Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2208.00377

Last performances improvement of the C-RED One camera using the 320x256 e-APD infrared Saphira detector

Philippe Feautrier·Jean-Luc Gach
View original source ↗Read source document ↗Export metadata ↓

Abstract

We present here the latest results obtained with the C-RED One camera developed by First Light Imaging for fast ultra-low noise infrared applications. This camera uses the Leonardo Saphira e-APD 320x256 infrared sensor in an autonomous cryogenic environment with a low vibration pulse tube and with embedded readout electronics system. Some recent improvements were made to the camera. The first important one concerns the total noise of the camera. Limited to 1.75 microns wavelength cut-off with proper cold filters, looking at a blackbody at room temperature and f/4 beam aperture, we now measure total noise down to 0.6 e at gain 50 in CDS mode 1720 FPS, dividing previous noise figure by a factor 2. The total camera background of 30-400 e/s is now achieved with a factor 3 of background reduction, the camera also looking at a room temperature blackbody with an F/4 beam aperture. Image bias oscillations, due to electronics grounding scheme, were carefully analyzed and removed. Focal plane detector vibrations transmitted by the pulse tube cooling machine were also analyzed, damped and measured down to 0.3 microns RMS, reducing focal plane vibrations by a factor 3. In addition, a vacuum getter of higher capacity is now used to offer camera operation without camera pumping during months. The camera main characteristics are detailed: pulse tube cooling at 80K with limited vibrations, permanent vacuum solution, ultra-low latency Cameralink full data interface, safety management of the camera by firmware, online firmware update, ambient liquid cooling and reduced weight of 20 kg.

Explore related subjects

astro-ph.IMphysics.ins-det

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Philippe Feautrier, Jean-Luc Gach. 2022-07-31. Last performances improvement of the C-RED One camera using the 320x256 e-APD infrared Saphira detector. https://arxiv.org/abs/2208.00377

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↗