Real time wavefront control system for the Large Synoptic Survey Telescope (LSST)

The LSST is an integrated, ground based survey system designed to conduct a decade-long time domain survey of the optical sky. It consists of an 8-meter class wide-field telescope, a 3.2 Gpixel camera, and an automated data processing system. In order to realize the scientific potential of the LSST, its optical system has to provide excellent and consistent image quality across the entire 3.5 degree Field of View. The purpose of the Active Optics System (AOS) is to optimize the image quality by controlling the surface figures of the telescope mirrors and maintaining the relative positions of the optical elements. The basic challenge of the wavefront sensor feedback loop for an LSST type 3-mirror telescope is the near degeneracy of the influence function linking optical degrees of freedom to the measured wavefront errors. Our approach to mitigate this problem is modal control, where a limited number of modes (combinations of optical degrees of freedom) are operated at the sampling rate of the wavefront sensing, while the control bandwidth for the barely observable modes is significantly lower. The paper presents a control strategy based on linear approximations to the system, and the verification of this strategy against system requirements by simulations using more complete, non-linear models for LSST optics and the curvature wavefront sensors

LSST Active Optics System Software Architecture

The Large Synoptic Survey Telescope (LSST) is an 8-meter class wide-field telescope now under construction on Cerro Pachon, near La Serena, Chile. This ground-based telescope is designed to conduct a decade-long time domain survey of the optical sky. In order to achieve the LSST scientific goals, the telescope requires delivering seeing limited image quality over the 3.5 degree field-of-view. Like many telescopes, LSST will use an Active Optics System (AOS) to correct in near real-time the system aberrations primarily introduced by gravity and temperature gradients. The LSST AOS uses a combination of 4 curvature wavefront sensors (CWS) located on the outside of the LSST field-of-view. The information coming from the 4 CWS is combined to calculate the appropriate corrections to be sent to the 3 different mirrors composing LSST. The AOS software incorporates a wavefront sensor estimation pipeline (WEP) and an active optics control system (AOCS). The WEP estimates the wavefront residual error from the CWS images. The AOCS determines the correction to be sent to the different degrees of freedom every 30 seconds. In this paper, we describe the design and implementation of the AOS. More particularly, we will focus on the software architecture as well as the AOS interactions with the various subsystems within LSST

LSST Science Book, Version 2.0

A survey that can cover the sky in optical bands over wide fields to faint magnitudes with a fast cadence will enable many of the exciting science opportunities of the next decade. The Large Synoptic Survey Telescope (LSST) will have an effective aperture of 6.7 meters and an imaging camera with field of view of 9.6 deg^2, and will be devoted to a ten-year imaging survey over 20,000 deg^2 south of +15 deg. Each pointing will be imaged 2000 times with fifteen second exposures in six broad bands from 0.35 to 1.1 microns, to a total point-source depth of r~27.5. The LSST Science Book describes the basic parameters of the LSST hardware, software, and observing plans. The book discusses educational and outreach opportunities, then goes on to describe a broad range of science that LSST will revolutionize: mapping the inner and outer Solar System, stellar populations in the Milky Way and nearby galaxies, the structure of the Milky Way disk and halo and other objects in the Local Volume, transient and variable objects both at low and high redshift, and the properties of normal and active galaxies at low and high redshift. It then turns to far-field cosmological topics, exploring properties of supernovae to z~1, strong and weak lensing, the large-scale distribution of galaxies and baryon oscillations, and how these different probes may be combined to constrain cosmological models and the physics of dark energy.Comment: 596 pages. Also available at full resolution at http://www.lsst.org/lsst/sciboo

Real time wavefront control system for the Large Synoptic Survey Telescope (LSST)

The LSST is an integrated, ground based survey system designed to conduct a decade-long time domain survey of the optical sky. It consists of an 8-meter class wide-field telescope, a 3.2 Gpixel camera, and an automated data processing system. In order to realize the scientific potential of the LSST, its optical system has to provide excellent and consistent image quality across the entire 3.5 degree Field of View. The purpose of the Active Optics System (AOS) is to optimize the image quality by controlling the surface figures of the telescope mirrors and maintaining the relative positions of the optical elements. The basic challenge of the wavefront sensor feedback loop for an LSST type 3-mirror telescope is the near degeneracy of the influence function linking optical degrees of freedom to the measured wavefront errors. Our approach to mitigate this problem is modal control, where a limited number of modes (combinations of optical degrees of freedom) are operated at the sampling rate of the wavefront sensing, while the control bandwidth for the barely observable modes is significantly lower. The paper presents a control strategy based on linear approximations to the system, and the verification of this strategy against system requirements by simulations using more complete, non-linear models for LSST optics and the curvature wavefront sensors

