant effect reducing the predictive power of the frameworks to ${\sim }80\hbox{-}85{{\ \rm per\ cent}}$. We apply our ML framework to six unidentified Fermi fields observed using the RoboPol polarimeter. We identified the same candidate source proposed by Mandarakas et al. for 3FGL J0221.2 + 2518.

Enhanced n-body annihilation of dark matter and its indirect signatures

We examine the possible indirect signatures of dark matter annihilation processes with a non-standard scaling with the dark matter density, and in particular the case where more than two dark matter particles participate in the annihilation process. We point out that such processes can be strongly enhanced at low velocities without violating unitarity, similar to Sommerfeld enhancement in the standard case of two-body annihilation, potentially leading to visible signals in indirect searches. We study in detail the impact of such multi-body annihilations on the ionization history of the universe and consequently the cosmic microwave background, and find that unlike in the two-body case, the dominant signal can naturally arise from the end of the cosmic dark ages, after the onset of structure formation. We examine the complementary constraints from the Galactic Center, Galactic halo, and galaxy clusters, and outline the circumstances under which each search would give rise to the strongest constraints. We also show that if there is a population of ultra-compact dense dark matter clumps present in the Milky Way with sufficiently steep density profile, then it might be possible to detect point sources illuminated by multi-body annihilation, even if there is no large low-velocity enhancement. Finally, we provide a case study of a model where 3-body annihilation dominates the freezeout process, and in particular the resonant regime where a large low-velocity enhancement is naturally generated.

DeepSource: point source detection using deep learning

Point source detection at low signal-to-noise is challenging for astronomical surveys, particularly in radio interferometry images where the noise is correlated. Machine learning is a promising solution, allowing the development of algorithms tailored to specific telescope arrays and science cases. We present DeepSource - a deep learning solution - that uses convolutional neural networks to achieve these goals. DeepSource enhances the Signal-to-Noise Ratio (SNR) of the original map and then uses dynamic blob detection to detect sources. Trained and tested on two sets of 500 simulated 1 deg x 1 deg MeerKAT images with a total of 300,000 sources, DeepSource is essentially perfect in both purity and completeness down to SNR = 4 and outperforms PyBDSF in all metrics. For uniformly-weighted images it achieves a Purity x Completeness (PC) score at SNR = 3 of 0.73, compared to 0.31 for the best PyBDSF model. For natural-weighting we find a smaller improvement of ~40% in the PC score at SNR = 3. If instead we ask where either of the purity or completeness first drop to 90%, we find that DeepSource reaches this value at SNR = 3.6 compared to the 4.3 of PyBDSF (natural-weighting). A key advantage of DeepSource is that it can learn to optimally trade off purity and completeness for any science case under consideration. Our results show that deep learning is a promising approach to point source detection in astronomical images.

Acoustic propagation characteristics of heteromorphic metamaterials

To explore the propagation characteristics of acoustic waves in heterogeneous acoustic materials, we studied the propagation of acoustic waves in resonant phononic crystals. We identified the vibration mechanism of two-dimensional three-component localized resonant phononic crystals. Using the finite element software COMSOL, an acoustic propagation model based on acoustic metamaterials was constructed and the local resonance characteristics of acoustic waves and the original cells were used to simulate multiple acoustic models based on triangular arrays. We found that the planar point-like and linear excitation sources incident from the bottom edge of the triangular array model converge to the focal point at the top corner after being controlled by the model. The low-loss movement effect of the point source could be achieved in a rectangular model with a triangular array. A reversal of the transmission of plane acoustic waves resulted when two or three identical triangular arrays were combined into a parallelogram or a trapezoid. This series of abnormal acoustic phenomena provides new directions for the detection of point sources, low-loss directional transmission of acoustic waves, and acoustic stealth.

NEW PHOTOMETRIC PIPELINE TO EXPLORE TEMPORAL AND SPATIAL VARIABILITY WITH KMTNET DEEP-SOUTH OBSERVATIONS

The DEEP-South photometric census of small Solar System bodies produces massive time-series data of variable, transient or moving objects as a by-product. To fully investigate unexplored variable phenomena, we present an application of multi-aperture photometry and FastBit indexing techniques for faster access to a portion of the DEEP-South year-one data. Our new pipeline is designed to perform automated point source detection, robust high-precision photometry and calibration of non-crowded fields which have overlap with previously surveyed areas. In this paper, we show some examples of catalog-based variability searches to find new variable stars and to recover targeted asteroids. We discover 21 new periodic variables with period ranging between 0.1 and 31 days, including four eclipsing binary systems (detached, over-contact, and ellipsoidal variables), one white dwarf/M dwarf pair candidate, and rotating variable stars. We also recover astrometry ($<\pm$1--2 arcsec level accuracy) and photometry of two targeted near-earth asteroids, 2006 DZ169 and 1996 SK, along with the small- ($\sim$0.12 mag) and relatively large-amplitude ($\sim$0.5 mag) variations of their dominant rotational signals in $R$-band.

