The Very Large Telescope Interferometer (VLTI) must control its Optical Path Differences (OPD) to extremely high precision in order to achieve its characteristic and desired high performance. This proves a challenge when using Very Large Telescope’s (VLT) 8 meter Unit Telescopes (UT) given they are not fully dedicated to interferometry and can be equipped with up to three different instruments each. Among the several important control systems that allow the VLTI to achieve the necessary precision for this task is Manhattan II (MNII), which measures vibrations along the Optical Path (mirrors M1 to M7) and sends Optical Path Length (OPL) corrections to the Delay Lines (DL). In the context of GRAVITY+ upgrade, MNII is being extended to cover a larger portion of the light path (previously M1 to M3) and expanded with Phase-locked Loop (PLL) to improve OPD control by targeting specific frequencies. Alongside, several options are being explored to further improve the capabilities of the system. Active compensation is improved by the upgrade of MNII’s PLL. In addition, better troubleshooting tools and automatic Anomaly Detection (AD) systems are needed to constantly monitor and react to the changing vibration signature of the UTs. Furthermore, similar AD systems will be fundamental in the future for the operation of the upcoming Extremely Large Telescope (ELT). This work is about the ongoing efforts to develop an automatic AD system using Machine Learning on MNII’s vibration data. We focus on the different methods and models used in the proof of concept which include Auto-encoders, clustering and classical statistical methods as well, the infrastructure required to have a working end-to-end prototype, the data pipeline, preprocessing and the future envisioned production system.
Datalab, the La Silla Paranal Observatory Platform for data analysis, is being migrated from Docker Swarm to Kubernetes to align with the integrated operations program's goals: Remote, Lean, Sustainable, and High performance. The migration implied to move from an on-premises to a cloud-native infrastructure replicated locally into a Cloud-Edge, providing hybrid cloud containerized applications support, implementing DevOps practices and automation. Using infrastructure as code and configuration management tools like Terraform and Ansible. Building CI/CD pipelines in Gitlab to automatically deploy the proper infrastructure into to the hybrid cloud to hold Kubernetes clusters (Azure Kubernetes Cluster and Vanilla Kubernetes). This approach allows the Observatory to enhance efficiency, reducing power consumption and improving scalability. Using Datalab as proof of concept but setting up the foundation to standardize these technologies in the organization. This paper outlines the provisioning and deployment of the new hybrid cloud infrastructure, providing a concise overview of its architecture, operational impact, and benefits for the observatory.
KEYWORDS: Data analysis, Observatories, Visualization, Error analysis, Data modeling, Telescopes, Education and training, Machine learning, Analytical research
The VLT at Paranal Observatory has been in operation for over two decades, and soon, the ELT will be managed by the same operational team. Maintaining operational efficiency and minimizing downtime with limited resources will be crucial. Previous research has shown that software logs effectively capture the telescopes' behavior, providing valuable operational insights. We've integrated various log analysis techniques from academic literature and industry best practices. These techniques allow engineers to monitor system health, analyze error sequences, detect anomalies, and reconstruct processes which improve maintenance and extract new insights. Additionally, we've utilized generative artificial intelligence and NLP transformer-based models, to infer observation behavior and predict execution failures. We have taken advantage of both the Paranal Datalab on-premises facility and Azure Cloud. In this work, we provide technical details and outline the key challenges and opportunities in adopting this technique within an astronomy facility.
KEYWORDS: Observatories, Data storage, Clouds, Data modeling, Visualization, Machine learning, Software development, Databases, Data processing, Data archive systems
During the last five years, Paranal has been developing a data centric paradigm for the monitoring and maintenance of the different systems in the observatory. The main objectives of this paradigm are, on the one hand, to automate as many tasks as possible to improve the dependability of the observatory while not increasing the FTE needed to operate it, and on the other, to increment the remote operation reducing the need of “in-situ” access to the system under scrutiny. In principle, the data centric approach is meant to complement and not replace the traditional problem-solving methods used at Paranal. Nevertheless, FTE-expensive tasks must be limited to exceptional situations. During all these years, we have moved from prototypes to production, and the observatory culture is slowly changing towards this data centric approach, including slow incorporation of AI/ML and NLP. Nonetheless, this is just stage one, as we are now moving to expand the scope incorporating, among other things, the cloud and creating a homogeneous, hybrid, data cyberinfrastructure.
