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Marina S. Pavlovic

Publications and source records attributed to Marina S. Pavlovic.

2 recordsLinked to original sources

Rubin M1M3 Dynamic performance : stability and actuation during operations

The Vera C. Rubin Observatory is preparing to commence the Legacy Survey of Space and Time with the fully integrated Simonyi Survey Telescope. To verify that the primary/tertiary (M1M3) mirror system is ready to meet the demanding survey requirements, dynamic tests of the 8.4 m, 53 ton M1M3 system were conducted to assess safety, stability, and image quality under realistic operating conditions. The M1M3 is supported by 156 pneumatic force actuators and positioned, relative to its mirror cell, by six hardpoint actuators that together must counteract gravitational and inertial loads during rapid telescope motion. The Rubin Observatory telescope mount is capable of moving at a rate that meets its nominal motion requirements, and can approach it maximum allowable values that are 50 percent higher. Even at just 20 % of its operational speed, it is an exceptionally fast motion for such a large structure. After slewing, the system must stabilize and dampen vibrations within 5 seconds to ensure image quality during observations. Achieving this rapid settling requires precise control of 156 force actuators, which must adjust dynamically with changes in telescope elevation to compensate for gravity effects. We present results for M1M3 from a comprehensive series of TMA dynamical tests spanning the operational envelope of slew velocities and accelerations. The analysis evaluates elevation axis balancing and lookup table updating as we install the M1M3 mirror; slew-and-settle behavior, force response and stability of the pneumatic actuator system across telescope attitudes including responses to the earthquake. The results demonstrate the readiness of the M1M3 subsystem for routine survey operations and provide validation data for ongoing performance modeling.

astro-ph.IM↗

Deep learning of quasar lightcurves in the LSST era

Deep learning techniques are required for the analysis of synoptic (multi-band and multi-epoch) light curves in massive data of quasars, as expected from the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST). In this follow-up study, we introduced an upgraded version of a conditional neural process (CNP) embedded in a multistep approach for analysis of large data of quasars in the LSST Active Galactic Nuclei Scientific Collaboration data challenge database. We present a case study of a stratified set of the u-band light curves for 283 quasars with very low variability $\sim 0.03$. In this sample, CNP average mean square error is found to be $\sim 5\% $($\sim 0.5$ mag). Interestingly, beside similar level of variability there are indications that individual light curves show flare like features. According to preliminary structure function analysis, these occurrences may be associated to microlensing events with larger time scales $5-10$ years.

astro-ph.IM↗