Space-qualified liquid-crystal variable retarders for wide-field-of-view coronagraphs

Bibcode
2011SPIE.8148E..31U
DOI
10.1117/12.904919
Uribe-Patarroyo, N.; Alvarez-Herrero, A.; García Parejo, P.; Vargas, J.; Heredero, R. L.; Restrepo, R.; Martínez-Pillet, V.; Del Toro Iniesta, J. C.; López, A.; Fineschi, S.; Capobianco, G.; Georges, M.; López, M.; Boer, G.; Manolis, I.
Bibliographical reference

Solar Physics and Space Weather Instrumentation IV. Edited by Fineschi, Silvano; Fennelly, Judy. Proceedings of the SPIE, Volume 8148, pp. 814810-814810-12 (2011).

Advertised on:
9
2011
Number of authors
15
IAC number of authors
1
Citations
0
Refereed citations
0
Description
Liquid-crystal variable retarders (LCVRs) are an emergent technology for space-based polarimeters, following its success as polarization modulators in ground-based polarimeters and ellipsometers. Wide-field double nematic LCVRs address the high angular sensitivity of nematic LCVRs at some voltage regimes. We present a work in which wide-field LCVRs were designed and built, which are suitable for wide-field-of-view instruments such as polarimetric coronagraphs. A detailed model of their angular acceptance was made, and we validated this technology for space environmental conditions, including a campaign studying the effects of gamma, proton irradiation, vibration and shock, thermo-vacuum and ultraviolet radiation.
Type
Non-refereed

It may interest you

  • Non-refereed
    CONCERTO: Instrument and status
    CONCERTO (CarbON CII line in post-rEionization and ReionizaTiOn) is a low-resolution Fourier transform spectrometer dedicated to the study of star-forming galaxies and clusters of galaxies in the transparent millimeter windows from the ground. It is characterized by a wide instantaneous 18.6 arcmin field of view, operates at 130-310 GHz, and was
    Fasano, Alessandro et al.

    Advertised on:

    6
    2024
    Bibcode
    2024EPJWC.29300018F
    Citations
    0
  • Non-refereed
    Generating galaxy clusters mass density maps from mock multiview images via deep learning
    Galaxy clusters are composed of dark matter, gas and stars. Their dark matter component, which amounts to around 80% of the total mass, cannot be directly observed but traced by the distribution of diffused gas and galaxy members. In this work, we aim to infer the cluster's projected total mass distribution from mock observational data, i.e. stars
    de Andres, Daniel et al.

    Advertised on:

    6
    2024
    Bibcode
    2024EPJWC.29300013D
    Citations
    0
  • Non-refereed
    A machine learning method to infer clusters of galaxies mass radial profiles from mock Sunyaev-Zel'dovich maps with The Three Hundred clusters
    Our study introduces a new machine learning algorithm for estimating 3D cumulative radial profiles of total and gas mass in galaxy clusters from thermal Sunyaev-Zel'dovich (SZ) effect maps. We generate mock images from 2522 simulated clusters, employing an autoencoder and random forest in our approach. Notably, our model makes no prior assumptions
    Ferragamo, A. et al.

    Advertised on:

    6
    2024
    Bibcode
    2024EPJWC.29300019F
    Citations
    0
Go back