Search arXivSearch

arXiv · 1711.00510

Temporal Variations of Telluric Water Vapor Absorption at Apache Point Observatory

Abstract

Time-variable absorption by water vapor in Earth's atmosphere presents an important source of systematic error for a wide range of ground-based astronomical measurements, particularly at near-infrared wavelengths. We present results from the first study on the temporal and spatial variability of water vapor absorption at Apache Point Observatory (APO). We analyze $\sim$400,000 high-resolution, near-infrared ($H$-band) spectra of hot stars collected as calibration data for the APO Galactic Evolution Explorer (APOGEE) survey. We fit for the optical depths of telluric water vapor absorption features in APOGEE spectra and convert these optical depths to Precipitable Water Vapor (PWV) using contemporaneous data from a GPS-based PWV monitoring station at APO. Based on simultaneous measurements obtained over a 3$^{\circ}$ field of view, we estimate that our PWV measurement precision is $\pm0.11$ mm. We explore the statistics of PWV variations over a range of timescales from less than an hour to days. We find that the amplitude of PWV variations within an hour is less than 1 mm for most (96.5%) APOGEE field visits. By considering APOGEE observations that are close in time but separated by large distances on the sky, we find that PWV is homogeneous across the sky at a given epoch, with 90% of measurements taken up to 70$^{\circ}$ apart within 1.5 hr having $Δ\,\rm{PWV}<1.0$ mm. Our results can be used to help simulate the impact of water vapor absorption on upcoming surveys at continental observing sites like APO, and also to help plan for simultaneous water vapor metrology that may be carried out in support of upcoming photometric and spectroscopic surveys.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Dan Li, Cullen H. Blake, David Nidever, Samuel Halverson. 2017-11-15. Temporal Variations of Telluric Water Vapor Absorption at Apache Point Observatory. https://doi.org/10.1088/1538-3873%2Faa97ca

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

KEEP EXPLORING

Related papers

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

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

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