Searcharxiv⌕ Search
Home/Archive/arXiv

arXiv · 2610.25581

Characterization of integrating-sphere systematic effects for photon calibration of the KAGRA gravitational wave detector

Shingo Hido·Misato Onishi·Shinji Miyoki·Takahiro Sawada·Takahiro Yamamoto·Takayuki Tomaru·Yoshiki Moriwaki·Dan Chen
View original source ↗Read source document ↗Export metadata ↓

Abstract

Photon calibration (Pcal) systems are essential for accurately reconstructing gravitational-wave strain in laser interferometric detectors. In the KAGRA Pcal system, the laser power applied to the end test mass is measured using integrating-sphere power sensors. During the first part of the fourth observing run (O4a), repeated calibration measurements showed scatter larger than expected from the uncertainties assigned to individual measurements. Although this excess scatter was empirically reflected in the previously reported calibration uncertainties, its physical origin was not identified. In this study, we experimentally characterize the response of the integrating spheres used in the KAGRA calibration chain with respect to three incident-beam conditions: incidence angle, polarization, and beam position. Based on these measurements and the reproducibility of the integrating-sphere placement in each calibration configuration, we evaluate the corresponding systematic uncertainties and explicitly incorporate them into the uncertainty model for the O4a Pcal-X calibration measurements. The inclusion of these previously unmodeled effects substantially improves the consistency between the estimated uncertainties and the observed scatter, particularly for calibration parameters measured at the KAGRA end station, where the reduced chi-square values decrease from values as large as $10^3$ to values of order unity to several. These results indicate that variations in the incident-beam conditions of integrating-sphere power sensors account for a substantial part of the previously unexplained scatter and establish a more physically motivated framework for evaluating Pcal laser-power calibration uncertainty. They also provide a basis for future reduction of the calibration uncertainty through improved control of the incident-beam conditions.

Explore related subjects

astro-ph.IM

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Shingo Hido, Misato Onishi, Shinji Miyoki, Takahiro Sawada, Takahiro Yamamoto, Takayuki Tomaru, Yoshiki Moriwaki, Dan Chen. 2026-10-18. Characterization of integrating-sphere systematic effects for photon calibration of the KAGRA gravitational wave detector. https://arxiv.org/abs/2610.25581

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

KEEP EXPLORING

Related papers

arXiv2026

A Radiation-Hardened, Fault-Aware Quantized Neural Network That Self-Heals

Deep learning (DL) models are increasingly being deployed into radiation-challenging environments in spacecraft, particle accelerators, and, soon, space-based data centers. Instantiated as a learned orchestration of numbers stored in persistent memory, models are susceptible to continual degradation as high-energy particles corrupt data. At inference time, new data passed through the network fundamentally break the off-line assumptions and guarantees of a static model determined during training. To preserve the quality and efficacy of the models, fault tolerance against bit flips is a clear objective. In this work we developed a runtime method for repairing the quantized weights of neural networks (NNs) after bit flips on commodity, commercial off-the-shelf (COTS) hardware, without storing extra replicas. Our work extends the well-established Fault-Aware Training (FAT) paradigm by adding an alignment penalty to concentrate undamaged activations and weights into a low-rank subspace, and we then reconstruct flagged corrupted coordinates from the uncorrupted survivors. We call this method {\it Projection Healing} and apply it to six regression benchmarks on quantized NNs. Holding a FAT checkpoint fixed and toggling only the runtime heal gives a further ${\sim}23\%$ reduction in the native-unit excess integral. Across those datasets the method extends the operational scrub interval by a median of $2.04\times$ at a $+5\%$ RMSE degradation and $1.96\times$ at $+50\%$, compared with $1.62\times$ and $1.07\times$ for classical Single Error Correction, Double Error Detection (SECDED). This introduces a new path towards designing resilient data-driven models, complementing established hardware redundancy, ECC, and FAT-based techniques.

astro-ph.IM↗
arXiv2026

Gaussian process-based Bayesian imaging of bolometric (sub-)millimetre data

Widefield single-dish telescopes operating at (sub-)mm wavelengths ($λ\sim 4$~mm to 350~$μ$m) tend to feature much faster mapping speeds than radio interferometers at the same wavelengths, and are more suitable for measuring signals on larger spatial scales. However, the data are dominated by atmospheric fluctuations on large scales and by photon and instrumental noise on smaller scales, making it difficult to recover the true astronomical sky. We build on maria-nifty, a Gaussian process-based Bayesian imaging framework for reconstructing sky maps from single-dish telescope data, implemented on top of Numerical Information Field Theory (NIFTy). It uses modular generative models to efficiently separate the astronomical signal from atmospheric emission while providing pixel-wise uncertainty quantification for the reconstructed sky maps. Moreover, our method cleanly separates individual astrophysical signals, even though they are superposed on the sky. Following its successful validation on synthetic time-ordered data generated using the maria software, we apply it to real data observed with the MUSTANG-2 bolometric array on the 100-meter Green Bank Telescope. For the galaxy cluster RXJ1347.5-1145, we separate the extended cluster emission from the compact source at its centre and obtain a smoother map with fewer artefacts compared to a standard maximum-likelihood reconstruction using the same data. The JAX-based implementation and GPU acceleration provide the basis for applying the method to the vastly larger datasets that the next generation of single-dish telescopes will deliver.

astro-ph.IM↗
arXiv2026

Estimator Precision and Selection for Up-the-Ramp Sampling with a High-Read-Noise InGaAs Camera

The noise properties of an InGaAs camera vary with gain, requiring a trade-off between implementation complexity and precision. Using generalized least squares (GLS) with known covariance as the precision benchmark, we examine the applicability and precision penalties of ordinary least squares (OLS) and correlated double sampling (CDS) at different noise operating points. We define $η=fT/σ_\mathrm{R}^2$, where $f$ is the total pixel count rate, $T$ the fitting span, and $σ_\mathrm{R}$ the single-read noise. For constant flux, uniform sampling, and independent white read noise plus Poisson noise, established finite-sampling variance expressions give equal OLS and CDS variances at $η=10$ for $N\ge4$ reads; OLS is favored below this boundary and CDS above it. Analytical and Monte Carlo comparisons for three gain modes quantify the precision loss of the better simplified estimator relative to GLS. In high-gain on-sky data, choosing OLS at 20 s and CDS at 90 s increases the background residual standard deviation by 3.55% and 3.01%, respectively, relative to two-iteration feasible GLS (FGLS). Photometry of 21 common sources does not clearly resolve most estimator differences. The observed OLS-CDS crossover occurs earlier than the ideal prediction, indicating that temporal covariance beyond the ideal model also affects the selection boundary. Observational validation is limited to high gain; medium- and low-gain choices rely on theory and simulations using calibrated parameters. OLS or CDS can replace GLS with a small precision penalty at suitable operating points. The ideal criterion provides an initial selection rule, whose practical boundary requires validation against observed temporal covariance.

astro-ph.IM↗