Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 1801.02104

In-flight Calibration of Hitomi Soft X-ray Spectrometer (3) Effective Area

Masahiro Tsujimoto·Takashi Okajima·Megan E. Eckart·Takayuki Hayashi·Akio Hoshino·Ryo Iizuka·Richard L. Kelley·Caroline A. Kilbourne·Maurice A. Leutenegger·Yoshitomo Maeda·Hideyuki Mori·Frederick S. Porter·Kosuke Sato·Toshiki Sato·Peter J. Serlemitsos·Andrew Szymkowiak·Tahir Yaqoob
View original source ↗Read source document ↗Export metadata ↓

Abstract

We present the result of the in-flight calibration of the effective area of the Soft X-ray Spectrometer (SXS) onboard the Hitomi X-ray satellite using an observation of the Crab nebula. We corrected for the artifacts when observing high count rate sources with the X-ray microcalorimeter. We then constructed a spectrum in the 0.5-20 keV band, which we modeled with a single power-law continuum attenuated by an interstellar extinction. We evaluated the systematic uncertainty upon the spectral parameters by various calibration items. In the 2-12 keV band, the SXS result is consistent with the literature values in flux (2.20 $\pm$ 0.08) $\times$10$^{-8}$ erg s$^{-1}$ cm$^{-2}$ with a 1$σ$ statistical uncertainty) but is softer in the power-law index (2.19 $\pm$ 0.11). The discrepancy is attributable to the systematic uncertainty of about $+$6/$-$7% and $+$2/$-$5% respectively for the flux and the power-law index. The softer spectrum is affected primarily by the systematic uncertainty of the Dewar gate valve transmission and the event screening.

Explore related subjects

astro-ph.IMastro-ph.HE

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Masahiro Tsujimoto, Takashi Okajima, Megan E. Eckart, Takayuki Hayashi, Akio Hoshino, Ryo Iizuka, Richard L. Kelley, Caroline A. Kilbourne, Maurice A. Leutenegger, Yoshitomo Maeda, Hideyuki Mori, Frederick S. Porter, Kosuke Sato, Toshiki Sato, Peter J. Serlemitsos, Andrew Szymkowiak, Tahir Yaqoob. 2018-01-07. In-flight Calibration of Hitomi Soft X-ray Spectrometer (3) Effective Area. https://doi.org/10.1093/pasj%2Fpsy008

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Denoising Interferometric Observations Using Visibility-Informed Neural Networks

The upcoming observations from the Square Kilometer Array Observatory will provide the astronomical community with a wealth of observations of important objects at long wavelengths. Full analysis of these outputs will necessitate specialized methods and software. Using synthetic observations of protoplanetary discs as an example, we present VIREO, a machine-learning-based visibility-informed method for denoising interferometric images. VIREO operates on image-plane observations, but it is informed by the interferometric measurement process through the UV-derived point spread function supplied as an additional input and used in the loss function. VIREO outperforms traditional cleaning methods and PSF-ignorant denoising models by producing data that is quantitatively cleaner and more conducive to analysis of the planets within the disc. Applying VIREO to archival ALMA data creates images with significantly less background noise, while maintaining, and in some cases enhancing, the substructure. By demonstrating the general utility of visibility-informed models, our results suggest that VIREO can be applied across interferometric observatories when trained on appropriate datasets.

astro-ph.IM↗
arXiv2026

GREX-PLUS Science Book v2

GREX-PLUS (Galaxy Reionization EXplorer and PLanetary Universe Spectrometer) is a mission candidate for a JAXA strategic L-class mission to be launched in the 2030s. Its primary science goals are two-fold: galaxy formation and evolution, and planetary system formation and evolution. The GREX-PLUS spacecraft will carry a telescope with a 1 m primary mirror aperture cooled down to 50 K. The two science instruments will be onboard: a wide-field camera in the 2--8 $μ$m wavelength band and a high-resolution spectrometer with a wavelength resolution of 30,000 in the 10--18 $μ$m band. The GREX-PLUS wide-field camera aims to detect the first generation of galaxies at redshift $z>15$. The GREX-PLUS high-resolution spectrometer aims to identify the location of the water ``snowline'' in protoplanetary disks. Both instruments will provide unique datasets for a broad range of scientific topics, including galaxy mass assembly, the origin of supermassive blackholes, infrared background radiation, molecular spectroscopy in the interstellar medium, transit spectroscopy of exoplanet atmospheres, planetary atmospheres in the Solar System, and so on. This document is the second version of a collection of scientific themes that can be achieved with GREX-PLUS. Each section in Chapters~2 and 3 is based on presentations at several GREX-PLUS Science Workshops.

astro-ph.IM↗
arXiv2026

Differentiable astrophysics at scale: solving and differentiating ODE ensembles on the GPU

Astronomers increasingly fit their models with gradient-based methods, such as Hamiltonian Monte Carlo, which need the derivatives of the model with respect to its parameters. In many analyses a prediction requires solving a small system of ordinary differential equations (ODEs) for thousands to millions of parameter sets, and this ensemble of integrations often sets the cost of the analysis. We introduce to the astronomical community GRADSOLVE, a JAX library that moves this computation to graphics processing units (GPUs): it integrates each member of the ensemble in its own GPU thread with its own step size and returns the derivatives with respect to the parameters in the same pass. We measure the speed-up it provides in three examples from different fields of astronomy, stellar orbits in the Galactic potential, the expansion history of a dark-energy cosmology and the spin precession of binary black holes, with every code held to the same accuracy requirement. For a million trajectories GRADSOLVE runs 8.5 to 1500 times faster than the serial CPU code of each example running on all 128 cores of a CPU, and several orders of magnitude faster than on one core. On the same GPU it is also 11 to 15 times faster than DIFFRAX, the state-of-the-art ODE library in JAX, in all three examples. With GRADSOLVE a million such integrations take seconds or less on one GPU, so gradient-based analyses that need ensembles of this size become routine. The code is publicly available at https://github.com/ECLIPSE-AI4Science/gradsolve.

astro-ph.IM↗