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"Wang, Jason"
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Operational prediction of solar flares using a transformer-based framework
Solar flares are explosions on the Sun. They happen when energy stored in magnetic fields around solar active regions (ARs) is suddenly released. Solar flares and accompanied coronal mass ejections are sources of space weather, which negatively affects a variety of technologies at or near Earth, ranging from blocking high-frequency radio waves used for radio communication to degrading power grid operations. Monitoring and providing early and accurate prediction of solar flares is therefore crucial for preparedness and disaster risk management. In this article, we present a transformer-based framework, named SolarFlareNet, for predicting whether an AR would produce a
γ
-class flare within the next 24 to 72 h. We consider three
γ
classes, namely the
≥
M5.0 class, the
≥
M class and the
≥
C class, and build three transformers separately, each corresponding to a
γ
class. Each transformer is used to make predictions of its corresponding
γ
-class flares. The crux of our approach is to model data samples in an AR as time series and to use transformers to capture the temporal dynamics of the data samples. Each data sample consists of magnetic parameters taken from Space-weather HMI Active Region Patches (SHARP) and related data products. We survey flare events that occurred from May 2010 to December 2022 using the Geostationary Operational Environmental Satellite X-ray flare catalogs provided by the National Centers for Environmental Information (NCEI), and build a database of flares with identified ARs in the NCEI flare catalogs. This flare database is used to construct labels of the data samples suitable for machine learning. We further extend the deterministic approach to a calibration-based probabilistic forecasting method. The SolarFlareNet system is fully operational and is capable of making near real-time predictions of solar flares on the Web.
Journal Article
Ethics and governance for digital disease surveillance
The question is not whether to use new data sources but how Digital epidemiology—the use of data generated outside the public health system for disease surveillance—has been in use for more than a quarter century [see supplementary materials (SM)]. But several countries have taken digital epidemiology to the next level in responding to COVID-19. Focusing on core public health functions of case detection, contact tracing, and isolation and quarantine, we explore ethical concerns raised by digital technologies and new data sources in public health surveillance during epidemics. For example, some have voiced concern that trust and participation in such approaches may be unevenly distributed across society; others have raised privacy concerns. Yet counterbalancing such concerns is the argument that “sometimes it is unethical not to use available data” ( 1 ); some trade-offs may be not only ethically justifiable but ethically obligatory. The question is not whether to use new data sources—such as cellphones, wearables, video surveillance, social media, internet searches and news, and crowd-sourced symptom self-reports—but how.
Journal Article
Predicting Solar Energetic Particles Using SDO/HMI Vector Magnetic Data Products and a Bidirectional LSTM Network
Solar energetic particles (SEPs) are an essential source of space radiation, and are hazardous for humans in space, spacecraft, and technology in general. In this paper, we propose a deep-learning method, specifically a bidirectional long short-term memory (biLSTM) network, to predict if an active region (AR) would produce an SEP event given that (i) the AR will produce an M- or X-class flare and a coronal mass ejection (CME) associated with the flare, or (ii) the AR will produce an M- or X-class flare regardless of whether or not the flare is associated with a CME. The data samples used in this study are collected from the Geostationary Operational Environmental Satellite's X-ray flare catalogs provided by the National Centers for Environmental Information. We select M- and X-class flares with identified ARs in the catalogs for the period between 2010 and 2021, and find the associations of flares, CMEs, and SEPs in the Space Weather Database of Notifications, Knowledge, Information during the same period. Each data sample contains physical parameters collected from the Helioseismic and Magnetic Imager on board the Solar Dynamics Observatory. Experimental results based on different performance metrics demonstrate that the proposed biLSTM network is better than related machine-learning algorithms for the two SEP prediction tasks studied here. We also discuss extensions of our approach for probabilistic forecasting and calibration with empirical evaluation.
