Incorporating Uncertainties in Atomic Data Into the Analysis of Solar and Stellar Observations: A Case Study in Fe XIII

Information about the physical properties of astrophysical objects cannot be measured directly but is inferred by interpreting spectroscopic observations in the context of atomic physics calculations. Ratios of emission lines, for example, can be used to infer the electron density of the emitting plasma. Similarly, the relative intensities of emission lines formed over a wide range of temperatures yield information on the temperature structure. A critical component of this analysis is understanding how uncertainties in the underlying atomic physics propagates to the uncertainties in the inferred plasma parameters. At present, however, atomic physics databases do not include uncertainties on the atomic parameters and there is no established methodology for using them even if they did. In this paper we develop simple models for the uncertainties in the collision strengths and decay rates for Fe XIII and apply them to the interpretation of density sensitive lines observed with the EUV Imagining spectrometer (EIS) on Hinode. We incorporate these uncertainties in a Bayesian framework. We consider both a pragmatic Bayesian method where the atomic physics information is unaffected by the observed data, and a fully Bayesian method where the data can be used to probe the physics. The former generally increases the uncertainty in the inferred density by about a factor of 5 compared with models that incorporate only statistical uncertainties. The latter reduces the uncertainties on the inferred densities, but identifies areas of possible systematic problems with either the atomic physics or the observed intensities.Comment: in press at Ap

Subliminal priming and persuasion: Striking while the iron is hot.

Abstract Three studies demonstrated that subliminally priming a goal-relevant cognition (thirst in Studies 1 and 2; sadness in Study 3) influenced behavior (in Study 1) and enhanced the persuasiveness of an ad targeting the goal (in Studies 2 and 3) when people were motivated to pursue the goal (when they were thirsty in Studies 1 and 2; when they expected to interact with another person in Study 3). These results suggest that subliminal priming can be used to enhance persuasion, but only when certain conditions are met. Both the priming of goal-relevant cognitions and the motive to pursue the goal were necessary for ads targeting the goal to be more persuasive. The implications of these results for the role of functionality in subliminal priming and for the use and abuse of subliminal priming in persuasion are discussed

What happened to smokers' beliefs about light cigarettes when "light/mild" brand descriptors were banned in the UK? Findings from the International Tobacco Control (ITC) Four Country Survey

Aim: This paper reports findings of an evaluation that examined how beliefs of smokers in the United Kingdom (UK) were affected by the removal of light and mild brand descriptors which came into effect on September 30, 2003 for Member States of the European Union (EU). Participants: The data come from the first 4 waves (2002-2005) of the International Tobacco Control Policy Evaluation (ITC) 4 Country Survey, an annual cohort telephone survey of adult smokers in Canada, United States, United Kingdom, and Australia (15450 individual cases). Design: The UK ban on misleading descriptors occurred around the 2nd wave of data collection in the ITC survey, permitting us to compare beliefs about light cigarettes among adult smokers in the UK both before and after the ban, with beliefs in three other countries unaffected by the ban. Results: The findings reveal that high levels of misperceptions about light cigarettes existed among smokers in all four countries before and after the EU ban took effect. There was a substantial decline in reported beliefs about the benefits of Lights in the UK following the policy change and an associated public information campaign, but by 2006 (i.e., Wave 4), these beliefs rebounded slightly and the change in beliefs was no greater than in the United States, where there was no policy change. Conclusion: We cannot conclude that the policy which required removal of the misleading labels has been effective in changing beliefs about light cigarettes. What seems apparent is that efforts to correct decades of consumer misperceptions about light cigarettes will require more than simply removing brand descriptors

The First Flight of the Marshall Grazing Incidence X-ray Spectrometer (MaGIXS)

The Marshall Grazing Incidence X-ray Spectrometer (MaGIXS) sounding rocket experiment launched on July 30, 2021 from the White Sands Missile Range in New Mexico. MaGIXS is a unique solar observing telescope developed to capture X-ray spectral images, in the 6 - 24 Angstrom wavelength range, of coronal active regions. Its novel design takes advantage of recent technological advances related to fabricating and optimizing X-ray optical systems as well as breakthroughs in inversion methodologies necessary to create spectrally pure maps from overlapping spectral images. MaGIXS is the first instrument of its kind to provide spatially resolved soft X-ray spectra across a wide field of view. The plasma diagnostics available in this spectral regime make this instrument a powerful tool for probing solar coronal heating. This paper presents details from the first MaGIXS flight, the captured observations, the data processing and inversion techniques, and the first science results.Comment: 20 pages, 18 figure

Towards comprehensive observing and modeling systems for monitoring and predicting regional to coastal sea level

