The problem of bias, meaning over- or under-estimation, of the component perpendicular to the line-of-sight
B
⊥
in vector magnetic-field maps is discussed. Previous works on this topic have ...illustrated that the problem exists; here we perform novel investigations to quantify the bias, fully understand its source(s), and provide mitigation strategies. First, we develop quantitative metrics to measure the
B
⊥
bias and quantify the effect in both local (physical) and native image-plane components. Second, we test and evaluate different options available to inversions and different data sources, to systematically characterize the impacts of these choices, including explicitly accounting for the magnetic fill fraction
f
f
. Third, we deploy a simple model to test how noise and different models of the bias may manifest. From these three investigations we find that while the bias is dominantly present in under-resolved structures, it is also present in strong-field, pixel-filling structures. Noise in the spectropolarimetric data can exacerbate the problem, but it is not the primary cause of the bias. We show that fitting
f
f
explicitly provides significant mitigation, but that other considerations such as the choice of
χ
2
-weights and optimization algorithms can impact the results as well. Finally, we demonstrate a straightforward “quick fix” that can be applied post facto but prior to solving the
180
∘
ambiguity in
B
⊥
, and which may be useful when global-scale structures are, e.g., used for model boundary input. The conclusions of this work support the deployment of inversion codes that explicitly fit
f
f
or, as with the new SyntHIA neural-net, that are trained on data that did so.
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DOBA, EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, IZUM, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UILJ, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
This paper studies the impact of changing financial frictions on the Great Moderation using an estimated, nonlinear New Keynesian model. The model features financial frictions, parameter drift, and ...stochastic volatility. The estimation results show that financial frictions fell during the 1980s and remained low throughout the Great Moderation. Based on counterfactual studies, the reduction in financial frictions was an important reason for the reduction in volatility observed during the Great Moderation. The results show little role for changing monetary policy or reduced shock volatility, two common explanations, in causing the Great Moderation.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Abstract
We investigate the cross-calibration of the Hinode/Solar Optical Telescope-Spectro-Polarimeter (SOT-SP) and Solar Dynamics Observatory/Helioseismic and Magnetic Imager (SDO/HMI) instrument ...metadata, specifically the correspondence of the scaling and pointing information. Accurate calibration of these data sets gives the correspondence needed by interinstrument studies and learning-based magnetogram systems, and is required for physically meaningful photospheric magnetic field vectors. We approach the problem by robustly fitting geometric models on correspondences between images from each instrument’s pipeline. This technique is common in computer vision, but several critical details are required when using scanning-slit spectrograph data like Hinode/SOT-SP. We apply this technique to data spanning a decade of the Hinode mission. Our results suggest corrections to the published Level 2 Hinode/SOT-SP data. First, an analysis on approximately 2700 scans suggests that the reported pixel size in Hinode/SOT-SP Level 2 data is incorrect by around 1%. Second, analysis of over 12,000 scans shows that the pointing information is often incorrect by dozens of arcseconds with a strong bias. Regression of these corrections indicates that thermal effects have caused secular and cyclic drift in Hinode/SOT-SP pointing data over its mission. We offer two solutions. First, direct coalignment with SDO/HMI data via our procedure can improve alignments for many Hinode/SOT-SP scans. Second, since the pointing errors are predictable, simple post-hoc corrections can substantially improve the pointing. We conclude by illustrating the impact of this updated calibration on derived physical data products needed for research and interpretation. Among other things, our results suggest that the pointing errors induce a hemispheric bias in estimates of radial current density.
Abstract
Both NASA’s Solar Dynamics Observatory (SDO) and the JAXA/NASA Hinode mission include spectropolarimetric instruments designed to measure the photospheric magnetic field. SDO’s Helioseismic ...and Magnetic Imager (HMI) emphasizes full-disk, high-cadence, and good-spatial-resolution data acquisition while Hinode’s Solar Optical Telescope Spectro-Polarimeter (SOT-SP) focuses on high spatial resolution and spectral sampling at the cost of a limited field of view and slower temporal cadence. This work introduces a deep-learning system, named the Synthetic Inversion Approximation (SynthIA), that can enhance both missions by capturing the best of each instrument’s characteristics. We use SynthIA to produce a new magnetogram data product, the Synthetic Hinode Pipeline (SynodeP), that mimics magnetograms from the higher-spectral-resolution Hinode/SOT-SP pipeline, but is derived from full-disk, high-cadence, and lower-spectral-resolution SDO/HMI Stokes observations. Results on held-out data show that SynodeP has good agreement with the Hinode/SOT-SP pipeline inversions, including magnetic fill fraction, which is not provided by the current SDO/HMI pipeline. SynodeP further shows a reduction in the magnitude of the 24 hr oscillations present in the SDO/HMI data. To demonstrate SynthIA’s generality, we show the use of SDO/Atmospheric Imaging Assembly data and subsets of the HMI data as inputs, which enables trade-offs between fidelity to the Hinode/SOT-SP inversions, number of observations used, and temporal artifacts. We discuss possible generalizations of SynthIA and its implications for space-weather modeling. This work is part of the NASA Heliophysics DRIVE Science Center at the University of Michigan under grant NASA 80NSSC20K0600E, and will be open-sourced.
