Changes between Version 572 and Version 573 of AstroTechTalk
- Timestamp:
- 18 Apr 2023, 11:49:44 (13 months ago)
Legend:
- Unmodified
- Added
- Removed
- Modified
-
AstroTechTalk
v572 v573 36 36 || 10.04.2023 || Holiday || -- || 37 37 || '''17.04.2023''' || '''Jiao He, Tushar Suhasaria''' || '''An overview on the experimental setups in the Origins of Life Lab[[BR]][[BR]]'''Over the last two decades, laboratory astrochemistry has played an immense role in elucidating how some of the complex organic molecules (COMs) are formed from simple molecular ice in the coldest regions of dense molecular cloud. Chemistry in the solid state can be driven by either the non-energetic processing (atom bombardment) or energetic processing (irradiation with ions, electrons and protons). Organic molecules continue to evolve as interstellar material transit from molecular clouds to planetary systems. A deeper understanding of the physical and chemical processes involved in the formation and evolution of prebiotic COMs may provide valuable insights into the origins of life. To this end, we have two setups in the origins of life lab that work in ultra-high vacuum regime and at cryogenic temperatures to mimic the space conditions. [[BR]][[BR]]In the first setup, we focus on the formation of molecules by atom addition reactions in the solid state. We use quadrupole mass spectrometer and Infrared spectroscopy to monitor changes in the ice. In the second setup, we can look at the energetic processing of single or multicomponent ices by energetic electrons or UV photons. In addition to IR spectroscopy, we will also employ a tunable ns-IR laser to first desorb and then a ns-UV laser to ionize molecules produced in the ice mixture. The formed ions will then be guided to a very high resolution Orbitrap mass spectrometer for in-situ detection. [[BR]][[BR]]The work of the Origins of Life laboratory will provide crucial data to the astrochemical and astrophysical community.[[BR]][[BR]]Presentation: English & German[[BR]]Slides: English[[BR]]Questions: German, English || 38 || '''24.04.2023''' || '''Walter Seifert (LSW)''' || The 4MOST High-Resolution-Spectrograph ||38 || '''24.04.2023''' || '''Walter Seifert (LSW)''' || '''Instrumentation projects at the Landessternwarte (LSW)[[BR]][[BR]]'''The LSW is involved in several instrumentation projects for medium and large telescopes. During the talk these will be presented and, of course, our contributions will be discussed in particular.[[BR]][[BR]]Details of the technical solutions or approaches for the instruments will be described, as well as the current status of the projects. Besides CUBES (ESO VLT), MOSAIC (ESO ELT), CARMENES PLUS (CAHA 3.5), ANDES K-band spectrograph (with MPIA, ESO ELT) and 2ES (!LaSilla 2.2) the focus will be on the 4MOST high resolution spectrograph for the ESO VISTA telescope.[[BR]][[BR]]Presentation: German[[BR]]Slides: English[[BR]]Questions: German, English || 39 39 || 01.05.2023 || Holiday || -- || 40 40 || '''08.05.2023''' || '''Jalo Nousiainen[[BR]](LUT-Universität, Lappeenranta, Finnland)''' || '''Model-based reinforcement learning and inverse problems in extreme adaptive optics control[[BR]][[BR]]'''The control of eXtreme Adaptive Optics (XAO) systems is crucial for the direct imaging of potentially habitable exoplanets on ground-based telescopes. However, current XAO control laws leave strong residuals, particularly at small angular separations from host stars where most habitable exoplanets are located. To address this issue, our recent work has focused on two approaches: Model-based Reinforcement Learning (MBRL) and spatio-temporal Gaussian process (ST-GP) regression.[[BR]]MBRL is a data-driven approach that learns control strategies from system feedback and promises to effectively manage factors that can hamper XAO performance, such as temporal delay, calibration errors, photon noise, and optical gains. I will present recent results from the GHOST test bench at ESO and discuss our future goals.[[BR]]ST-GP regression, however, allows for the theoretical examination of predictive control strategies. Factors that affect predictive controllers' performance include the wavefront sensor type, measurement noise level, AO system geometry (aliasing, actuator spacing), and atmospheric conditions (e.g., seeing, wind speed). Through ST-GP regression, we can explore the theoretical limits of predictive control under different conditions and geometries.[[BR]]Overall, our work aims to advance XAO control methods to enable high-contrast imaging of potentially habitable exoplanets using ground-based telescopes.[[BR]][[BR]]Presentation: English[[BR]]Slides: English[[BR]]Questions: German, English ||
