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Irina Kitiashvili

Publications and source records attributed to Irina Kitiashvili.

At least 55 records · Page 3
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Understanding Space Radiation in Earth Environment with Radiation Data Portal

The impact of radiation dramatically increases at high altitudes in the Earth’s atmosphere and in space. Therefore, monitoring and access to radiation environment measurements are critical for estimating the radiation exposure risks of aircraft and spacecraft crews and the impact of space weather disturbances on electronics. Addressing these needs requires reliable access to multisource radiation environment data and enhanced visualization and search capabilities. The Radiation Data Portal provides an interactive web-based application for convenient search and visualization of in-flight radiation measurements.

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Asteroseismic Inversions of Mixed Acoustic-Gravity Modes to Probe the Stellar Core Structure

The discovery of mixed acoustic-gravity modes of oscillations of moderate mass stars opens a unique opportunity to infer the structure of the inner energy-generating cores and thus test the stellar evolution theory. The mixed modes have properties of internal gravity waves (g-modes) in the convectively stable helium core and properties of acoustic modes outside the core. We select several sets of the oscillation mode frequencies in the mass range from about 1.3 to 1.6 solar masses from the Kepler Legacy database, and apply the optimally localized averaging inversion technique previously developed for low-degree helioseismology. The inversion technique takes into account the uncertainties in the determination of the mass and radius of the stars, as well as the surface effects. The methodology provides sensitivity kernels for various structure properties, including the sound speed, density, and Ledoux parameter of convective stability, and, thus, the direct relationship between the stellar properties and the deviation of observed frequencies from the reference models. The inversion results reveal significant deviations in the core structure from the reference models calculated using the MESA evolutionary code for the stellar parameters obtained by the asteroseismic model grid fitting. Our analysis shows that the best resolution of the inner helium core and surrounding shell is achieved in inversions for the Ledoux parameter.

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Using Long-Short Term Memory Models to Predict Solar Active Regions Emergence

We train Long Short-Term Memory (LSTM) models that predict the formation of active regions (ARs), the main source of eruptive solar activity. Using the Doppler shift velocity, the continuum intensity and the magnetic field full-disk maps from SDO/HMI we have created time-series datasets of acoustic power and magnetic flux which are used to train Long Short-Term Memory (LSTM) models on predicting decreases in continuum intensity 12 hours in advance. Testing of the models' performance was done on data from 5 ARs, unseen from the model during training. The model predicted the emergence of AR11726, AR13165 and AR13179, 10, 29 and 5 hours in advance, and variations of this model achieved average RMSE values of 0.11 for both active and quiet parts of the solar disc, showing the ability of the model to capture acoustic power anomalies and predict continuum intensity variations. This work sets the foundations for the very first ML-aided prediction of solar ARs.

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Development of Solar Flare and Energetic Particle Prediction Portal (SEP 3 )

Solar activity is a primary factor determining the state of the Earth’s space environment, geomagnetic and ionospheric disturbances, and radiation hazards. In the current state of knowledge, machine learning (ML) methods provide essential tools for processing data, investigating relationships among various physical properties and characteristics, uncovering hidden connections, and predicting hazardous solar events. The primary difficulty in developing and applying modern machine-learning tools in heliophysics is that the essential data are scattered among over a hundred data repositories developed by instrument teams of space missions and ground-based observatories. In addition, statistical and ML methods require long time series of homogeneous measurements. To facilitate ML-ready data preparation and access, we have developed an interactive database of solar flares integrating the most essential datasets (https://solarflare.njit.edu/). The database performs an initial data processing and is automatically updated. In addition, we are developing the Solar Energetic Particle Prediction Portal (SEP3, https://sun.njit.edu/SEP3), which hosts web applications that allow users to retrieve the database records. The Portal has a search page for browsing the events from the most widely used catalogs and a dedicated space to share the most recent achievements of the team. The interactive widget can display soft X-ray and proton flux time series from GOES satellites and the flare records. The data portal has been used to evaluate the forecasts of solar proton events and investigate machine-learning approaches to SEP prediction.

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