A major challenge for managing impacts and implementing effective mitigation measures and adaptation strategies for coastal zones affected by future sea level (SL) rise is our limited capacity to predict SL change at the coast on relevant spatial and temporal scales. Predicting coastal SL requires the ability to monitor and simulate a multitude of physical processes affecting SL, from local effects of wind waves and river runoff to remote influences of the large-scale ocean circulation on the coast. Here we assess our current understanding of the causes of coastal SL variability on monthly to multi-decadal timescales, including geodetic, oceanographic and atmospheric aspects of the problem, and review available observing systems informing on coastal SL. We also review the ability of existing models and data assimilation systems to estimate coastal SL variations and of atmosphere-ocean global coupled models and related regional downscaling efforts to project future SL changes. We discuss (1) observational gaps and uncertainties, and priorities for the development of an optimal and integrated coastal SL observing system, (2) strategies for advancing model capabilities in forecasting short-term processes and projecting long-term changes affecting coastal SL, and (3) possible future developments of sea level services enabling better connection of scientists and user communities and facilitating assessment and decision making for adaptation to future coastal SL change.RP was funded by NASA grant NNH16CT00C. CD was supported by the Australian Research Council (FT130101532 and DP 160103130), the Scientific Committee on Oceanic Research (SCOR) Working Group 148, funded by national SCOR committees and a grant to SCOR from the U.S. National Science Foundation (Grant OCE-1546580), and the Intergovernmental Oceanographic Commission of UNESCO/International Oceanographic Data and Information Exchange (IOC/IODE) IQuOD Steering Group. SJ was supported by the Natural Environmental Research Council under Grant Agreement No. NE/P01517/1 and by the EPSRC NEWTON Fund Sustainable Deltas Programme, Grant Number EP/R024537/1. RvdW received funding from NWO, Grant 866.13.001. WH was supported by NASA (NNX17AI63G and NNX17AH25G). CL was supported by NASA Grant NNH16CT01C. This work is a contribution to the PIRATE project funded by CNES (to TP). PT was supported by the NOAA Research Global Ocean Monitoring and Observing Program through its sponsorship of UHSLC (NA16NMF4320058). JS was supported by EU contract 730030 (call H2020-EO-2016, “CEASELESS”). JW was supported by EU Horizon 2020 Grant 633211, Atlantos

The First Flight of the Marshall Grazing Incidence X-Ray Spectrometer (MaGIXS)

The Marshall Grazing Incidence X-ray Spectrometer (MaGIXS) sounding rocket experiment launched on 2021 July 30 from the White Sands Missile Range in New Mexico. MaGIXS is a unique solar observing telescope developed to capture X-ray spectral images of coronal active regions in the 6–24 Å wavelength range. Its novel design takes advantage of recent technological advances related to fabricating and optimizing X-ray optical systems, as well as breakthroughs in inversion methodologies necessary to create spectrally pure maps from overlapping spectral images. MaGIXS is the first instrument of its kind to provide spatially resolved soft X-ray spectra across a wide field of view. The plasma diagnostics available in this spectral regime make this instrument a powerful tool for probing solar coronal heating. This paper presents details from the first MaGIXS flight, the captured observations, the data processing and inversion techniques, and the first science results

ClimSim: A large multi-scale dataset for hybrid physics-ML climate emulation

Modern climate projections lack adequate spatial and temporal resolution due to computational constraints. A consequence is inaccurate and imprecise predictions of critical processes such as storms. Hybrid methods that combine physics with machine learning (ML) have introduced a new generation of higher fidelity climate simulators that can sidestep Moore's Law by outsourcing compute-hungry, short, high-resolution simulations to ML emulators. However, this hybrid ML-physics simulation approach requires domain-specific treatment and has been inaccessible to ML experts because of lack of training data and relevant, easy-to-use workflows. We present ClimSim, the largest-ever dataset designed for hybrid ML-physics research. It comprises multi-scale climate simulations, developed by a consortium of climate scientists and ML researchers. It consists of 5.7 billion pairs of multivariate input and output vectors that isolate the influence of locally-nested, high-resolution, high-fidelity physics on a host climate simulator's macro-scale physical state.The dataset is global in coverage, spans multiple years at high sampling frequency, and is designed such that resulting emulators are compatible with downstream coupling into operational climate simulators. We implement a range of deterministic and stochastic regression baselines to highlight the ML challenges and their scoring. The data (https://huggingface.co/datasets/LEAP/ClimSim_high-res) and code (https://leap-stc.github.io/ClimSim) are released openly to support the development of hybrid ML-physics and high-fidelity climate simulations for the benefit of science and society