Highly Sensitive, Non-cryogenic NIR High-resolution Spectrograph, WINERED

WINERED is a novel near-infrared (NIR) high-resolution spectrograph (HRS) that pursues the highest possible sensitivity to realize high-precision spectroscopy in the NIR as in the optical wavelength range. WINERED covers 0.9–1.35 μm (z, Y, and J-bands) with three modes (Wide mode and two Hires modes) at the maximum spectral resolutions of R = 28,000 and R = 70,000. For fulfilling the objective, WINERED is designed with an unprecedentedly high instrument throughput (up to 50% at maximum including the quantum efficiency of the array) that is three times or more than other existing optical/NIR HRSs. This is mainly realized by a combination of non-white pupil and no fiber-fed configuration in optical design, the moderate (optimized) wavelength coverage, and the high-throughput gratings. Another prominent feature of WINERED is “warm” instrument despite for infrared (IR) observations. Such non-cryogenic (no cold stop) IR instrument finally became possible with the combination of custom-made thermal-cut filter of 10−8 class, 1.7 μm cutoff HAWAII-2RG array, and a cold baffle reducing the direct thermal radiation to the IR array into the solid angle of f/2. The thermal background is suppressed below 0.1 photons pixel−1 s−1 even in the wide band of 0.9–1.35 μm under the environment of 290 K. WINERED had been installed to the 3.58 m New Technology Telescope at La Silla Observatory, ESO, since 2017. Even with the intermediate size telescope, WINERED was confirmed to provide a limiting magnitude (for SNR = 30 with 8 hr. integration time) of J = 16.4 mag for the Wide mode and J = 15.1 mag for the Hires mode, respectively, under the natural seeing conditions. These sensitivities are comparable to those for the existing NIR-HRSs attached to the 8–10 m class telescopes with AO. WINERED type spectrographs may become a critical not only for the currently on-going extremely large telescopes to reduce the developing cost and time but also for smaller telescopes to extend their lives with long programs.

Real-time Closed-loop Active Surface Technology of a Large Radio Telescope

The surface accuracy of a large radio telescope’s primary reflector is easily affected by gravity and temperature change during observations. An active surface system is crucial to ensure the regular operation and high-quality data output of the radio telescope. We propose a real-time closed-loop active surface system including two components. The first component, a new type of photoelectric edge sensor, detects the angle change of the adjacent panels. The second component, the displacement actuator, adjusts the panels’ position and posture to compensate for the angle changes. So, over the entire observation, the closed-loop surface control system with these two components could actively maintain the primary reflector’s accuracy in real time. Using this approach, we constructed an experimental active surface system for the Xinjiang Qitai 110 m Radio Telescope (QTT) to test the maintenance of the surface accuracy. The angle measurement accuracy is better than 0.″2, and the positioning accuracy of the displacement actuator could achieve ±15 μm over the whole 50 mm stroke. The preliminary test results show that the accuracy requirements of the QTT’s primary reflector surface can be met using the active surface system we propose.

Classification of Astrophysics Journal Articles with Machine Learning to Identify Data for NED

The NASA/IPAC Extragalactic Database (NED) is a comprehensive online service that combines fundamental multi-wavelength information for known objects beyond the Milky Way and provides value-added, derived quantities and tools to search and access the data. The contents and relationships between measurements in the database are continuously augmented and revised to stay current with astrophysics literature and new sky surveys. The conventional process of distilling and extracting data from the literature involves human experts to review the journal articles and determine if an article is of extragalactic nature, and if so, what types of data it contains. This is both labor intensive and unsustainable, especially given the ever-increasing number of publications each year. We present here a machine learning (ML) approach developed and integrated into the NED production pipeline to help automate the classification of journal article topics and their data content for inclusion into NED. We show that this ML application can successfully reproduce the classifications of a human expert to an accuracy of over 90% in a fraction of the time it takes a human, allowing us to focus human expertise on tasks that are more difficult to automate.