The Expansion of the Young Supernova Remnant 0509-68.7 (N103B)

Abstract We present a second epoch of Chandra observations of the Type Ia Large Magellanic Cloud supernova remnant (SNR) 0509-68.7 (N103B) obtained in 2017. When combined with the earlier observations from 1999, we have a 17.4 year baseline with which we can search for evidence of the remnant’s expansion. Although the lack of strong point source detections makes absolute image alignment at the necessary accuracy impossible, we can measure the change in the diameter and the area of the remnant, and find that it has expanded by an average velocity of 4170 (2860, 5450) km s−1. This supports the picture of this being a young remnant; this expansion velocity corresponds to an undecelerated age of 850 years, making the real age somewhat younger, consistent with results from light echo studies. Previous infrared observations have revealed high densities in the western half of the remnant, likely from circumstellar material, so it is probable that the real expansion velocity is lower on that side of the remnant and higher on the eastern side. A similar scenario is seen in Kepler’s SNR. N103B joins the rare class of Magellanic Cloud SNRs with measured proper motions.

X-UDS: The Chandra Legacy Survey of the UKIDSS Ultra Deep Survey Field

We present the X-UDS survey, a set of wide and deep Chandra observations of the Subaru-XMM Deep/UKIDSS Ultra Deep Survey (SXDS/UDS) field. The survey consists of 25 observations that cover a total area of 0.33 deg$^{2}$. The observations are combined to provide a nominal depth of ~600 ksec in the central 100 arcmin$^{2}$ region of the field that has been imaged with Hubble/WFC3 by the CANDELS survey and $\sim$200 ksec in the remainder of the field. In this paper, we outline the survey's scientific goals, describe our observing strategy, and detail our data reduction and point source detection algorithms. Our analysis has resulted in a total of 868 band-merged point sources detected with a false-positive Poisson probability of $<1\times10^{-4}$. In addition, we present the results of an X-ray spectral analysis and provide best-fitting neutral hydrogen column densities, $N_{\rm H}$, as well as a sample of 51 Compton-thick active galactic nucleus candidates. Using this sample, we find the intrinsic Compton-thick fraction to be 30-35% over a wide range in redshift ($z=0.1-3$), suggesting the obscured fraction does not evolve very strongly with epoch. However, if we assume that the Compton-thick fraction is dependent on luminosity, as is seen for Compton-thin sources, then our results are consistent with a rise in the obscured fraction out to $z\sim3$. Finally, an examination of the host morphologies of our Compton-thick candidates shows a high fraction of morphological disturbances, in agreement with our previous results. All data products described in this paper are made available via a public website.

Photoacoustic Source Detection and Reflection Artifact Removal Enabled by Deep Learning.

Interventional applications of photoacoustic imaging typically require visualization of point-like targets, such as the small, circular, cross-sectional tips of needles, catheters, or brachytherapy seeds. When these point-like targets are imaged in the presence of highly echogenic structures, the resulting photoacoustic wave creates a reflection artifact that may appear as a true signal. We propose to use deep learning techniques to identify these types of noise artifacts for removal in experimental photoacoustic data. To achieve this goal, a convolutional neural network (CNN) was first trained to locate and classify sources and artifacts in pre-beamformed data simulated with -Wave. Simulations initially contained one source and one artifact with various medium sound speeds and 2-D target locations. Based on 3,468 test images, we achieved a 100% success rate in classifying both sources and artifacts. After adding noise to assess potential performance in more realistic imaging environments, we achieved at least 98% success rates for channel signal-to-noise ratios (SNRs) of -9dB or greater, with a severe decrease in performance below -21dB channel SNR. We then explored training with multiple sources and two types of acoustic receivers and achieved similar success with detecting point sources. Networks trained with simulated data were then transferred to experimental waterbath and phantom data with 100% and 96.67% source classification accuracy, respectively (particularly when networks were tested at depths that were included during training). The corresponding mean ± one standard deviation of the point source location error was 0.40 ± 0.22 mm and 0.38 ± 0.25 mm for waterbath and phantom experimental data, respectively, which provides some indication of the resolution limits of our new CNN-based imaging system. We finally show that the CNN-based information can be displayed in a novel artifact-free image format, enabling us to effectively remove reflection artifacts from photoacoustic images, which is not possible with traditional geometry-based beamforming.

AN INSTANTANEOUS POINT SOURCE DETECTION AND EXTRACTION SYSTEM FOR GEO REMOTE SENSING SATELLITE

Abstract. To accomplish the task of detecting the instantaneous point source, an on-board information real-time processing system is designed which can process the point-source detection with reconfigurable function. The system has the algorithm reconfigurable function, which can detect and extract the instantaneous point source from the remote sensing image. By using FPGA programming, the satellite target detection and processing algorithm can be update easily. At the same time, the software can be reconfigured to improve the system's information processing capabilities. The system has been verified by simulating real instantaneous source point target image data to meet the real-time processing requirements of instantaneous point source information detection.