Journal Article
Atmospheric Metallicity and C/O of HD 189733 b from High-resolution Spectroscopy
We present high-resolution K-band emission spectra of the quintessential hot Jupiter HD 189733 b from the Keck Planet Imager and Characterizer. Using a Bayesian retrieval framework, we fit the dayside pressure–temperature profile, orbital kinematics, mass-mixing ratios of H2O, CO, CH4, NH3, HCN, and H2S, and the 13CO/12CO ratio. We measure mass fractions of logH2O=−2.0−0.4+0.4 and logCO=−2.2−0.5+0.5 , and place upper limits on the remaining species. Notably, we find logCH4 < −4.5 at 99% confidence, despite its anticipated presence at the equilibrium temperature of HD 189733 b assuming local thermal equilibrium. We make a tentative (∼3σ) detection of 13CO, and the retrieved posteriors suggest a 12C/13C ratio similar to or substantially less than the local interstellar value. The possible 13C enrichment would be consistent with accretion of fractionated material in ices or in the protoplanetary disk midplane. The retrieved abundances correspond to a substantially substellar atmospheric C/O = 0.3 ± 0.1, while the carbon and oxygen abundances are stellar to slightly superstellar, consistent with core-accretion models which predict an inverse correlation between C/O and metallicity. The specific combination of low C/O and high metallicity suggests significant accretion of solid material may have occurred late in the formation process of HD 189733 b.
Journal Article
Characterizing electronic health record usage patterns of inpatient medicine residents using event log data
Amid growing rates of burnout, physicians report increasing electronic health record (EHR) usage alongside decreasing clinical facetime with patients. There exists a pressing need to improve physician-computer-patient interactions by streamlining EHR workflow. To identify interventions to improve EHR design and usage, we systematically characterize EHR activity among internal medicine residents at a tertiary academic hospital across various inpatient rotations and roles from June 2013 to November 2016. Logged EHR timestamps were extracted from Stanford Hospital's EHR system (Epic) and cross-referenced against resident rotation schedules. We tracked the quantity of EHR logs across 24-hour cycles to reveal daily usage patterns. In addition, we decomposed daily EHR time into time spent on specific EHR actions (e.g. chart review, note entry and review, results review).In examining 24-hour usage cycles from general medicine day and night team rotations, we identified a prominent trend in which night team activity promptly ceased at the shift's end, while day team activity tended to linger post-shift. Across all rotations and roles, residents spent on average 5.38 hours (standard deviation = 2.07) using the EHR. PGY1 (post-graduate year one) interns and PGY2+ residents spent on average 2.4 and 4.1 times the number of EHR hours on information review (chart, note, and results review) as information entry (note and order entry).Analysis of EHR event log data can enable medical educators and programs to develop more targeted interventions to improve physician-computer-patient interactions, centered on specific EHR actions.
Journal Article
Reconstruction of Solar Extreme-ultraviolet Irradiance Using Ca II K Images and SOHO/SEM Data with Bayesian Deep Learning and Uncertainty Quantification
Solar extreme-ultraviolet (EUV) irradiance plays a crucial role in heating the Earth’s ionosphere, thermosphere, and mesosphere, affecting atmospheric dynamics over varying time scales. Although significant effort has been spent studying short-term EUV variations from solar transient events, there is little work to explore the long-term evolution of the EUV flux over multiple solar cycles. Continuous EUV flux measurements have only been available since 1995, leaving significant gaps in earlier data. In this study, we propose a Bayesian deep learning model, named SEMNet, to fill the gaps. We validate our approach by applying SEMNet to construct Solar and Heliospheric Observatory/Solar EUV Monitor EUV flux measurements in the period between 1998 and 2014 using Ca II K images from the Precision Solar Photometric Telescope. We then extend SEMNet through transfer learning to reconstruct solar EUV irradiance in the period between 1950 and 1960 using Ca II K images from the Kodaikanal Solar Observatory. Experimental results show that SEMNet provides reliable predictions along with uncertainty bounds, demonstrating the feasibility of Ca II K images as a robust proxy for long-term EUV fluxes. These findings contribute to a better understanding of solar influences on Earth’s climate over extended periods.
