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Register a new userRecovering pyramid WS gain in non-common path aberration correction mode via deformable lens
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Added by
Simonetta Chinellato
Publication date
2018
fields
Physics
and research's language is
English
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It is by now well known that pyramid based wavefront sensors, once in closed loop, have the capability to improve more and more the gain as the reference natural star image size is getting smaller on the pyramid pin. Especially in extreme adaptive optics applications, in order to correct the non-common path aberrations between the scientific and sensing channel, it is common use to inject a certain amount of offset wavefront deformation into the DM(s), departing at the same time the pyramid from the optimal working condition. In this paper we elaborate on the possibility to correct the low order non-common path aberrations at the pyramid wavefront sensor level by means of an adaptive refractive lens placed on the optical path before the pyramid itself, allowing the mitigation of the gain loss.
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The Gemini Planet Imager (GPI) entered on-sky commissioning phase, and had its First Light at the Gemini South telescope in November 2013. Meanwhile, the fast loops for atmospheric correction of the Extreme Adaptive Optics (XAO) system have been closed on many dozen stars at different magnitudes (I=4-8), elevation angles and a variety of seeing conditions, and a stable loop performance was achieved from the beginning. Ultimate contrast performance requires a very low residual wavefront error (design goal 60 nm RMS), and optimization of the planet finding instrument on different ends has just begun to deepen and widen its dark hole region. Laboratory raw contrast benchmarks are in the order of 10^-6 or smaller. In the telescope environment and in standard operations new challenges are faced (changing gravity, temperature, vibrations) that are tackled by a variety of techniques such as Kalman filtering, open-loop models to keep alignment to within 5 mas, speckle nulling, and a calibration unit (CAL). The CAL unit was especially designed by the Jet Propulsion Laboratory to control slowly varying wavefront errors at the focal plane of the apodized Lyot coronagraph by the means of two wavefront sensors: 1) a 7x7 low order Shack-Hartmann SH wavefront sensor (LOWFS), and 2) a special Mach-Zehnder interferometer for mid-order spatial frequencies (HOWFS) - atypical in that the beam is split in the focal plane via a pinhole but recombined in the pupil plane with a beamsplitter. The original design goal aimed for sensing and correcting on a level of a few nm which is extremely challenging in a telescope environment. This paper focuses on non-common path low order wavefront correction as achieved through the CAL unit on sky. We will present the obtained results as well as explain challenges that we are facing.
Non Common Path Aberrations (NCPA) are often considered as a critical issue in Adaptive Optics (AO) systems, since they introduce bias errors between real wavefronts propagating to the science detectors and those measured by the Wavefront Sensor (WFS). This is especially true when the AO system is coupled to a coronagraph instrument intended for the discovery and characterization of extra-solar planets, because useful planet signals could be mistaken with residual speckles generated by NCPA. Therefore, compensating for those errors is of prime importance and is already the scope of a few theoretical studies and experimental validations on-sky. This communication presents the conceptual optical design of a pseudo-interferometer arrangement suitable to accurate NCPA calibration, based on two WFS cooperating in real-time. The concept is applicable to both classical imaging and spectroscopy assisted by AO, and to high-contrast coronagraphs searching for habitable extra-solar planets. Practical aspects are discussed, such as the choice of WFS and coronagraph types, or specific requirements on additional hardware components, e.g. dichroic beamsplitters
Circumstellar environments are now routinely observed by dedicated high-contrast imagers on large, ground-based observatories. These facilities combine extreme adaptive optics and coronagraphy to achieve unprecedented sensitivities for exoplanet detection and spectral characterization. However, non-common path aberrations (NCPA) in these coronagraphic systems represent a critical limitation for the detection of giant planets with a contrast lower than a few $10^{-6}$ at very small separations ($<$0.3$^{primeprime}$) from their host star. In 2013 we proposed ZELDA, a Zernike wavefront sensor to measure these residual quasi-static phase aberrations and a prototype was installed in SPHERE, the exoplanet imager for the VLT. In 2016, we demonstrated the ability of our sensor to provide a nanometric calibration and compensation for these aberrations on an internal source in the instrument, resulting in a contrast gain of 10 at 0.2$^{primeprime}$ in coronagraphic images. However, initial on-sky tests in 2017 did not show a tangible gain in contrast when calibrating the NCPA internally and then applying the correction on sky. In this communication, we present recent on-sky measurements to demonstrate the potential of our sensor for the NCPA compensation during observations and quantify the contrast gain in coronagraphic data.
The two main advantages of exoplanet imaging are the discovery of objects in the outer part of stellar systems -- constraining models of planet formation --, and its ability to spectrally characterize the planets -- information on their atmosphere. It is however challenging because exoplanets are up to 1e10 times fainter than their star and separated by a fraction of arcsecond. Current instruments like SPHERE/VLT or GPI/Gemini detect young and massive planets because they are limited by non-common path aberrations (NCPA) that are not corrected by the adaptive optics system. To probe fainter exoplanets, new instruments capable of minimizing the NCPA is needed. One solution is the self-coherent camera (SCC) focal plane wavefront sensor, whose performance was demonstrated in laboratory attenuating the starlight by factors up to several 1e8 in space-like conditions at angular separations down to 2L/D. In this paper, we demonstrate the SCC on the sky for the first time. We installed an SCC on the stellar double coronagraph (SDC) instrument at the Hale telescope. We used an internal source to minimize the NCPA that limited the vortex coronagraph performance. We then compared to the standard procedure used at Palomar. On internal source, we demonstrated that the SCC improves the coronagraphic detection limit by a factor between 4 and 20 between 1.5 and 5L/D. Using this SCC calibration, the on-sky contrast is improved by a factor of 5 between 2 and 4L/D. These results prove the ability of the SCC to be implemented in an existing instrument. This paper highlights two interests of the self-coherent camera. First, the SCC can minimize the speckle intensity in the field of view especially the ones that are very close to the star where many exoplanets are to be discovered. Then, the SCC has a 100% efficiency with science time as each image can be used for both science and NCPA minimization.
Balancing group teaching and individual mentoring is an important issue in education area. The nature behind this issue is to explore common characteristics shared by multiple students and individual characteristics for each student. Biclustering methods have been proved successful for detecting meaningful patterns with the goal of driving group instructions based on students characteristics. However, these methods ignore the individual characteristics of students as they only focus on common characteristics of students. In this article, we propose a framework to detect both group characteristics and individual characteristics of students simultaneously. We assume that the characteristics matrix of students is composed of two parts: one is a low-rank matrix representing the common characteristics of students; the other is a sparse matrix representing individual characteristics of students. Thus, we treat the balancing issue as a matrix recovering problem. The experiment results show the effectiveness of our method. Firstly, it can detect meaningful biclusters that are comparable with the state-of-the-art biclutering algorithms. Secondly, it can identify individual characteristics for each student simultaneously. Both the source code of our algorithm and the real datasets are available upon request.
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