Building the Evryscope: Hardware Design and Performance

The Evryscope is a telescope array designed to open a new parameter space in optical astronomy, detecting short timescale events across extremely large sky areas simultaneously. The system consists of a 780 MPix 22-camera array with an 8150 sq. deg. field of view, 13" per pixel sampling, and the ability to detect objects down to Mg=16 in each 2 minute dark-sky exposure. The Evryscope, covering 18,400 sq.deg. with hours of high-cadence exposure time each night, is designed to find the rare events that require all-sky monitoring, including transiting exoplanets around exotic stars like white dwarfs and hot subdwarfs, stellar activity of all types within our galaxy, nearby supernovae, and other transient events such as gamma ray bursts and gravitational-wave electromagnetic counterparts. The system averages 5000 images per night with ~300,000 sources per image, and to date has taken over 3.0M images, totaling 250TB of raw data. The resulting light curve database has light curves for 9.3M targets, averaging 32,600 epochs per target through 2018. This paper summarizes the hardware and performance of the Evryscope, including the lessons learned during telescope design, electronics design, a procedure for the precision polar alignment of mounts for Evryscope-like systems, robotic control and operations, and safety and performance-optimization systems. We measure the on-sky performance of the Evryscope, discuss its data-analysis pipelines, and present some example variable star and eclipsing binary discoveries from the telescope. We also discuss new discoveries of very rare objects including 2 hot subdwarf eclipsing binaries with late M-dwarf secondaries (HW Vir systems), 2 white dwarf / hot subdwarf short-period binaries, and 4 hot subdwarf reflection binaries. We conclude with the status of our transit surveys, M-dwarf flare survey, and transient detection.Comment: 24 pages, 24 figures, accepted PAS

Variables in the Southern Polar Region Evryscope 2016 Dataset

The regions around the celestial poles offer the ability to find and characterize long-term variables from ground-based observatories. We used multi-year Evryscope data to search for high-amplitude (~5% or greater) variable objects among 160,000 bright stars (Mv < 14.5) near the South Celestial Pole. We developed a machine learning based spectral classifier to identify eclipse and transit candidates with M-dwarf or K-dwarf host stars - and potential low-mass secondary stars or gas giant planets. The large amplitude transit signals from low-mass companions of smaller dwarf host stars lessens the photometric precision and systematics removal requirements necessary for detection, and increases the discoveries from long-term observations with modest light curve precision. The Evryscope is a robotic telescope array that observes the Southern sky continuously at 2-minute cadence, searching for stellar variability, transients, transits around exotic stars and other observationally challenging astrophysical variables. In this study, covering all stars 9 < Mv < 14.5, in declinations -75 to -90 deg, we recover 346 known variables and discover 303 new variables, including 168 eclipsing binaries. We characterize the discoveries and provide the amplitudes, periods, and variability type. A 1.7 Jupiter radius planet candidate with a late K-dwarf primary was found and the transit signal was verified with the PROMPT telescope network. Further followup revealed this object to be a likely grazing eclipsing binary system with nearly identical primary and secondary K5 stars. Radial velocity measurements from the Goodman Spectrograph on the 4.1 meter SOAR telescope of the likely-lowest-mass targets reveal that six of the eclipsing binary discoveries are low-mass (.06 - .37 solar mass) secondaries with K-dwarf primaries, strong candidates for precision mass-radius measurements.Comment: 32 pages, 17 figures, accepted to PAS

