Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 1803.07630

The Effects of Bandpass Variations on Foreground Removal Forecasts for Future CMB Experiments

J. T. Ward·D. Alonso·J. Errard·M. J. Devlin·M. Hasselfield
View original source ↗Read source document ↗Export metadata ↓

Abstract

Time-dependent and systematic variations in band gain and central frequencies of instruments used to study the Cosmic Microwave Background are important factors in the data-to-map analysis pipeline. If not properly characterized, they could limit the ability of next-generation experiments to remove astrophysical foreground contamination. Uncertainties include the instrument detector band, which could systematically change across the focal plane, as well as the calibration of the instrument used to measure the bands. A potentially major effect is time-dependent gain and band uncertainties caused by atmospheric fluctuations. More specifically, changes in atmospheric conditions lead to frequency-dependent changes in the atmospheric transmission which, in turn, leads to variations in the effective gain and central frequency of the instrument's bandpass. Using atmospheric modeling software and ACTPol bandpasses, we simulate the expected variations in band gain and central frequency for 20, 40, 90, 150, and 240 GHz bands as a function of precipitable water vapor, observing angle, and ground temperature. Combining these effects enables us to set maximum and minimum limits on the expected uncertainties in band gain and central frequency over the course of a full observing season. We then introduce the uncertainties to parametric maximum-likelihood component separation methods on simulated CMB maps to forecast foreground removal performance and likelihoods on the tensor-to-scalar ratio r. We conclude that to measure a $σ(r=0)$ ~ $10^{-3}$ with a bias on the recovered $r$ under control, the limit on the uncertainty in the relative gain and central frequency of the bandpass must be <2% and <1%, respectively. We also comment on the possibility of self-calibrating bandpass uncertainties.

Explore related subjects

astro-ph.IMastro-ph.CO

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

J. T. Ward, D. Alonso, J. Errard, M. J. Devlin, M. Hasselfield. 2018-03-20. The Effects of Bandpass Variations on Foreground Removal Forecasts for Future CMB Experiments. https://doi.org/10.3847/1538-4357%2Faac71f

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

KEEP EXPLORING

Related papers

arXiv2026

Observations of Binary Stars with the 1.3-m Devasthal Fast Optical Telescope Using Speckle Interferometry: An Attempt

We present a feasibility study of implementing optical interferometry and speckle techniques with the 1.3-m Devasthal Fast Optical Telescope (DFOT) at Aryabhatta Research Institute of Observational Sciences (ARIES), which is currently dedicated to photometric observations. Using the sCMOS camera at the DFOT backend, we perform interferometric speckle observations of six binary stars. Standard Speckle Interferometry algorithms are applied to analyze the recorded speckle images. While this study does not aim to achieve the diffraction limit of DFOT or address a full science-driven resolution case, it serves as a crucial testbed for instrumentation, data acquisition, and analysis of Speckles with DFOT. Although the individual speckle patterns exhibit significant positional variations from frame to frame, the Speckle Interferometry analysis successfully recovers the characteristic three-peak structure of the wide binaries, allowing their relative separations and position angles to be determined, demonstrating the viability of the approach. The measured angular relative separations of $γ$ Leo, $γ$ Vir, and $ζ$ Her are $4.744'' \pm 0.012''$, $3.387'' \pm 0.027''$, and $1.582'' \pm 0.026''$, respectively, with corresponding projected position angles of $126.94^{\circ} \pm 0.14^\circ$, $171.57^{\circ} \pm 0.33^\circ$, and $81.06^{\circ} \pm 0.91^\circ$. These measurements are consistent with previously reported values in the literature, which provides strong motivation for more systematic observations and future implementation of optical interferometry techniques at meter-class telescopes.

astro-ph.IM↗
arXiv2026

Time-Domain Synthesis of Gravitational-Wave Detector Glitches using Class-Conditional Derivative Generative Adversarial Networks

Gravitational-wave detectors such as LIGO, Virgo, and KAGRA are highly sensitive instruments susceptible to many noise sources. Short-duration transient noise events, known as glitches, pose a particular challenge for data analysis pipelines, as they can mimic or obscure astrophysical signals. We present GlitchGAN, a class-conditional generative model built on the Conditional Derivative GAN (cDVGAN) architecture, capable of synthesizing seven glitch types from LIGO's third observing run (O3) directly in the time domain. GlitchGAN generalizes effectively, learning to reproduce a diverse glitch space consistent with high-quality DeepExtractor reconstructions, and can generate hybrid glitch morphologies by interpolating across its class-conditioning vector. It generates 1000 glitches in under 22 seconds on a CPU, suitable for detector simulations, mock data challenges, and pipeline validation. Synthetic glitches are validated using the Gravity Spy classifier and UMAP embeddings, both showing strong agreement with real data. To probe residual distributional differences, we train a separate holdout GlitchGAN model and use a downstream CNN to distinguish held-out real glitches from synthetic ones: detectability is high in a clean representation but drops substantially once both populations are injected into realistic detector noise, the condition under which they would typically be used. Despite this, GlitchGAN-generated glitches remain practically useful: augmenting real training sets with synthetic samples matches simple duplication of real data when data is abundant, and increasingly outperforms it as real data becomes scarce. Finally, we highlight a limitation of magnitude-only spectrograms: magnitude Q-transform classifiers can confidently misclassify physically unrealistic glitches from less robust models, underscoring the need for validation methods that preserve phase information.

astro-ph.IM↗
arXiv2026

SGN: A python framework for stream-processing pipelines

We present the Stream Graph Navigator (SGN), a lightweight Python framework for building streaming data applications. In SGN, stream-processing pipelines are built by connecting computational components into directed acyclic graphs that run within an event loop. The time-series extension of the SGN library, SGN-TS, introduces signal processing methods to handle time series data. Together, SGN and SGN-TS provide the foundation for SGNL, a matched-filtering gravitational-wave search pipeline, and are being adopted by multiple projects across the low-latency gravitational-wave data analysis infrastructure as an extensible and maintainable framework for future gravitational-wave observations.

astro-ph.IM↗