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Abstract:

<jats:p>From Pioneer 10 to Juno, which is still active, several missions and space observatories have studied Jupiter&amp;#8217;s atmosphere. Complementary, although limited by the telluric bands of water vapor, ground-based observations continue providing information about its vertical structure and its distribution around the planet. The main chemical composition of Jupiter&amp;#8217;s atmosphere has been unraveled but lots of questions still remain open, such as the global abundance of water, the responsible chemistry for the coloration of the clouds, or what drives the aurora [1-2]. Moreover, observations by NIMS/Galileo [3-4] and VIMS/Cassini [5], have demonstrated the remarkable potential of VIS-NIR spectrometry for characterizing the composition and dynamics of planetary atmospheres [6].The Moons And Jupiter Imaging Spectrometer (MAJIS) instrument is part of the science payload of the ESA L-Class mission JUICE (Jupiter ICy Moons Explorer) [7] to be launched in 2022 with an arrival at Jupiter in 2031. MAJIS combines two spectral channels able to cover the 0.5 &amp;#8211; 2.35 &amp;#956;m range (VIS-NIR channel) and the 2.25 &amp;#8211; 5.54 &amp;#956;m range (IR channel) [8]. As part of its scientific objectives, MAJIS will investigate the composition, structure, dynamics and evolution of Jupiter&amp;#8217;s atmosphere at different levels, trace tropospheric cloud features, and characterize major and minor species, aerosols properties, and hot spots [9]. As explained by Langevin et al. [9], the spectral resolving power of MAJIS exceeds by three times that of NIMS or VIMS, with a spatial resolution four times better than NIMS, so it will efficiently track tropospheric processes such as clouds and hazes. Moreover, the close to equatorial orbit of JUICE for most of the mission will provide a comprehensive coverage of Jupiter in local time complementary to JIRAM/Juno [9].We are interested in the scientific analysis of the MAJIS observations regarding the composition of Jupiter&amp;#8217;s atmosphere, specifically on the H2O and CH4 contents, which are the most abundant species in the troposphere as a whole, after H2 and He [1]. Although it is expected that water vapor has a higher global volume mixing ratio than CH4 in the deep troposphere, this has yet to be observed [1]. Additionally, the strong spectral features due to crystalline water ice (1.5 &amp;#181;m and 2.0 &amp;#181;m) require a large abundance of water to be explained [10]. Therefore, we would like to perform simulations of different test cases with respect to the viewing geometries of MAJIS and the technical properties of its Flight Model VIS-NIR detector [11].To proceed, we need to adapt the Radiative Transfer code developed at the Belgian Institute for Space Aeronomy (BIRA-IASB), ASIMUT-ALVL. It has been extensively used to characterize Mars and Venus atmospheres [12-19]. This tool is able to perform forward model simulations and atmospheric spectrum retrievals in nadir and limb geometries. To apply it to Jupiter&amp;#8217;s atmosphere, some changes need to be done, such as implementing Jupiter&amp;#8217;s physical parameters and adding the Rayleigh scattering contribution due to the dominant atmospheric species H2 and He. A more demanding modification to the code concerns the treatment of the Collision-Induced Absorption (CIA) due to H2-H2 and H2-He molecular systems.A typical atmosphere&amp;#8217;s vertical structure of Jupiter has been retrieved from [20-21]. The molecular line-lists and cross-sections have been implemented from the HITRAN online database with line parameters adequate for an H2-dominant atmosphere. Additionally, the microphysical parameters of the clouds and aerosols have been obtained from [22]. The different contributions to the spectra are being identified then simulated and finally validated through comparison with previous works [20-21]. This methodology ensures that each radiative contribution is well-understood and correctly implemented into ASIMUT-ALVL before assessing the performances of the MAJIS VIS-NIR channel to characterize the vertical structure of the Jovian atmosphere.In this presentation, we will describe the different contributions and the challenges we faced for their implementation. A preliminary sensitivity analysis of MAJIS VIS-NIR will be discussed.AcknowledgmentsThis project acknowledges the support of M. L&amp;#243;pez-Puertas and the funding provided by the Scientific Research Fund (FNRS) through the Aspirant Grant: 34828772 MAJIS detectors and impact on science.References[1] Mc Grath, M.A., et al., Ed. 2004, Cambridge University Press, p. 59-77.[2] MAJIS Team, JUICE Definition 51������� Report, 2014.[3] Irwin, P.G.J., et al. Icarus, 2001. 149(2): p. 397-415.[4] Baines, K.H., et al. Icarus, 2002. 159(1): p. 74-94.[5] Brown, R.H., et al. Icarus, 2003. 164(2): p. 461-470.[6] Langevin, Y., et al., Lunar and Planetary Science Conference, 2014.[7] Grasset, O., et al., Planetary and Space Science, Vol. 78, pp. 1-21, 2013.[8] Guerri, I., et al., International Society for Optics and Photonics, Vol. 10690, 2018.[9] Langevin, Y., et al., EPSC, 2013. P. EPSC2013-548-1.[10] Grassi, D., et al., Journal of Geophysical Research: Planets, 2020. 125.4: e2019JE006206.[11] Cisneros-Gonz&amp;#225;lez, M. E. et al., Space Telescopes and Instrumentation in Proc. SPIE 2020, 11443, 114431L.[12] Montmessin, F., et al. Icarus, 2017. 297: p. 195-216.[13] Vandaele, A.C., et al. Optics Express, 2013. 21(18): p. 21148.[14] Vandaele , A.C., et al. Adv. Space Res., 2016. 57: p. 443-458.[15] Vandaele , A.C., et al. Icarus, 2016. 272: p. 48-59.[16] Vandaele, A.C., et al. Icarus, 2017. 295: p. 1-15.[17] Vandaele, A.C., et al. Planet. Space Sci., 2015. 119: p. 233-249.[18] Neefs, E., et al., Applied Optics, 2015. 54(28): p. 8494-8520.[19] Robert, S., et al., Planet. Space Sci., 2016. 124: p. 94-104.[20] L&amp;#243;pez-Puertas, M., et al., The Astronomical Journal, 2018. 156.4: 169.[21] Guerlet, S., et al. Icarus, 2020. 351: 113935.[22] Monta&amp;#241;&amp;#233;s-Rodr&amp;#237;guez, P., et al., The Astrophysical Journal Letters, 2015, vol. 801, no 1, p. L8.</jats:p>

