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Register a new userFaint Object Detection in Multi-Epoch Observations via Catalog Data Fusion
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Observational astronomy in the time-domain era faces several new challenges. One of them is the efficient use of observations obtained at multiple epochs. The work presented here addresses faint object detection with multi-epoch data, and describes an incremental strategy for separating real objects from artifacts in ongoing surveys, in situations where the single-epoch data are summaries of the full image data, such as single-epoch catalogs of flux and direction estimates for candidate sources. The basic idea is to produce low-threshold single-epoch catalogs, and use a probabilistic approach to accumulate catalog information across epochs; this is in contrast to more conventional strategies based on co-added or stacked image data across all epochs. We adopt a Bayesian approach, addressing object detection by calculating the marginal likelihoods for hypotheses asserting there is no object, or one object, in a small image patch containing at most one cataloged source at each epoch. The object-present hypothesis interprets the sources in a patch at different epochs as arising from a genuine object; the no-object (noise) hypothesis interprets candidate sources as spurious, arising from noise peaks. We study the detection probability for constant-flux objects in a simplified Gaussian noise setting, comparing results based on single exposures and stacked exposures to results based on a series of single-epoch catalog summaries. Computing the detection probability based on catalog data amounts to generalized cross-matching: it is the product of a factor accounting for matching of the estimated fluxes of candidate sources, and a factor accounting for matching of their estimated directions. We find that probabilistic fusion of multi-epoch catalog information can detect sources with only modest sacrifice in sensitivity and selectivity compared to stacking.
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Fully convolutional networks have shown outstanding performance in the salient object detection (SOD) field. The state-of-the-art (SOTA) methods have a tendency to become deeper and more complex, which easily homogenize their learned deep features, resulting in a clear performance bottleneck. In sharp contrast to the conventional ``deeper schemes, this paper proposes a ``wider network architecture which consists of parallel sub networks with totally different network architectures. In this way, those deep features obtained via these two sub networks will exhibit large diversity, which will have large potential to be able to complement with each other. However, a large diversity may easily lead to the feature conflictions, thus we use the dense short-connections to enable a recursively interaction between the parallel sub networks, pursuing an optimal complementary status between multi-model deep features. Finally, all these complementary multi-model deep features will be selectively fused to make high-performance salient object detections. Extensive experiments on several famous benchmarks clearly demonstrate the superior performance, good generalization, and powerful learning ability of the proposed wider framework.
3D object detection based on LiDAR-camera fusion is becoming an emerging research theme for autonomous driving. However, it has been surprisingly difficult to effectively fuse both modalities without information loss and interference. To solve this issue, we propose a single-stage multi-view fusion framework that takes LiDAR birds-eye view, LiDAR range view and camera view images as inputs for 3D object detection. To effectively fuse multi-view features, we propose an attentive pointwise fusion (APF) module to estimate the importance of the three sources with attention mechanisms that can achieve adaptive fusion of multi-view features in a pointwise manner. Furthermore, an attentive pointwise weighting (APW) module is designed to help the network learn structure information and point feature importance with two extra tasks, namely, foreground classification and center regression, and the predicted foreground probability is used to reweight the point features. We design an end-to-end learnable network named MVAF-Net to integrate these two components. Our evaluations conducted on the KITTI 3D object detection datasets demonstrate that the proposed APF and APW modules offer significant performance gains. Moreover, the proposed MVAF-Net achieves the best performance among all single-stage fusion methods and outperforms most two-stage fusion methods, achieving the best trade-off between speed and accuracy on the KITTI benchmark.
We present a description of the design and usage of a new synoptic pipeline and database model for time series photometry in the VISTA Data Flow System (VDFS). All UKIRT-WFCAM data and most of the VISTA main survey data will be processed and archived by the VDFS. Much of these data are multi-epoch, useful for finding moving and variable objects. Our new database design allows the users to easily find rare objects of these types amongst the huge volume of data being produced by modern survey telescopes. Its effectiveness is demonstrated through examples using Data Release 5 of the UKIDSS Deep Extragalactic Survey (DXS) and the WFCAM standard star data. The synoptic pipeline provides additional quality control and calibration to these data in the process of generating accurate light-curves. We find that 0.6+-0.1% of stars and 2.3+-0.6% of galaxies in the UKIDSS-DXS with K<15 mag are variable with amplitudes Delta K>0.015 mag
We present H, Ks and L filter polarimetric differential imaging (PDI) data for the transitional disk around HD100546 obtained in 2013, together with an improved re-reduction of previously published 2006 data. We reveal the disk in polarized scattered light in all three filters, achieving an inner working angle of 0.1 arcsec. Additional, short-exposure observations in the H and Ks filter probe the surrounding of the star down to about 0.03 (about 3 AU). HD100546 is fascinating because of its variety of sub-structures possibly related to forming planets in the disk, and PDI is currently the best technique to image them in the near-IR. Our key results are: (1) For the first time ever, we detect a disk in L-band PDI data. (2) We constrain the outer radius of the inner hole to 14pm2 AU and its eccentricity to < 0.133. (3) We detect a dark lane in the front side of the disk, which is likely an effect of the scattering angle and the scattering function of the grains. (4) We find a spiral arm in the northeast which has no obvious connection to spiral arms seen before by other authors further out in the disk, but winds in the same direction (clockwise). (5) The two bright scattering peaks along the semi-major axis are asymmetric, with the southeastern one being significantly brighter. This could be related to the inner companion candidate that is close to the brighter side of the disk at the time of the observations. (6) The scattering color is close to grey between H and Ks filter, but the scattering in L filter is significantly weaker. (7) We measure the position angle of the disk to be 138pm3 deg, consistent with previous observations. (8) We derive the dust scattering function in the H and Ks filter between 35 and 130 deg at two different radii (30-50 and 80-110 AU) and show that our results are consistent with a disk that is more strongly flared in the outer regions.
Multi-messenger astronomy is becoming the key to understanding the Universe from a comprehensive perspective. In most cases, the data and the technology are already in place, therefore it is important to provide an easily-accessible package that combines datasets from multiple telescopes at different wavelengths. In order to achieve this, we are working to produce a data analysis pipeline that allows the data reduction from different instruments without needing detailed knowledge of each observation. Ideally, the specifics of each observation are automatically dealt with, while the necessary information on how to handle the data in each case is provided by a tutorial that is included in the program. We first focus our project on the study of pulsars and their wind nebulae (PWNe) at radio and gamma-ray frequencies. In this way, we aim to combine time-domain and imaging datasets at two extremes of the electromagnetic spectrum. In addition, the emission has the same non-thermal origin in pulsars at radio and gamma-ray frequencies, and the population of electrons is believed to be the same at these energies in PWNe. The final goal of the project will be to unveil the properties of these objects by tracking their behaviour using all of the available multi-wavelength data.
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