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Register a new userPushing the limit of instrument capabilities
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Authors
Denis V. Shulyak
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Chemically Peculiar (CP) stars have been subject of systematic research since more than 50 years. With the discovery of pulsation of some of the cool CP stars, the availability of advanced spectropolarimetric instrumentation and high signal- to-noise, high resolution spectroscopy, a new era of CP star research emerged about 20 years ago. Together with the success in ground-based observations, new space projects are developed that will greatly benefit for future investigations of these unique objects. In this contribution we will give an overview of some interesting results obtained recently from ground-based observations and discuss on future outstanding Gaia space mission and its impact on CP star research.
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We present in this paper the general formalism and data processing steps used in the MATISSE data reduction software, as it has been developed by the MATISSE consortium. The MATISSE instrument is the mid-infrared new generation interferometric instrument of the Very Large Telescope Interferometer (VLTI). It is a 2-in-1 instrument with 2 cryostats and 2 detectors: one 2k x 2k Rockwell Hawaii 2RG detector for L&M-bands, and one 1k x 1k Raytheon Aquarius detector for N-band, both read at high framerates, up to 30 frames per second. MATISSE is undergoing its first tests in laboratory today.
The Marshall Grazing Incidence X-ray Spectrometer (MaGIXS) is a sounding rocket experiment that observes the soft X-ray spectrum of the Sun from 6.0 - 24 Angstrom (0.5 - 2.0 keV), successfully launched on 30 July 2021. End-to-end alignment of the flight instrument and calibration experiments are carried out using the X-ray and Cryogenic Facility (XRCF) at NASA Marshall Space Flight Center. In this paper, we present the calibration experiments of MaGIXS, which include wavelength calibration, measurement of line spread function, and determination of effective area. Finally, we use the measured instrument response function to predict the expected count rates for MaGIXS flight observation looking at a typical solar active region
The Large Yield Radiometer (LYRA) is an XUV-EUV-MUV (soft X-ray to mid-ultraviolet) solar radiometer onboard the European Space Agency PROBA2 mission that was launched in November 2009. LYRA acquires solar irradiance measurements at a high cadence (nominally 20 Hz) in four broad spectral channels, from soft X-ray to MUV, that have been chosen for their relevance to solar physics, space weather and aeronomy. In this article, we briefly review the design of the instrument, give an overview of the data products distributed through the instrument website, and describe the way that data are calibrated. We also briefly present a summary of the main fields of research currently under investigation by the LYRA consortium.
The Spectral Imaging of the Coronal Environment (SPICE) instrument is a high-resolution imaging spectrometer operating at extreme ultraviolet (EUV) wavelengths. In this paper, we present the concept, design, and pre-launch performance of this facility instrument on the ESA/NASA Solar Orbiter mission. The goal of this paper is to give prospective users a better understanding of the possible types of observations, the data acquisition, and the sources that contribute to the instruments signal. The paper discusses the science objectives, with a focus on the SPICE-specific aspects, before presenting the instruments design, including optical, mechanical, thermal, and electronics aspects. This is followed by a characterisation and calibration of the instruments performance. The paper concludes with descriptions of the operations concept and data processing. The performance measurements of the various instrument parameters meet the requirements derived from the missions science objectives. The SPICE instrument is ready to perform measurements that will provide vital contributions to the scientific success of the Solar Orbiter mission.
The rapid development of large pre-trained language models has greatly increased the demand for model compression techniques, among which quantization is a popular solution. In this paper, we propose BinaryBERT, which pushes BERT quantization to the limit by weight binarization. We find that a binary BERT is hard to be trained directly than a ternary counterpart due to its complex and irregular loss landscape. Therefore, we propose ternary weight splitting, which initializes BinaryBERT by equivalently splitting from a half-sized ternary network. The binary model thus inherits the good performance of the ternary one, and can be further enhanced by fine-tuning the new architecture after splitting. Empirical results show that our BinaryBERT has only a slight performance drop compared with the full-precision model while being 24x smaller, achieving the state-of-the-art compression results on the GLUE and SQuAD benchmarks.
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