The IPAC Image Subtraction and Discovery Pipeline for the intermediate Palomar Transient Factory

We describe the near real-time transient-source discovery engine for the intermediate Palomar Transient Factory (iPTF), currently in operations at the Infrared Processing and Analysis Center (IPAC), Caltech. We coin this system the IPAC/iPTF Discovery Engine (or IDE). We review the algorithms used for PSF-matching, image subtraction, detection, photometry, and machine-learned (ML) vetting of extracted transient candidates. We also review the performance of our ML classifier. For a limiting signal-to-noise ratio of 4 in relatively unconfused regions, "bogus" candidates from processing artifacts and imperfect image subtractions outnumber real transients by ~ 10:1. This can be considerably higher for image data with inaccurate astrometric and/or PSF-matching solutions. Despite this occasionally high contamination rate, the ML classifier is able to identify real transients with an efficiency (or completeness) of ~ 97% for a maximum tolerable false-positive rate of 1% when classifying raw candidates. All subtraction-image metrics, source features, ML probability-based real-bogus scores, contextual metadata from other surveys, and possible associations with known Solar System objects are stored in a relational database for retrieval by the various science working groups. We review our efforts in mitigating false-positives and our experience in optimizing the overall system in response to the multitude of science projects underway with iPTF.Comment: 66 pages, 21 figures, 7 tables, accepted by PAS

LANDSAT-D investigations in snow hydrology

Work undertaken during the contract and its results are described. Many of the results from this investigation are available in journal or conference proceedings literature - published, accepted for publication, or submitted for publication. For these the reference and the abstract are given. Those results that have not yet been submitted separately for publication are described in detail. Accomplishments during the contract period are summarized as follows: (1) analysis of the snow reflectance characteristics of the LANDSAT Thematic Mapper, including spectral suitability, dynamic range, and spectral resolution; (2) development of a variety of atmospheric models for use with LANDSAT Thematic Mapper data. These include a simple but fast two-stream approximation for inhomogeneous atmospheres over irregular surfaces, and a doubling model for calculation of the angular distribution of spectral radiance at any level in an plane-parallel atmosphere; (3) incorporation of digital elevation data into the atmospheric models and into the analysis of the satellite data; and (4) textural analysis of the spatial distribution of snow cover

Visual object tracking

University of Technology Sydney. Faculty of Engineering and Information Technology.Visual object tracking is a critical task in many computer-vision-related applications, such as surveillance and robotics. If the tracking target is provided in the first frame of a video, the tracker will predict the location and the shape of the target in the following frames. Despite the significant research effort that has been dedicated to this area for several years, this field remains challenging due to a number of issues, such as occlusion, shape variation and drifting, all of which adversely affect the performance of a tracking algorithm. This research focuses on incorporating the spatial and temporal context to tackle the challenging issues related to developing robust trackers. The spatial context is what surrounds a given object and the temporal context is what has been observed in the recent past at the same location. In particular, by considering the relationship between the target and its surroundings, the spatial context information helps the tracker to better distinguish the target from the background, especially when it suffers from scale change, shape variation, occlusion, and background clutter. Meanwhile, the temporal contextual cues are beneficial for building a stable appearance representation for the target, which enables the tracker to be robust against occlusion and drifting. In this regard, we attempt to develop effective methods that take advantage of the spatial and temporal context to improve the tracking algorithms. Our proposed methods can benefit three kinds of mainstream tracking frameworks, namely the template-based generative tracking framework, the pixel-wise tracking framework and the tracking-by-detection framework. For the template-based generative tracking framework, a novel template based tracker is proposed that enhances the existing appearance model of the target by introducing mask templates. In particular, mask templates store the temporal context represented by the frame difference in various time scales, and other templates encode the spatial context. Then, using pixel-wise analytic tools which provide richer details, which naturally accommodates tracking tasks, a finer and more accurate tracker is proposed. It makes use of two convolutional neural networks to capture both the spatial and temporal context. Lastly, for a visual tracker with a tracking-by-detection strategy, we propose an effective and efficient module that can improve the quality of the candidate windows sampled to identify the target. By utilizing the context around the object, our proposed module is able to refine the location and dimension of each candidate window, thus helping the tracker better focus on the target object

Learning object bounding boxes for 3D instance segmentation on point clouds

We propose a novel, conceptually simple and general framework for instance seg-mentation on 3D point clouds. Our method, called3D-BoNet, follows the simpledesign philosophy of per-point multilayer perceptrons (MLPs). The frameworkdirectly regresses 3Dboundingboxes for all instances in a point cloud, whilesimultaneously predicting a point-level mask for each instance. It consists of abackbone network followed by two parallel network branches for 1) bounding boxregression and 2) point mask prediction. 3D-BoNet is single-stage, anchor-freeand end-to-end trainable. Moreover, it is remarkably computationally efficientas, unlike existing approaches, it does not require any post-processing steps suchas non-maximum suppression, feature sampling, clustering or voting. Extensiveexperiments show that our approach surpasses existing work on both ScanNet andS3DIS datasets while being approximately10×more computationally efficient.Comprehensive ablation studies demonstrate the effectiveness of our design

Synthetic Aperture Radar (SAR) data processing

The available and optimal methods for generating SAR imagery for NASA applications were identified. The SAR image quality and data processing requirements associated with these applications were studied. Mathematical operations and algorithms required to process sensor data into SAR imagery were defined. The architecture of SAR image formation processors was discussed, and technology necessary to implement the SAR data processors used in both general purpose and dedicated imaging systems was addressed

Power spectrum of the maxBCG sample: detection of acoustic oscillations using galaxy clusters

We use the direct Fourier method to calculate the redshift-space power spectrum of the maxBCG cluster catalog -- currently by far the largest existing galaxy cluster sample. The total number of clusters used in our analysis is 12,616. After accounting for the radial smearing effect caused by photometric redshift errors and also introducing a simple treatment for the nonlinear effects, we show that currently favored low matter density "concordance" LCDM cosmology provides a very good fit to the estimated power. Thanks to the large volume (~0.4 h^{-3}Gpc^{3}), high clustering amplitude (linear effective bias parameter b_{eff} ~3x(0.85/sigma_8)), and sufficiently high sampling density (~3x10^{-5} h^{3}Mpc^{-3}) the recovered power spectrum has high enough signal to noise to allow us to find evidence (~2 sigma CL) for the baryonic acoustic oscillations (BAO). In case the clusters are additionally weighted by their richness the resulting power spectrum has slightly higher large-scale amplitude and smaller damping on small scales. As a result the confidence level for the BAO detection is somewhat increased: ~2.5 sigma. The ability to detect BAO with relatively small number of clusters is encouraging in the light of several proposed large cluster surveys.Comment: MNRAS accepted, extended analysis of arXiv:0705.1843, 15 page