Intelligent Embedded Vision for Summarization of Multi-View Videos in IIoT

Nowadays, video sensors are used on a large scale for various applications including security monitoring and smart transportation. However, the limited communication bandwidth and storage constraints make it challenging to process such heterogeneous nature of Big Data in real time. Multi-view video summarization (MVS) enables us to suppress redundant data in distributed video sensors settings. The existing MVS approaches process video data in offline manner by transmitting it to the local or cloud server for analysis, which requires extra streaming to conduct summarization, huge bandwidth, and are not applicable for integration with industrial internet of things (IIoT). This paper presents a light-weight CNN and IIoT based computationally intelligent (CI) MVS framework. Our method uses an IIoT network containing smart devices, Raspberry Pi (clients and master) with embedded cameras to capture multi-view video (MVV) data. Each client Raspberry Pi (RPi) detects target in frames via light-weight CNN model, analyzes these targets for traffic and crowd density, and searches for suspicious objects to generate alert in the IIoT network. The frames of each client RPi are encoded and transmitted with approximately 17.02% smaller size of each frame to master RPi for final MVS. Empirical analysis shows that our proposed framework can be used in industrial environments for various applications such as security and smart transportation and can be proved beneficial for saving resources

The Robotilter: An Automated Lens / CCD Alignment System for the Evryscope

Camera lenses are increasingly used in wide-field astronomical surveys due to their high performance, wide field-of-view (FOV) unreachable from traditional telescope optics, and modest cost. The machining and assembly tolerances for commercially available optical systems cause a slight misalignment (tilt) between the lens and CCD, resulting in PSF degradation. We have built an automated alignment system (Robotilters) to solve this challenge, optimizing 4 degrees of freedom - 2 tilt axes, a separation axis (the distance between the CCD and lens), and the lens focus (the built-in focus of the lens by turning the lens focusing ring which moves the optical elements relative to one another) in a compact and low-cost package. The Robotilters remove tilt and optimize focus at the sub 10 micron level, are completely automated, take 2 hours to run, and remain stable for multiple years once aligned. The Robotilters were built for the Evryscope telescope (a 780 MPix 22-camera array with an 8150 sq.deg. field of view and continuous 2-minute cadence) designed to detect short timescale events across extremely large sky areas simultaneously. Variance in quality across the image field, especially the corners and edges compared to the center, is a significant challenge in wide-field astronomical surveys like the Evryscope. The individual star PSFs (which typically extend only a few pixels) are highly susceptible to slight increases in optical aberrations in this situation. The Robotilter solution resulted in a limiting magnitude improvement of .5 mag in the center of the image and 1.0 mag in the corners for typical Evryscope cameras, with less distorted and smaller PSFs (half the extent in the corners and edges in many cases). In this paper we describe the Robotilter mechanical and software design, camera alignment results, long term stability, and image improvement.Comment: Accepted to JATIS, January 202

Robotilter: an automated lens/CCD alignment system for the Evryscope

Camera lenses are increasingly used in wide-field astronomical surveys due to their high performance, wide field-of-view (FOV) unreachable from traditional telescope optics, and modest cost. The machining and assembly tolerances for commercially available optical systems cause a slight misalignment (tilt) between the lens and CCD, resulting in point spread function (PSF) degradation. We have built an automated alignment system (Robotilters) to solve this challenge, optimizing four degrees of freedom¿two tilt axes, a separation axis (the distance between the CCD and lens), and the lens focus (the built-in focus of the lens by turning the lens focusing ring, which moves the optical elements relative to one another) in a compact and low-cost package. The Robotilters remove tilt and optimize focus at the sub-10-μm level, are completely automated, take ≈2 h to run, and remain stable for multiple years once aligned. The Robotilters were built for the Evryscope telescope (a 780-MPix 22-camera array with an 8150-sq. deg FOV and continuous 2-min cadence) designed to detect short-timescale events across extremely large sky areas simultaneously. Variance in quality across the image field, especially the corners and edges compared to the center, is a significant challenge in wide-field astronomical surveys like the Evryscope. The individual star PSFs (which typically extend only a few pixels) are highly susceptible to slight increases in optical aberrations in this situation. The Robotilter solution resulted in a limiting magnitude improvement of 0.5 mag in the center of the image and 1.0 mag in the corners for typical Evryscope cameras, with less distorted and smaller PSFs (half the extent in the corners and edges in many cases). We describe the Robotilter mechanical and software design, camera alignment results, long-term stability, and image improvement. The potential for general use in wide-field astronomical surveys is also explored

