Experimental Astronomy最新文献

The precise computation of binary star parameters is crucial for understanding their formation, evolution, and dynamics. However, large datasets of available astronomical measurements require substantial effort for computing using classic astronomical methods. Deep learning (DL) is a promising approach that can provide a proper solution for estimating the parameters and reducing the burden of the lengthy procedures of astronomical computations. This study proposes two DL-based models for estimating binary star parameters. The first is the well-known multi-layer perceptron (MLP) model, whereas the second is based on long short-term memory (LSTM). We rely on databases, such as large sky multi-object fiber spectroscopic telescope area (LAMOST), to train the proposed models. In addition, we verify the training ratio showing that the performance of both models at a low training ratio of (30%), based on the mean square error (MSE), results in acceptable performance. Furthermore, the LSTM-based DL model outperforms the conventional MLP for different training ratios. Eventually, the two models have superiority compared to the benchmark methods.

The Large Area Detector (LAD) is one of the science payloads of the enhanced X-ray Timing and Polarimetry (eXTP) mission. The LAD is a spectral-timing instrument with a broad energy response, covering a range from 2 to 30 keV, a good timing resolution of better than 10 µs, and an expected energy resolution of 260 eV at 6 keV. The LAD consists of 640 large-area multi-anode Silicon Drift Detectors (SDDs). Given the large number of detectors, the LAD uses a modular design. Each module comprises sixteen detectors, and each detector is equipped with dedicated Front-End Electronics (FEE), interfacing with two separate Module Back-End Electronics (MBEEs). Each MBEE is designed to process the data from 1,792 anode channels in 8 FEEs (224 anode channels per FEE), performing the energy reconstruction and time tagging for X-ray events. The MBEE uses the European Field Programmable Gate Array (FPGA) from NanoXplore™, based on a pipeline concept, which reduces dead time, making the LAD suitable for higher flux X-ray detection, and it can handle a sustained flux of >500 mCrab and a continuous flux of >15 Crab for up to 300 minutes (Feroci et al. 2018). Additionally, the MBEE serves as the central hub for configuring the module’s electronics, including the FEEs, the Power Supply Unit (PSU), and the MBEE itself, and it is also responsible for collecting housekeeping data to monitor the system’s status. The prototype MBEE was designed, manufactured, and programmed with FPGA firmware using VHDL. The basic functional test was conducted in this paper, and the results indicated that the MBEE could be operated in different modes to perform the functions mentioned above. Analysis and testing show that it can transmit event packets-containing timing tag, event type, position ID, and energy information-at a baud rate of 2 Mbps with an event loss fraction of 1.5%.

High-resolution absorption spectroscopy toward bright background sources has had a paramount role in understanding early galaxy formation, the evolution of the intergalactic medium and the reionisation of the Universe. However, these studies are now approaching the boundaries of what can be achieved at ground-based 8-10m class telescopes. The identification of primeval systems at the highest redshifts, within the reionisation epoch and even into the dark ages, and of the products of the first generation of stars and the chemical enrichment of the early Universe, requires observing very faint targets with a signal-to-noise ratio high enough to detect very weak spectral signatures. In this paper, we describe the giant leap forward that will be enabled by ANDES, the high-resolution spectrograph for the ELT, in these key science fields, together with a brief, non-exhaustive overview of other extragalactic research topics that will be pursued by this instrument, and its synergistic use with other facilities that will become available in the early 2030s.

The Mid-infrared ELT Imager and Spectrograph (METIS) is a first-generation instrument for the Extremely Large Telescope (ELT), Europe’s next-generation 39 m ground-based telescope for optical and infrared wavelengths, which is currently under construction at the European Southern Observatory (ESO) site at Cerro Armazones in Chile. METIS will offer diffraction-limited imaging, low- and medium-resolution slit spectroscopy, and coronagraphy for high-contrast imaging between 3 and 13 microns, as well as high-resolution integral field spectroscopy between 3 and 5 microns. The main METIS science goals are the detection and characterisation of exoplanets, the investigation of proto-planetary disks, and the formation of planets. The Single-Conjugate Adaptive Optics (SCAO) system corrects atmospheric distortions and is thus essential for diffraction-limited observations with METIS. SCAO will be used for all observing modes, with high-contrast imaging imposing the most demanding requirements on its performance. The Final Design Review (FDR) of METIS took place in the fall of 2022; the development of the instrument, including its SCAO system, has since entered the Manufacturing, Assembly, Integration and Testing (MAIT) phase. Numerous challenging aspects of an ELT Adaptive Optics (AO) system are addressed in the mature designs for the SCAO control system and the SCAO hardware module: the complex interaction with the telescope entities that participate in the AO control, wavefront reconstruction with a fragmented and moving pupil, secondary control tasks to deal with differential image motion, non-common path aberrations and mis-registration. A K-band pyramid wavefront sensor and a GPU-based Real-Time Computer (RTC), tailored to the needs of METIS at the ELT, are core components. This current paper serves as a natural sequel to our previous work presented in Hippler et al. (2018). It reflects all the updates that were implemented between the Preliminary Design Review (PDR) and FDR, and includes updated performance estimations in terms of several key performance indicators, including achieved contrast curves. We outline all important design decisions that were taken, and present the major challenges we faced and the main analyses carried out to arrive at these decisions and eventually the final design. We also elaborate on our testing and verification strategy, and, last not least, comprehensively present the full design, hardware and software in this paper to provide a single source of reference which will remain valid at least until commissioning.