Abstract:

In this paper we present the European Low Frequency Survey (ELFS), a project that will enable the detection of primordial B-mode polarization by measuring the Galactic and extra-Galactic foregrounds in the 5– 120 GHz frequency window. Indeed, the main difficulty in measuring the Bmode polarization comes not just from its sheer faintness, but from the fact that many other objects in the Universe also emit polarized microwaves, which mask the faint CMB signal. The first stage of this project will be carried out in synergy with the Simons Array (SA) collaboration, installing a 5.5–11 GHz coherent receiver at the focus of one of the three 3.5m SA telescopes in Atacama, Chile (“ELFS on SA”). The receiver will be equipped with a fully digital backend based on the latest Xilinx RF System-on-Chip devices that will provide frequency resolution of 1MHz across the whole observing band, allowing us to clean the scientific signal from unwanted radio frequency interference, particularly from low-Earth orbit satellite mega-constellations. This paper reviews the scientific motivation for ELFS and its instrumental characteristics, and provides an update on the development of ELFS on SA

Abstract:

The arrival of the Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST), Euclid-Wide and Roman wide-area sensitive surveys will herald a new era in strong lens science in which the number of strong lenses known is expected to rise from to. However, current lens-finding methods still require time-consuming follow-up visual inspection by strong lens experts to remove false positives which is only set to increase with these surveys. In this work, we demonstrate a range of methods to produce calibrated probabilities to help determine the veracity of any given lens candidate. To do this we use the classifications from citizen science and multiple neural networks for galaxies selected from the Hyper Suprime-Cam survey. Our methodology is not restricted to particular classifier types and could be applied to any strong lens classifier which produces quantitative scores. Using these calibrated probabilities, we generate an ensemble classifier, combining citizen science, and neural network lens finders. We find such an ensemble can provide improved classification over the individual classifiers. We find a false-positive rate of 10-3 can be achieved with a completeness of 46 per cent, compared to 34 per cent for the best individual classifier. Given the large number of galaxy-galaxy strong lenses anticipated in LSST, such improvement would still produce significant numbers of false positives, in which case using calibrated probabilities will be essential for population analysis of large populations of lenses and to help prioritize candidates for follow-up.