Edge-enhanced dual discriminator generative adversarial network for fast MRI with parallel imaging using multi-view information

In clinical medicine, magnetic resonance imaging (MRI) is one of the most important tools for diagnosis, triage, prognosis, and treatment planning. However, MRI suffers from an inherent slow data acquisition process because data is collected sequentially in k-space. In recent years, most MRI reconstruction methods proposed in the literature focus on holistic image reconstruction rather than enhancing the edge information. This work steps aside this general trend by elaborating on the enhancement of edge information. Specifically, we introduce a novel parallel imaging coupled dual discriminator generative adversarial network (PIDD-GAN) for fast multi-channel MRI reconstruction by incorporating multi-view information. The dual discriminator design aims to improve the edge information in MRI reconstruction. One discriminator is used for holistic image reconstruction, whereas the other one is responsible for enhancing edge information. An improved U-Net with local and global residual learning is proposed for the generator. Frequency channel attention blocks (FCA Blocks) are embedded in the generator for incorporating attention mechanisms. Content loss is introduced to train the generator for better reconstruction quality. We performed comprehensive experiments on Calgary-Campinas public brain MR dataset and compared our method with state-of-the-art MRI reconstruction methods. Ablation studies of residual learning were conducted on the MICCAI13 dataset to validate the proposed modules. Results show that our PIDD-GAN provides high-quality reconstructed MR images, with well-preserved edge information. The time of single-image reconstruction is below 5ms, which meets the demand of faster processing

TFAW survey II: 6 Newly Validated Planets and 13 Planet Candidates from K2

Searching for Earth-sized planets in data from Kepler's extended mission (K2) is a niche that still remains to be fully exploited. The TFAW survey is an ongoing project that aims to re-analyze all light curves in K2 C1-C8 and C12-C18 campaigns with a wavelet-based detrending and denoising method, and the period search algorithm TLS to search for new transit candidates not detected in previous works. We have analyzed a first subset of 24 candidate planetary systems around relatively faint host stars (10.9 < $K_{p}$ < 15.4) to allow for follow-up speckle imaging observations. Using VESPA and TRICERATOPS, we statistically validate six candidates orbiting four unique host stars by obtaining false-positive probabilities smaller than 1% with both methods. We also present 13 vetted planet candidates that might benefit from other, more precise follow-up observations. All of these planets are sub-Neptune-sized, with two validated planets and three candidates with sub-Earth sizes, and have orbital periods between 0.81 and 23.98 days. Some interesting systems include two ultra-short-period planets, three multi-planetary systems, three sub-Neptunes that appear to be within the small planet Radius Gap, and two validated and one candidate sub-Earths (EPIC 210706310, EPIC 210768568, and EPIC 246078343) orbiting metal-poor stars.Comment: Submitted to Monthly Notices of the Royal Astronomical Society. 25 pages, 14 figure

Building the Evryscope: Hardware Design and Performance

The Evryscope is a telescope array designed to open a new parameter space in optical astronomy, detecting short-timescale events across extremely large sky areas simultaneously. The system consists of a 780 MPix 22-camera array with an 8150 sq. deg. field of view, 13″ per pixel sampling, and the ability to detect objects down to {m}g\prime ≃ 16 in each 2-minute dark-sky exposure. The Evryscope, covering 18,400 sq. deg. with hours of high-cadence exposure time each night, is designed to find the rare events that require all-sky monitoring, including transiting exoplanets around exotic stars like white dwarfs and hot subdwarfs, stellar activity of all types within our galaxy, nearby supernovae, and other transient events such as gamma-ray bursts and gravitational-wave electromagnetic counterparts. The system averages 5000 images per night with ∼300,000 sources per image, and to date has taken over 3.0M images, totaling 250 TB of raw data. The resulting light curve database has light curves for 9.3M targets, averaging 32,600 epochs per target through 2018. This paper summarizes the hardware and performance of the Evryscope, including the lessons learned during telescope design, electronics design, a procedure for the precision polar alignment of mounts for Evryscope-like systems, robotic control and operations, and safety and performance-optimization systems. We measure the on-sky performance of the Evryscope, discuss its data analysis pipelines, and present some example variable star and eclipsing binary discoveries from the telescope. We also discuss new discoveries of very rare objects including two hot subdwarf eclipsing binaries with late M-dwarf secondaries (HW Vir systems), two white dwarf/hot subdwarf short-period binaries, and four hot subdwarf reflection binaries. We conclude with the status of our transit surveys, M-dwarf flare survey, and transient detection