LSST Active Optics System Software Architecture

The Large Synoptic Survey Telescope (LSST) is an 8-meter class wide-field telescope now under construction on Cerro Pachon, near La Serena, Chile. This ground-based telescope is designed to conduct a decade-long time domain survey of the optical sky. In order to achieve the LSST scientific goals, the telescope requires delivering seeing limited image quality over the 3.5 degree field-of-view. Like many telescopes, LSST will use an Active Optics System (AOS) to correct in near real-time the system aberrations primarily introduced by gravity and temperature gradients. The LSST AOS uses a combination of 4 curvature wavefront sensors (CWS) located on the outside of the LSST field-of-view. The information coming from the 4 CWS is combined to calculate the appropriate corrections to be sent to the 3 different mirrors composing LSST. The AOS software incorporates a wavefront sensor estimation pipeline (WEP) and an active optics control system (AOCS). The WEP estimates the wavefront residual error from the CWS images. The AOCS determines the correction to be sent to the different degrees of freedom every 30 seconds. In this paper, we describe the design and implementation of the AOS. More particularly, we will focus on the software architecture as well as the AOS interactions with the various subsystems within LSST

The guider and wavefront curvature sensor subsystem for the Large Synoptic Survey Telescope

The Large Synoptic Survey Telescope instrument include four guiding and wavefront sensing subsystems called corner raft subsystems, in addition to the main science array of 189 4K x 4K CCDs. These four subsystems are placed at the four corners of the instrumented field of view. Each wavefront/guiding subsystem comprises a pair of 4K x 4K guide sensors, capable of producing 9 frames/second, and a pair of offset 2K x 4K wavefront curvature sensors from which the images are read out at the cadence of the main camera system, providing 15 sec integrations. These four guider/wavefront corner rafts are mechanically and electrically isolated from the science sensor rafts and can be installed or removed independently from any other focal plane subsystem. We present the implementation of this LSST subsystem detailing both hardware and software development and status.Astronom

Artificial Intelligence, Machine Learning and Modeling for Understanding the Oceans and Climate Change

International audienceThe ongoing transformation of climate and biodiversity will have a drastic impact on almost all forms of life in the ocean with further consequences on food security, ecosystem services in coastal and inland communities. Despite these impacts, scientific data and infrastructures are still lacking to understand and quantify the consequences of these perturbations on the marine ecosystem. Understanding this phenomenon is not only an urgent but also a scientifically demanding task. Consequently, it is a problem that must be addressed with a tific cohort approach, where multi-disciplinary teams collaborate to bring the best of different scientific areas. In this proposal paper, we describe our newly launched four-years project focusedon developing new artificial intelligence, machine learning, and mathematical modeling tools to contribute to the understanding of the structure, functioning, and underlying mechanisms and dynamics of the global ocean symbiome and its relation with climate change. These actions should enable the understanding of our oceans and predict and mitigate the consequences of climate and biodiversity changes

Twelve numerical, symbolic and hybrid supervised classification methods

International audienceSupervised classification has already been the subject of numerous studies in the fields of Statistics, Pattern Recognition and Artificial Intelligence under various appellations which include discriminant analysis, discrimination and concept learning. Many practical applications relating to this field have been developed. New methods have appeared in recent years, due to developments concerning Neural Networks and Machine Learning. These "hybrid" approaches share one common factor in that they combine symbolic and numerical aspects. The former are characterized by the representation of knowledge, the latter by the introduction of frequencies and probabilistic criteria. In the present study, we shall present a certain number of hybrid methods, conceived (or improved) by members of the SYMENU research group. These methods issue mainly from Machine Learning and from research on Classification Trees done in Statistics, and they may also be qualified as "rule-based". They shall be compared with other more classical approaches. This comparison will be based on a detailed description of each of the twelve methods envisaged, and on the results obtained concerning the "Waveform Recognition Problem" proposed by Breiman et al which is difficult for rule based approaches

LSST control software component design

Construction of the Large Synoptic Survey Telescope system involves several different organizations, a situation that poses many challenges at the time of the software integration of the components. To ensure commonality for the purposes of usability, maintainability, and robustness, the LSST software teams have agreed to the following for system software components: a summary state machine, a manner of managing settings, a flexible solution to specify controller/controllee relationships reliably as needed, and a paradigm for responding to and communicating alarms. This paper describes these agreed solutions and the factors that motivated these

Clinical Consensus Guideline on the Management of Phaeochromocytoma and Paraganglioma in Patients Harbouring Germline SDHD Pathogenic Variants

Patients with germline SDHD pathogenic variants (encoding succinate dehydrogenase subunit D; ie, paraganglioma 1 syndrome) are predominantly affected by head and neck paragangliomas, which, in almost 20% of patients, might coexist with paragangliomas arising from other locations (eg, adrenal medulla, para-aortic, cardiac or thoracic, and pelvic). Given the higher risk of tumour multifocality and bilaterality for phaeochromocytomas and paragangliomas (PPGLs) because of SDHD pathogenic variants than for their sporadic and other genotypic counterparts, the management of patients with SDHD PPGLs is clinically complex in terms of imaging, treatment, and management options. Furthermore, locally aggressive disease can be discovered at a young age or late in the disease course, which presents challenges in balancing surgical intervention with various medical and radiotherapeutic approaches. The axiom-first, do no harm-should always be considered and an initial period of observation (ie, watchful waiting) is often appropriate to characterise tumour behaviour in patients with these pathogenic variants. These patients should be referred to specialised high-volume medical centres. This consensus guideline aims to help physicians with the clinical decision-making process when caring for patients with SDHD PPGLs