Telescope cycle time estimation is one of the basic issues of observational astronomy. There are not many tools that help to calulate the cycle time for multiple telescopes with multiple instruments. This work presents a new tool for determing the observation time; it was applied at the Cerro Murphy Observatory (OCM) but can be used at any other observatory. The Machine Learning (ML) method was implied, resulting in a fully automatic software module that works without any user intervention. We propose a polynomial multiple regression method and demonstrate all steps to build a reliable ML model like data collecting, data cleaning, model training and error evaluation in relation to the implementation in the observatory software. The method was designed to work for different telescopes with several instruments. Accuracy analysis and the assessment of model errors were based on real data from telescopes, proving the usefulness of the presented method. Error evaluation shows that for 84.2 % of nights, the prediction error in operation time prediction does not exceed 2 %. Converted into a 10-hour observation night, 2 % corresponds to an error of no more than 12 minutes. The described model is already working at the OCM and optimizes the efficiency of the observations.

A Compton/Pair telescope, designed to provide spectral resolved images of cosmic photons from sub-MeV to GeV energies, records a wealth of data in a combination of tracking detector and calorimeter. Onboard event classification can be required to decide on which data to down-link with priority, given limited data-transfer bandwidth. Event classification is also the first and one of the most crucial steps in reconstructing data. Its outcome determines the further handling of the event, i.e., the type of reconstruction (Compton, pair) or, possibly, the decision to discard it. Errors at this stage result in misreconstruction and loss of source information. We present a classification algorithm driven by a Convolutional Neural Network. It provides classification of the type of electromagnetic interaction, based solely on low-level detector data. We introduce the task, describe the architecture and the dataset used, and present the performance of this method in the context of the proposed (e-)ASTROGAM and similar telescopes.

Reflectivity is a key topic in soft X-ray optics research and serves as the foundation for studying the performance of the optics for X-ray astronomical satellites. Since its establishment, the 100-m X-ray Test Facility (100XF) has been continuously developing various testing functionalities, including calibration of timing, imaging, and energy response. This paper provides a detailed description of the X-ray optics reflectivity test method based on the 100XF, which can be applied to various grazing incident X-ray optics, including Wolter-I and lobster-eye types, significantly expanding the application scope of the 100XF. A flat mirror sample (SiO(_{text{2 }}) coated on a Si wafer) is tested. Results of the variation of reflectivity with angle @ C-K(alpha ) (0.28 keV), Al-K(alpha )(1.49 keV), and Ti-K(alpha )(4.50 keV) are presented in the description. The reflectivity test method has also been applied to the coating reflectivity study of the enhanced X-ray Timing and Polarimetry Mission (eXTP) mirror. At the same time, a new method utilizing the continuum spectrum of bremsstrahlung was carried out to study the continuous variation of reflectivity with energy, greatly improving efficiency compared to traditional methods, and all the results show a good agreement with the theoretical values. The deviation between the test and theoretical values in the low-energy range (1.5-8.0 keV) is less than 10%.

The Chasing All Transients Constellation Hunters (CATCH) space mission is focused on exploring the dynamic universe via X-ray follow-up observations of various transients. The first pathfinder of the CATCH mission, CATCH-1, was launched on June 22, 2024, alongside the Space-based multiband astronomical Variable Objects Monitor (SVOM) mission. CATCH-1 is equipped with narrow-field optimized Micro Pore Optics (MPOs) featuring a large effective area and incorporates four Silicon Drift Detectors (SDDs) in its focal plane. This paper presents the system calibration results conducted before the satellite integration. Utilizing the data on the performance of the mirror and detectors obtained through the system calibration, combined with simulated data, the ground calibration database can be established. Measuring the relative positions of the mirror and detector system, which were adjusted during system calibration, allows for accurate installation of the entire satellite. Furthermore, the paper outlines the operational workflow of the ground network post-satellite launch.

Energetic particles of galactic and solar origin charge the metal free-falling test masses (TMs) of the interferometers for gravitational wave detection in space. The deposited charge couples with stray electric fields thus generating spurious Coulomb forces between the TMs and the electrode housing that limit the interferometer sensitivity. Long-term and short-term galactic cosmic-ray variations are strongly energy-dependent and the TM charging varies with particle energy distribution. We propose three different approaches involving Monte Carlo simulations and machine learning models in comparison to particle transport with the Parker equation to study the recurrent modulation of energy spectra of galactic particles ascribable to the passage of high-speed solar wind streams. The transit of interplanetary counterparts of coronal mass ejections modifies the effects of high-speed streams. This work aims at better understanding the energy-dependence of galactic cosmic-ray short-term variations for the Laser Interferometer Space Antenna (LISA), the first interferometer for gravitational wave detection in space, starting from lessons learned with LISA Pathfinder. The outcomes of our models will be used to assess the TM charging during the time LISA will remain in orbit around the Sun.

HERMES-Pathfinder is a space-borne mission based on a constellation of six nano-satellites flying in a low-Earth orbit. The 3U CubeSats, to be launched in early 2025, host miniaturized instruments with a hybrid Silicon Drift Detector/scintillator photodetector system, sensitive to both X-rays and gamma-rays. A seventh payload unit is installed onboard SpIRIT, an Australian-Italian nano-satellite developed by a consortium led by the University of Melbourne and launched in December 2023. The project aims at demonstrating the feasibility of Gamma-Ray Burst detection and localization using miniaturized instruments onboard nano-satellites. The HERMES flight model payloads were exposed to multiple well-known radioactive sources for spectroscopic calibration under controlled laboratory conditions. The analysis of the calibration data allows both to determine the detector parameters, necessary to map instrumental units to accurate energy measurements, and to assess the performance of the instruments. We report on these efforts and quantify features such as spectroscopic resolution and energy thresholds, at different temperatures and for all payloads of the constellation. Finally we review the performance of the HERMES payload as a photon counter, and discuss the strengths and the limitations of the architecture.