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Register a new userThe Richardson-Lucy Deconvolution method to Extract LAMOST 1D Spectra
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We use the Richardson-Lucy deconvolution algorithm to extract one dimensional (1D) spectra from LAMOST spectrum images. Compared with other deconvolution algorithms, this algorithm is much more fast. The practice on a real LAMOST image illustrates that the 1D resulting spectrum of this method has a higher SNR and resolution than those extracted by the LAMOST pipeline. Furthermore, our algorithm can effectively depress the ringings that are often shown in the 1D resulting spectra of other deconvolution methods.
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Next-generation neutrinoless double beta decay experiments aim for half-life sensitivities of ~$10^{27}$ yr, requiring suppressing backgrounds to <1 count/tonne/yr. For this, any extra background rejection handle, beyond excellent energy resolution and the use of extremely radiopure materials, is of utmost importance. The NEXT experiment exploits differences in the spatial ionization patterns of double beta decay and single-electron events to discriminate signal from background. While the former display two Bragg peak dense ionization regions at the opposite ends of the track, the latter typically have only one such feature. Thus, comparing the energies at the track extremes provides an additional rejection tool. The unique combination of the topology-based background discrimination and excellent energy resolution (1% FWHM at the Q-value of the decay) is the distinguishing feature of NEXT. Previous studies demonstrated a topological background rejection factor of ~5 when reconstructing electron-positron pairs in the $^{208}$Tl 1.6 MeV double escape peak (with Compton events as background), recorded in the NEXT-White demonstrator at the Laboratorio Subterraneo de Canfranc, with 72% signal efficiency. This was recently improved through the use of a deep convolutional neural network to yield a background rejection factor of ~10 with 65% signal efficiency. Here, we present a new reconstruction method, based on the Richardson-Lucy deconvolution algorithm, which allows reversing the blurring induced by electron diffusion and electroluminescence light production in the NEXT TPC. The new method yields highly refined 3D images of reconstructed events, and, as a result, significantly improves the topological background discrimination. When applied to real-data 1.6 MeV $e^-e^+$ pairs, it leads to a background rejection factor of 27 at 57% signal efficiency.
We generalize Richardson-Lucy (RL) deblurring to 4-D light fields by replacing the convolution steps with light field rendering of motion blur. The method deals correctly with blur caused by 6-degree-of-freedom camera motion in complex 3-D scenes, without performing depth estimation. We introduce a novel regularization term that maintains parallax information in the light field while reducing noise and ringing. We demonstrate the method operating effectively on rendered scenes and scenes captured using an off-the-shelf light field camera. An industrial robot arm provides repeatable and known trajectories, allowing us to establish quantitative performance in complex 3-D scenes. Qualitative and quantitative results confirm the effectiveness of the method, including commonly occurring cases for which previously published methods fail. We include mathematical proof that the algorithm converges to the maximum-likelihood estimate of the unblurred scene under Poisson noise. We expect extension to blind methods to be possible following the generalization of 2-D Richardson-Lucy to blind deconvolution.
Our study aims to recognize M-type stars which are classified as UNKNOWN due to bad quality in Large sky Area Multi-Object fibre Spectroscopic Telescope (LAMOST) DR5 V1. A binary nonlinear hashing algorithm based on Multi-Layer Pseudo Inverse Learning (ML-PIL) is proposed to effectively learn spectral features for the M-type star detection, which can overcome the bad fitting problem of template matching, particularly for low S/N spectra. The key steps and the performance of the search scheme are presented. A positive dataset is obtained by clustering the existing M-type spectra to train the ML-PIL networks. By employing this new method, we find 11,410 M-type spectra out of 642,178 UNKNOWN spectra, and provide a supplemental catalogue. Both the supplemental objects and released M-type stars in DR5 V1 are composed a whole M type sample, which will be released in the official DR5 to the public in June 2019, All the M-type stars in the dataset are classified to giants and dwarfs by two suggested separators: 1) color diagram of H versus J~K from 2MASS; 2) line indices CaOH versus CaH1, and the separation is validated with HRD derived from Gaia DR2. The magnetic activities and kinematics of M dwarfs are also provided with the EW of H_alpha emission line and the astrometric data from Gaia DR2 respectively.
The VST Optical Imaging of the CDFS and ES1 Fields (VOICE) Survey, in synergy with the SUDARE survey, is a deep optical $ugri$ imaging of the CDFS and ES1 fields using the VLT Survey Telescope (VST). The observations for the CDFS field comprise about 4.38 deg$^2$ down to $rsim26$ mag. The total on-sky time spans over four years in this field, distributed over four adjacent sub-fields. In this paper, we use the multi-epoch $r$-band imaging data to measure the variability of the detected objects and search for transients. We perform careful astrometric and photometric calibrations and point spread function (PSF) modeling. A new method, referring to as differential running-average photometry, is proposed to measure the light curves of the detected objects. With the method, the difference of PSFs between different epochs can be reduced, and the background fluctuations are also suppressed. Detailed uncertainty analysis and detrending corrections on the light curves are performed. We visually inspect the light curves to select variable objects, and present some objects with interesting light curves. Further investigation of these objects in combination with multi-band data will be presented in our forthcoming paper.
The Lucy Mission accomplishes its science during a series of five flyby encounters with seven Trojan asteroid targets. This mission architecture drives a concept of operations design that maximizes science return, provides redundancy in observations where possible, features autonomous fault protection and utilizes onboard target tracking near closest approach. These design considerations reduce risk during the relatively short time-critical periods when science data is collected. The payload suite consists of a color camera and infrared imaging spectrometer, a high-resolution panchromatic imager, and a thermal infrared spectrometer. The mission design allows for concurrent observations of all instruments. Additionally, two spacecraft subsystems will also contribute to the science investigations: the Terminal Tracking Cameras will obtain wide field-of-view imaging near closest approach to determine the shape of each of the Trojan targets and the telecommunication subsystem will carry out Doppler tracking of the spacecraft to determine the mass of each of the Trojan targets.
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