Journal Article
Retrieving C and O Abundance of HR 8799 c by Combining High- and Low-resolution Data
The formation and evolution pathway for the directly imaged multiplanetary system HR 8799 remains mysterious. Accurate constraints on the chemical composition of the planetary atmosphere(s) are key to solving the mystery. We perform a detailed atmospheric retrieval on HR 8799 c to infer the chemical abundances and abundance ratios using a combination of photometric data along with low- and high-resolution spectroscopic data (R ∼ 20–35,000). We specifically retrieve [C/H], [O/H], and C/O and find them to be 0.55−0.39+0.36 , 0.47−0.32+0.31 , and 0.67−0.15+0.12 at 68% confidence. The superstellar C and O abundances, yet a stellar C/O ratio, reveal a potential formation pathway for HR 8799 c. Planet c, and likely the other gas giant planets in the system, formed early on (likely within ∼1 Myr), followed by further atmospheric enrichment in C and O through the accretion of solids beyond the CO ice line. The enrichment either preceded or took place during the early phase of the inward migration to the current planet locations.
Journal Article
Solar Flare Index Prediction Using SDO/HMI Vector Magnetic Data Products with Statistical and Machine-learning Methods
Solar flares, especially the M- and X-class flares, are often associated with coronal mass ejections. They are the most important sources of space weather effects, which can severely impact the near-Earth environment. Thus it is essential to forecast flares (especially the M- and X-class ones) to mitigate their destructive and hazardous consequences. Here, we introduce several statistical and machine-learning approaches to the prediction of an active region’s (AR) flare index (FI) that quantifies the flare productivity of an AR by taking into account the number of different class flares within a certain time interval. Specifically, our sample includes 563 ARs that appeared on the solar disk from 2010 May to 2017 December. The 25 magnetic parameters, provided by the Space-weather HMI Active Region Patches (SHARP) from the Helioseismic and Magnetic Imager on board the Solar Dynamics Observatory, characterize coronal magnetic energy stored in ARs by proxy and are used as the predictors. We investigate the relationship between these SHARP parameters and the FI of ARs with a machine-learning algorithm (spline regression) and the resampling method (Synthetic Minority Oversampling Technique for Regression with Gaussian Noise). Based on the established relationship, we are able to predict the value of FIs for a given AR within the next 1 day period. Compared with other four popular machine-learning algorithms, our methods improve the accuracy of FI prediction, especially for a large FI. In addition, we sort the importance of SHARP parameters by the Borda count method calculated from the ranks that are rendered by nine different machine-learning methods.
Journal Article
VLTI/GRAVITY Observations of AF Lep b: Preference for Circular Orbits, Cloudy Atmospheres, and a Moderately Enhanced Metallicity
Direct imaging observations are biased toward wide-separation, massive companions that have degenerate formation histories. Although the majority of exoplanets are expected to form via core accretion, most directly imaged exoplanets have not been convincingly demonstrated to follow this formation pathway. We obtained new interferometric observations of the directly imaged giant planet AF Lep b with the VLTI/GRAVITY instrument. We present three epochs of ∼50 μas relative astrometry and the K-band spectrum of the planet for the first time at a resolution of R = 500. Using only these measurements, spanning less than 2 months, and the Hipparcos-Gaia Catalogue of Accelerations, we are able to significantly constrain the planet’s orbit; this bodes well for interferometric observations of planets discovered by Gaia DR4. Including all available measurements of the planet, we infer an effectively circular orbit (e < 0.02, 0.07, and 0.13 at 1σ, 2σ, and 3σ, respectively) in spin–orbit alignment with the host and measure a dynamical mass of M p = 3.75M Jup ± 0.5M Jup. Models of the spectrum of the planet show that it is metal-rich ([M/H] = 0.75 ± 0.25), with a C/O abundance encompassing the solar value. This ensemble of results shows that the planet is consistent with core accretion formation.
Journal Article