Abstract
The Helioseismic and Magnetic Imager (HMI) on board NASA’s Solar Dynamics Observatory produces estimates of the photospheric magnetic field, which are a critical input to many space weather ...modeling and forecasting systems. The magnetogram products produced by HMI and its analysis pipeline are the result of a per-pixel optimization that estimates solar atmospheric parameters and minimizes disagreement between a synthesized and observed Stokes vector. In this paper, we introduce a deep-learning-based approach that can emulate the existing HMI pipeline results two orders of magnitude faster than the current pipeline algorithms. Our system is a U-Net trained on input Stokes vectors and their accompanying optimization-based Very Fast Inversion of the Stokes Vector (VFISV) inversions. We demonstrate that our system, once trained, can produce high-fidelity estimates of the magnetic field and kinematic and thermodynamic parameters while also producing meaningful confidence intervals. We additionally show that despite penalizing only per-pixel loss terms, our system is able to faithfully reproduce known systematic oscillations in full-disk statistics produced by the pipeline. This emulation system could serve as an initialization for the full Stokes inversion or as an ultrafast proxy inversion. This work is part of the NASA Heliophysics DRIVE Science Center (SOLSTICE) at the University of Michigan, under grant NASA 80NSSC20K0600E, and will be open sourced.
Abstract Vector magnetograms of the Sun’s photosphere are cornerstones for much of solar physics research. These data are often produced by data-analysis pipelines combining per-pixel Stokes ...polarization vector inversion with a disambiguation that resolves an intrinsic 180° ambiguity. We introduce a learning-based method, SuperSynthIA, that produces full-disk vector magnetograms from Stokes vector observations. As input, SuperSynthIA uses Stokes polarization images from Solar Dynamics Observatory (SDO)/Helioseismic and Magnetic Imager (HMI). As output, SuperSynthIA simultaneously emulates the inversion and disambiguation outputs from the Hinode/Solar Optical Telescope-Spectro-Polarimeter (SOT-SP) pipeline. Our method extends our previous approach SynthIA with heliographic outputs as well as using an improved data set and inference method. SuperSynthIA provides a new tool for improved magnetic fields from full-disk SDO/HMI observations using information derived from the enhanced capabilities of Hinode/SOT-SP. Compared to our previous SynthIA, SuperSynthIA provides physics-ready vector magnetograms and mitigates unphysical angle preferences and banding artifacts in SynthIA. SuperSynthIA data are substantially more temporally consistent than those from the SDO/HMI pipeline, most notably seen in, e.g., evolving active regions. SuperSynthIA substantially reduces noise in low-signal areas, resulting in less center-to-limb bias outside of strong-signal areas. We show that outputs from SuperSynthIA track the SDO/HMI-recorded evolution of the magnetic field. We discuss the limitations of SuperSynthIA that the user must understand, and we demonstrate a broad set of evaluations to test SuperSynthIA and discuss remaining known artifacts. Our tests provide both methodology and evidence that SuperSynthIA outputs are ready for use by the community, and that learning-based approaches are suitable for physics-ready magnetograms.
Our method uses manipulation in video to learn to understand held-objects and hand-object contact. We train a system that takes a single RGB image and produces a pixel-embedding that can be used to ...answer grouping questions (do these two pixels go together) as well as hand-association questions (is this hand holding that pixel). Rather than painstakingly annotate segmentation masks, we observe people in realistic video data. We show that pairing epipolar geometry with modern optical flow produces simple and effective pseudo-labels for grouping. Given people segmentations, we can further associate pixels with hands to understand contact. Our system achieves competitive results on hand and hand-held object tasks.
This paper studies the economic impact of financial and uncertainty shocks using an estimated, nonlinear New Keynesian model with financial frictions. While uncertainty shocks have smaller impacts ...than most first-order shocks, increases in uncertainty surrounding labor supply, capital production and wealth lead to a sizable drop in consumption and investment. Financial frictions play an important role in amplifying the uncertainty shocks. As macroeconomic uncertainty increases, the performance of loans also becomes less certain. This causes financial conditions to tighten and the credit spread to increase, which results in a decline in investment. The filtered states of the model show that the uncertainty of labor supply, capital production and wealth shocks increased in the United States during the 2007–2009 financial crisis. Counterfactual results show that these increases in volatility and uncertainty, especially for the wealth shock, played key roles in causing the Great Recession, accounting for most of the decline in investment and the tightening of credit conditions.
•Estimates New Keynesian model with financial frictions and uncertainty shocks using U.S. data.•Uncertainty shocks negatively impact investment and consumption.•Wealth, labor supply and capital production are the most important uncertainty shocks.•Financial frictions play an important role in transmitting uncertainty shocks.•Wealth volatility was a major contributor to the Great Recession.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
"Trees were central to Henry David Thoreau's creativity as a writer, his work as a naturalist, his thought and his inner life. His portraits of them were so perfect, it was as if he could to see the ...sap flowing beneath their bark. When Thoreau wrote that the poet loves the pine tree as his own shadow in the air, he was speaking about himself. In short, he spoke their language. In this original book, Richard Higgins explores Thoreau's deep connections to trees: his keen perception of them, the joy they gave him, the poetry he saw in them, his philosophical view of them, and how they fed his soul. His lively essays show that trees were a thread connecting all parts of Thoreau's being...heart, mind and spirit. Included are one hundred excerpts from Thoreau's writing about trees, paired with sixty-eight of the author's photographs. Thoreau's words are as vivid now as they were in 1890, when an English naturalist wrote that he was unusually able to 'to preserve the flashing forest colors in unfading light.' Thoreau and the Language of Trees shows that Thoreau, with uncanny foresight, believed trees were essential to the preservation of the world"
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