TFAW survey II: six newly validated planets and 13 planet candidates from K2

Searching for Earth-sized planets in data from Kepler's extended mission (K2) is a niche that still remains to be fully exploited. The TFAW survey is an ongoing project that aims to re-analyse all light curves in K2 C1-C8 and C12-C18 campaigns with a wavelet-based detrending and denoising method, and the period search algorithm TLS to search for new transit candidates not detected in previous works. We have analysed a first subset of 24 candidate planetary systems around relatively faint host stars (10.9 < Kp < 15.4) to allow for follow-up speckle imaging observations. Using vespa and TRICERATOPS, we statistically validate six candidates orbiting four unique host stars by obtaining false-positive probabilities smaller than 1 per cent with both methods. We also present 13 vetted planet candidates that might benefit from other, more precise follow-up observations. All of these planets are sub-Neptune-sized with two validated planets and three candidates with sub-Earth sizes, and have orbital periods between 0.81 and 23.98 d. Some interesting systems include two ultra-short-period planets, three multiplanetary systems, three sub-Neptunes that appear to be within the small planet Radius Gap, and two validated and one candidate sub-Earths (EPIC 210706310.01, K2-411 b, and K2-413 b) orbiting metal-poor stars

Efficacy and safety of nimodipine in subcortical vascular dementia : A randomized placebo-controlled trial

Background and Purpose-Evidence of drug efficacy in vascular dementia (VaD) is scanty. Therapeutic trials should address VaD subtypes. We studied the efficacy and safety of the calcium antagonist nimodipine in subcortical VaD. Methods-242 patients defined as affected by subcortical VaD based on clinical (ICD-10) and computed tomography criteria were randomized to oral nimodipine 90 mg/d or placebo. Results-230 patients (121 nimodipine, mean age 75.2\uc2\ub16.1; 109 placebo, 75.4\uc2\ub16.0) were valid for the intention-to-treat analysis. At 52 weeks, the Sandoz Clinical Assessment Geriatric scale 5-point variation (primary outcome measure) did not differ significantly between the 2 groups. However, patients on nimodipine performed better than placebo patients in lexical production (P<0.01) and less frequently showed deterioration (3 or more point-drop versus baseline) on a Mini-Mental State Examination (28.1% versus 50.5%; \ucf\u872 P<0.01) and Global Deterioration Scale (P<0.05). Dropouts and adverse events were all significantly more common among placebo than nimodipine patients, particularly cardiovascular (30 versus 13; RR, 2.26; 95% CI, 1.11 to 4.60) and cerebrovascular events (28 versus 10; RR, 2.48; 95% CI, 1.23 to 4.98), and behavioral disturbances requiring intervention (22 versus 5; RR, 3.88; 95% CI, 1.49 to 10.12). A worst-rank analysis, performed to correct for the effect of the high dropout rate in the placebo group, showed additional significant differences in favor of nimodipine in Set Test and MMSE total scores. Conclusions-Nimodipine may be of some benefit in subcortical VaD. Confirming previous results, the safety analysis of this study shows that in this high-risk population, nimodipine might protect against cardiovascular comorbidities