iSHELL: a 1–5 micron R = 80,000 Immersion Grating Spectrograph for the NASA Infrared Telescope Facility

iSHELL is a 1.06–5.3 μm high spectral resolution spectrograph built for the 3.2 m NASA Infrared Telescope Facility (IRTF) on Maunakea, Hawaii. Dispersion is accomplished with a silicon immersion grating in order to keep the instrument small enough to be mounted at the Cassegrain focus of the telescope. The white pupil spectrograph produces resolving powers of up to about R ≡ λ/δλ = 80,000 (0.″375 slit). Cross-dispersing gratings mounted in a tiltable mechanism allow observers to select different wavelength ranges and, in combination with a slit wheel and Dekker mechanism, slit widths ranging from 0.″375 to 4.″0 and slit lengths ranging from 5″ to 25″. One Teledyne 2048 × 2048 HAWAII-2RG array is used in the spectrograph, and one Raytheon 512 × 512 Aladdin 2 array is used in a 1–5 μm slit viewer for object acquisition, guiding, and scientific imaging. iSHELL has been in productive regular use on IRTF since first light in 2016 September. In this paper we discuss details of the science case, design, construction and astronomical use of iSHELL.

Skynet’s New Observing Mode: The Campaign Manager

Built in 2004, the Skynet robotic telescope network originally consisted of six 0.4 m telescopes located at the Cerro-Tololo Inter-American Observatory in the Chilean Andes. The network was designed to carry out simultaneous multi-wavelength observations of gamma-ray bursts (GRBs) when they are only tens of seconds old. To date, the network has been expanded to ≈20 telescopes, including a 20 m radio telescope, that span four continents and five countries. The Campaign Manager (CM) is a new observing mode that has been developed for Skynet. Available to all Skynet observers, the CM semi-autonomously and indefinitely scales and schedules exposures on the observer’s behalf while allowing for modification to scaling parameters in real time. The CM is useful for follow up to various transient phenomena including gravitational-wave events, GRB localizations, young supernovae, and eventually, sufficiently bright Argus Optical Array and Large Synoptic Survey Telescope events.

Searching for Galactic H ii Regions from the LAMOST Spectroscopic Database

In this paper, we pioneer a new machine-learning method to search for H ii regions in spectra from The Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST). H ii regions are emission nebulae created when young and massive stars ionize nearby gas clouds with high-energy ultraviolet radiation. Having more H ii region samples will help us understand the formation and evolution of stars. Machine-learning methods are often applied to search for special celestial bodies such as H ii regions. LAMOST has conducted spectral surveys and provided a wealth of valuable spectra for the research of special and rare celestial bodies. To overcome the problem of sparse positive samples and diversification of negative samples, a novel method called the self-calibrated convolution network is introduced and implemented for spectral processing. A deep network classifier with a structure called a self-calibrated block provides a high precision rate, and the recall rate is improved by adding the strategy of positive-unlabeled bagging. Experimental results show that this method can achieve better performance than other current methods. Eighty-nine spectra are identified as Galactic H ii regions after cross-matching with the WISE Catalog of Galactic H ii Regions, confirming the effectiveness of the method proposed in this paper.

A Low-rank Approach to Image Defringing

In this work, we revisit the problem of interference fringe patterns in CCD chips occurring in near-infrared bands due to multiple light reflections within the chip. We briefly discuss the traditional approaches that were developed to remove these patterns from science images, and mention their limitations. We then introduce a new method to globally estimate the fringe patterns in a collection of science images without additional external data, allowing for some variation of the patterns between images. We demonstrate this new method on near-infrared images taken by the CFHT wide-field imager Megacam.

Gender Systematics in the NRAO Proposal Review System

Several recent investigations indicate the existence of gender-related systematic trends in the peer review of proposals for observations on astronomical facilities. This includes the National Radio Astronomy Observatory (NRAO) where there is evidence of a gender imbalance in the rank of proposals with male principal investigators (PIs) favored over female PIs. Since semester 2017A (17A), the NRAO has taken the following steps: (1) inform science review panels (SRPs) and the telescope time allocation committee (TAC) about the gender imbalance; and (2) increase the female representation on SRPs and the TAC to reflect the community demographics. Here we analyze SRP normalized rank-ordered scores, or linear ranks, by PI gender for NRAO observing proposals from semesters 12A–21A. We use bootstrap resampling to generate modeled distributions and the Anderson–Darling (AD) test to evaluate the probability that the linear rank distributions for male and female PIs are drawn from the same parent sample. We find that between semesters 12A–17A that male PIs are favored over female PIs (AD p-value 0.0084), whereas between semesters 17B–21A female PIs are favored over male PIs, but at a lower significance (AD p-value 0.11). Therefore the gender imbalance is currently being ameliorated, but this imbalance may have been reversed. Regardless, we plan to adopt a dual-anonymous approach to proposal review to reduce the possibility of bias to occur.