Innovation at the Keck Observatory

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Humanities, Program in Writing and Humanistic Studies, 2005.Includes bibliographical references (leaves 27-30).A study of historical, current, and future developments at the Keck Observatory revealed a thriving philosophy of innovation. Intended to defy obsoletion and keep the observatory competitive over long time scales, this philosophy continues to resonate with Keck Observatory scientists. The Keck Observatory consists of two 1 0-meter telescopes situated near the apex of Mauna Kea on the big island of Hawaii. Three main innovations keep the observatory competitive. The observatory contains the first modem active optics-controlled segmented primary mirror, principally designed by Dr. Jerry Nelson. Though it currently reigns as the world's largest aperture at 10 meters, monolithic mirror supporters still question its viability. The observatory also links both primary mirrors together as a single 20-meter telescope using interferometry. Finally, the observatory employs both a natural and laser guide star adaptive optics system. Forward-thinking Keck scientists, however, are researching multi-conjugate adaptive optics systems. As a result of its innovations, Keck has retained its position as a major player in the realm of observational astronomy for over a decade.by Monica Godha Bobra.S.M

The Helioseismic and Magnetic Imager (HMI) Vector Magnetic Field Pipeline: Overview and Performance

The Helioseismic and Magnetic Imager (HMI) began near-continuous full-disk solar measurements on 1 May 2010 from the Solar Dynamics Observatory (SDO). An automated processing pipeline keeps pace with observations to produce observable quantities, including the photospheric vector magnetic field, from sequences of filtergrams. The primary 720s observables were released in mid 2010, including Stokes polarization parameters measured at six wavelengths as well as intensity, Doppler velocity, and the line-of-sight magnetic field. More advanced products, including the full vector magnetic field, are now available. Automatically identified HMI Active Region Patches (HARPs) track the location and shape of magnetic regions throughout their lifetime. The vector field is computed using the Very Fast Inversion of the Stokes Vector (VFISV) code optimized for the HMI pipeline; the remaining 180 degree azimuth ambiguity is resolved with the Minimum Energy (ME0) code. The Milne-Eddington inversion is performed on all full-disk HMI observations. The disambiguation, until recently run only on HARP regions, is now implemented for the full disk. Vector and scalar quantities in the patches are used to derive active region indices potentially useful for forecasting; the data maps and indices are collected in the SHARP data series, hmi.sharp_720s. Patches are provided in both CCD and heliographic coordinates. HMI provides continuous coverage of the vector field, but has modest spatial, spectral, and temporal resolution. Coupled with limitations of the analysis and interpretation techniques, effects of the orbital velocity, and instrument performance, the resulting measurements have a certain dynamic range and sensitivity and are subject to systematic errors and uncertainties that are characterized in this report.Comment: 42 pages, 19 figures, accepted to Solar Physic

Predicting Solar Flares Using CNN and LSTM on Two Solar Cycles of Active Region Data

We consider the flare prediction problem that distinguishes flare-imminent active regions that produce an M- or X-class flare in the future 24 hours, from quiet active regions that do not produce any flare within $\pm 24$ hours. Using line-of-sight magnetograms and parameters of active regions in two data products covering Solar Cycle 23 and 24, we train and evaluate two deep learning algorithms -- CNN and LSTM -- and their stacking ensembles. The decisions of CNN are explained using visual attribution methods. We have the following three main findings. (1) LSTM trained on data from two solar cycles achieves significantly higher True Skill Scores (TSS) than that trained on data from a single solar cycle with a confidence level of at least 0.95. (2) On data from Solar Cycle 23, a stacking ensemble that combines predictions from LSTM and CNN using the TSS criterion achieves significantly higher TSS than the "select-best" strategy with a confidence level of at least 0.95. (3) A visual attribution method called Integrated Gradients is able to attribute the CNN's predictions of flares to the emerging magnetic flux in the active region. It also reveals a limitation of CNN as a flare prediction method using line-of-sight magnetograms: it treats the polarity artifact of line-of-sight magnetograms as positive evidence of flares.Comment: 31 pages, 16 figures, accepted in the Ap

A machine-learning data set prepared from the NASA solar dynamics observatory mission

In this paper, we present a curated data set from the NASA Solar Dynamics Observatory (SDO) mission in a format suitable for machine-learning research. Beginning from level 1 scientific products we have processed various instrumental corrections, down-sampled to manageable spatial and temporal resolutions, and synchronized observations spatially and temporally. We illustrate the use of this data set with two example applications: forecasting future extreme ultraviolet (EUV) Variability Experiment (EVE) irradiance from present EVE irradiance and translating Helioseismic and Magnetic Imager observations into Atmospheric Imaging Assembly observations. For each application, we provide metrics and baselines for future model comparison. We anticipate this curated data set will facilitate machine-learning research in heliophysics and the physical sciences generally, increasing the scientific return of the SDO mission. This work is a direct result of the 2018 NASA Frontier Development Laboratory Program. Please see the Appendix for access to the data set, totaling 6.5TBs

A Survey of Computational Tools in Solar Physics

The SunPy Project developed a 13-question survey to understand the software and hardware usage of the solar physics community. 364 members of the solar physics community, across 35 countries, responded to our survey. We found that 99$\pm$0.5% of respondents use software in their research and 66% use the Python scientific software stack. Students are twice as likely as faculty, staff scientists, and researchers to use Python rather than Interactive Data Language (IDL). In this respect, the astrophysics and solar physics communities differ widely: 78% of solar physics faculty, staff scientists, and researchers in our sample uses IDL, compared with 44% of astrophysics faculty and scientists sampled by Momcheva and Tollerud (2015). 63$\pm$4% of respondents have not taken any computer-science courses at an undergraduate or graduate level. We also found that most respondents utilize consumer hardware to run software for solar-physics research. Although 82% of respondents work with data from space-based or ground-based missions, some of which (e.g. the Solar Dynamics Observatory and Daniel K. Inouye Solar Telescope) produce terabytes of data a day, 14% use a regional or national cluster, 5% use a commercial cloud provider, and 29% use exclusively a laptop or desktop. Finally, we found that 73$\pm$4% of respondents cite scientific software in their research, although only 42$\pm$3% do so routinely