Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 2610.22834

MIDSSAR: MId Dispersion Spectrograph for Stellar Activity Research

Fumihde Iwamuro·Daisaku Nogami·Hiroyuki Maehara·Masaru Kino·Kai Ikuta·Akito Tajitsu·Sanghee Lee·Miho Kawabata·Keisuke Isogai·Kenta Taguchi·Kazunari Shibata
View original source ↗Read source document ↗Export metadata ↓

Abstract

We have developed and commissioned MIDSSAR (MId Dispersion Spectrograph for Stellar Activity Research), a novel fiber-fed, dual-band spectrograph optimized for high-cadence monitoring of stellar superflares and magnetic activity phenomena, mounted on the 3.8-m Seimei Telescope at Kyoto University Okayama Observatory. Utilizing a custom-designed volume phase holographic grating and a commercially available volume binary transmission grating, the instrument achieves simultaneous observations of key chromospheric activity indicators: the CaII H&K band (382.5-406.5nm) at a resolving power of R=13,800, and the H$α$ band (641.1-667.6nm) at R=18,200. MIDSSAR is strategically integrated with the existing high-dispersion spectrograph GAOES-RV to enable concurrent photosphere-chromosphere monitoring. The control software coordinates high-speed dual-camera exposures down to 1ms while incorporating real-time telescope telemetry into the data headers. We also developed a dedicated data reduction pipeline that automatically executes calibration, spatial reconstruction of the 61-fiber bundle, and one-dimensional spectral extraction with a wavelength precision of $\sim$ 0.002nm. Initial on-sky test observations conducted from March to May 2026 successfully verified the system's quantitative readiness. The observed signal-to-noise ratio characteristics perfectly align with modeled theoretical limits under varying seeing conditions, confirming that the entire instrument complex successfully meets its primary design requirements and operational expectations for systematic stellar astrophysics surveys.

Explore related subjects

astro-ph.IMastro-ph.SR

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Fumihde Iwamuro, Daisaku Nogami, Hiroyuki Maehara, Masaru Kino, Kai Ikuta, Akito Tajitsu, Sanghee Lee, Miho Kawabata, Keisuke Isogai, Kenta Taguchi, Kazunari Shibata. 2026-10-16. MIDSSAR: MId Dispersion Spectrograph for Stellar Activity Research. https://arxiv.org/abs/2610.22834

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

KEEP EXPLORING

Related papers

arXiv2026

Missed Detections and False Alarms in a CNN Technosignature Detector on Breakthrough Listen Data

Radio technosignature searches rely on machine learning detectors to shrink the candidate pool before a human looks at it. They are validated on held-out data from the training distribution, which excludes the cases where they are most likely to fail: a technosignature has unknown morphology, and terrestrial interference is unbounded in variety. I trained a standard competition detector once, froze it, and probed it with 15,000 injections across ten morphologies and five amplitudes, placed into real Green Bank backgrounds. Two directions were tested: genuine signals with morphologies other than a straight narrowband line, and interference resembling the training data. Recall did not follow the predicted ordering, and it fell as signals brightened, even for a needle of the kind the detector was trained on (from 0.590 to 0.090). One interference morphology reached a false positive rate of 0.627, comparable to the detector's own baseline recall of 0.5525 on genuine signals. Gradient-weighted Class Activation Mapping (Grad-CAM) showed attention along the injected path in one missed family by a pre-specified test, and visually in a second. A pre-specified, time-averaged ON-OFF filter removed false alarms and genuine detections at the same rate. The detector reads only the ON panels. Since the only evidence distinguishing a genuine candidate from terrestrial interference lies in the discarded OFF panels, it cannot tell if a signal originates from the target star; it can only tell if it resembles the training data. This explains the false alarm directly; the missed signals reflect what the detector learned to recognise, which reading the OFF panels would not by itself change.

astro-ph.IM↗
arXiv2026

On the foundations of anthropic reasoning: Implications for life across the universe

With only one confirmed example, namely the Earth, the abundance of life across the universe remains deeply uncertain, and local evidence about Earth's particular circumstances can significantly shape our knowledge of life elsewhere. Such extrapolation is difficult not only because of this sample size of one, but also due to observer selection effects discussed under the Anthropic Principle. In this paper, we describe and apply a framework called Full Indexical Conditioning (FIC) for performing Bayesian inference in this situation. Inspired by, but distinct from, Full Non-Indexical Conditioning, FIC conditions on all evidence available to the reasoner while considering that this evidence could have been otherwise. We address the reference class problem associated with such reasoning, demonstrating that substantive conclusions are unaffected by the choice of reference class provided it is sufficiently large. We apply FIC to several problems in astrobiology, including the Doomsday argument, the early abiogenesis time, and our location around a G-type rather than M-type star, clarifying the foundations of these arguments. We conclude that the early abiogenesis time does not rule out pessimistic possibilities for life's abundance, and that while life around M stars may be uncommon, its temporal distribution could extend far into the future. Ultimately, FIC offers a clear conceptual framework, grounded in straightforward probability theory, for tackling problems involving anthropic selection effects.

astro-ph.IM↗
arXiv2026

Seeing Through the RFI Mask: DT-FISTA Inpainting of Pulsar Dynamic Spectra via Fourier Sparsity

Radio frequency interference (RFI) frequently contaminates radio observations, and excising affected samples leaves masked regions in sampled images. Replacing masked samples with zero or the mean causes spectral leakage in Fourier-domain power representations. This contamination is particularly severe for modern pulsar scintillation studies, which rely on identifying and characterizing faint parabolic scintillation arcs in secondary spectra, the 2D power spectra of dynamic spectra. Scintillation arcs probe discrete scattering screens in the interstellar medium (ISM) and constrain their physical properties. However, leakage can obscure faint arcs whose power is often $<10^{-3}$ of the dominant pulsar signal power. Wiener inpainting can mitigate this leakage, but a dense implementation requires $\mathcal{O}(N^2)$ memory and $\mathcal{O}(JN^2)$ time for $N$ pixels and $J$ iterations, which is computationally expensive for full-resolution spectra containing millions of pixels. We introduce the decreasing-threshold fast iterative shrinkage-thresholding algorithm (DT-FISTA). Using a sparse Fourier representation, DT-FISTA initially estimates masked intensities from the strongest Fourier components and progressively refines them with weaker components as the soft threshold decreases. Nesterov momentum extrapolation accelerates these updates. Each iteration uses 2D fast Fourier transforms (FFT) and pointwise operations, requiring $\mathcal{O}(N\log N)$ time and $\mathcal{O}(N)$ memory. Tests on $300\times300$ pixel patches from Five-hundred-meter Aperture Spherical radio Telescope (FAST) observations yield lower reconstruction errors than the dense Wiener implementation, with runtime and memory requirements reduced by factors $>10^3$. Applied to full-resolution dynamic spectra, DT-FISTA suppresses mask-induced artifacts and reveals previously obscured faint arcs, enabling efficient ISM studies.

astro-ph.IM↗