For almost two decades, large volumes of technical data, in a variety of formats, have resulted from the normal operations at the observatory. Similarly, in the last few years, dealing with huge amounts of data has become a priority for several industries, and as consequence, terms like "Big Data" or "Data Lake" have started to be more and more commonly used. Under these circumstances, frameworks and tools have proliferated and later released as "Open Software"; the hardware, on the other hand, has also changed giving the power to deal with this volume of data in a reasonable timeframe, and at a reasonable price.
We hereafter present the first version of a modern data lab developed for the Maintenance Support and Engineering Department (MSE) at the Paranal Observatory, “The MSE DataLab”. This DataLab will allow us to take advantage of this new technological evolution and to be prepared for the current and further challenges to come. These challenges, of course, refer to improving the overall observatory dependability (Reliability, Availability and Maintainability) by supporting the operations in our current and forthcoming telescopes. First, in our Very Large Telescopes (VLT), the VLT Interferometer (VLTI) and the survey telescopes (VISTA and VST). Secondly, in the Extremely Large Telescope (ELT) and the Cherenkov Telescope Array (CTA).
KEYWORDS: Observatories, Calibration, Iterated function systems, Optical spheres, Sensors, Optical filtering, Stars, Polarimetry, Control systems, K band
The Paranal Very Large Telescopes (VLT) Observatory is a complex multifunctional observatory where many different systems are generating telemetry parameters.As systems becoming more and more complex, also the amount of telemetry data is increasing. This telemetry data is usually saved in various data repositories.In order to obtain a full system overview, it is necessary to link all that data in a meaningful and easy to interpret way. A step forward from simple telemetry data visualisation has been done by developing a new tool that can combine different data sources and has a powerful graphing capability.This new tool, called SystMon, is developed in iPython an interactive-web browser environment under the philosophy of notebooks which combine the code and the final product. The application can be shared among other colleagues and having the code side by side gives the accessibility to inspect and review the process improving and adding new capabilities to the application. SystMon allows to manipulate, generate andvisualise data in different types of graphs and also to create directly statistical reports. SystMon helps the user tomodel, visualiseand interpret telemetry data in a web-based platform for monitoring the health of systems, understanding short- and long-term behaviour and to anticipate corrective interventions.
KEYWORDS: Coronagraphy, Stars, Principal component analysis, Point spread functions, L band, Adaptive optics, Exoplanets, Space telescopes, Planets, Observatories
In November 2012, we installed an L-band annular groove phase mask (AGPM) vector vortex coronagraph (VVC) inside NACO, the adaptive optics camera of ESO’s Very Large Telescope. The mask, made out of diamond subwavelength gratings has been commissioned, science qualified, and is now offered to the community. Here we report ground-breaking on-sky performance levels in terms of contrast, inner working angle, and discovery space. This new practical demonstration of the VVC, coming a few years after Palomar’s and recent record-breaking lab experiments in the visible (E. Serabyn et al. 2013, these proceedings), shows once again that this new-generation coronagraph has reached a high level of maturity.
GIRAFFE is an intermediate resolution spectrograph covering a wavelength range from 360-930nm and fed by
optical fibers as a part of FLAMES, the multi-object fiber facility mounted at the ESO VLT Kueyen. For some time we sought a new detector for GIRAFFE spectrograph to boost the instrument's red QE (Quantum Efficiency) capabilities, while still retaining very good blue response. We aimed also at reducing the strong fringing present in the red spectra. The adopted solution was an e2v custom 2-layer AR (Anti-Reflection) coated Deep Depletion CCD44-82 CCD. This device was made in a new e2v Technologies AR coating plant and delivered to ESO in mid 2007 with performance that matches predictions. The new CCD was commissioned in May 2008. Here we report on the results.
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