REU – 2021 Student Presentations

Student Name in bold

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01. Solar cycle effects on Polar Mesospheric Clouds

Allison Fagerson, Aimee Merkel, Dr. Gary Thomas and Mark E. Hervig

An investigation into the long-term variability of Polar Mesospheric Clouds(PMC’s) with the 11-year solar cycle. PMCs are used as tracers in the mesosphere due to being sensitive to local environment changes like temperature and water vapor. Methods include looking at satellite observations in comparison to model simulations through NCAR’s Whole Atmosphere Community Climate Model(WACCM). SBUV satellite observations have provided the longest record of PMCs from space which is used to study long term variability. WACCM models are used to study how PMC’s respond to changes in water vapor and temperature in a controlled environment. During solar maximum, lyman alpha is an effective destroyer of water vapor in the mesosphere so PMC occurrence is expected to be subdued. During solar minimum when lyman alpha is lowest, PMC occurrence should be heightened but the last solar cycle satellite observations indicated different results. Satellite observations indicate that the dampening of PMCs could be linked to a missing solar cycle with water vapor.
To further look into the missing solar cycle signal, this study is investigating the 11 year patterns in PMCs using the WACCM model. Satellite observations agree with the model’s reproduction of PMCs observing water vapor and temperature until around 2002 where they differ in PMC development. This variability from the model compared to satellite data can be indicating a change in the atmospheric response to solar minimum. This investigation looks to explain this variability in PMCs with the solar cycle and the mesospheric trends.

Slides  |  Poster

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02. Atmospheric Scale-Height and Density via Solar Occultations Measured by SORCE

Claudia Luna, Joshua Peter Elliott, Ed Thiemann

Solar Occultations have been used to obtain the atmospheric scale-heights and densities of species in the Earth’s thermosphere for almost 50 years by measuring the change in solar irradiance after it has passed through the Earth’s atmosphere. However, measurements from the lower and middle thermosphere are few. By using data collected from the SOlar Stellar Irradiance Comparison Experiment (SOLSTICE) instrument, which flew aboard the Solar Radiation and Climate Experiment (SORCE) satellite, we are able to fill in some of the information gaps at these higher altitudes.
The observations used were collected during the months of June and July of 2004, at latitudes ranging from 20° to 33° and altitudes ranging from ~100-200 km. These measurements are inherently constrained to dawn and dusk local times. Because molecular oxygen (O2) is the only major species absorber of the lyman-alpha line, which is seen at 121.6nm, we are able to use the irradiance values taken at wavelengths in the range 121.55-121.65nm, to obtain the scale-height and density of O2 in the lower and middle thermosphere.

Slides  |  Poster

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03. An Alternative Approach to Measuring Solar Velocities on the ​​Solar Dynamics Observatory (SDO) using the Helioseismic and Magnetic Imager (HMI)

Brittany Cavin and Valentin Martinez Pillet 

Aboard the Solar Dynamics Observatory (SDO), the Helioseismic and Magnetic Imager (HMI) measures the velocity of the Sun’s magnetic fields. However, there is a calibration issue causing an artificial 24h periodicity. The SDO spacecraft’s velocity is being combined with the solar velocities. The spacecraft’s velocity is often much greater than the solar velocities and causes inaccurate data. I compared the current HMI Method results (explained in Couvidat et al., 2016) with an alternative method, a Gaussian Fit Method, that could bypass the calibration error and avoid the leak of spacecraft velocity on the solar data. By plotting the Sun’s spectra at six different wavelengths, I could use a Gaussian fit to calculate the distribution at each pixel. After noticing trends in the two methods, I can claim the Gaussian Method works better for a majority of the Sun’s surface. However, it tends to be sensitive at identifying the wavelengths at Sunspot locations, varying slightly from the original HMI data. I believe the best method would be a combination of the two methods, with the Gaussian Fit being the leading method for the quiet Sun’s surface and the HMI method for sunspot locations. This would give the most accurate results for solar velocities by limiting the need for calibration.

Slides  |  Poster

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04. AFT: Predicting Active Region Evolution Using Far-Side Helioseismology

Alyssa Russell, Lisa Upton, Shea A Hess Webber, Ruizhu Chen

Active regions, areas on the sun with strong magnetic flux, are good indicators of where harmful space weather may originate. In order to better prepare for these phenomena, it is important to be able to predict how flux on the Sun evolves over time. This project aims to improve current models of flux evolution by incorporating new far-side helioseismic data. The Helioseismic and Magnetic Imager (HMI) instrument on the Solar Dynamics Observatory creates full-surface magnetograms and dopplergrams. The Advective Flux Transport Model (AFT), a surface flux transport model, assimilates measurements of near-side magnetic field observations from the HMI magnetograms, and simulates the evolution of active regions on the far-side of the Sun. However, AFT is unable to include new active regions that emerge on the far-side. Helioseismology uses dopplergrams to measure the variations in solar wave propagations and create near and far-side maps of the flows on the Sun, which can then be used to detect locations of far-side active regions. Machine learning is then used to turn this information into maps of the far-side active region flux. Using morphological masking techniques on the helioseismic-derived magnetic flux maps, we identified the location and flux of the far-side active regions and tracked their evolution over time. We then incorporated these far-side data into AFT, creating more accurate maps of the flux on the far side of the Sun. Finally, we compared these improved AFT maps with the original AFT simulations in order to determine the impact on the simulations. Through these comparisons, we demonstrate a proof of concept of using helioseismology-derived far-side flux maps to improve simulations of magnetic field evolution over the entire Sun.

Slides  |  Poster

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05. The impact of solar wind structures on Earth’s magnetosphere

Patricio Ramos and Lauren Blum

Solar wind is an important factor inside the Earth-Sun system. But, when this solar wind is the result of an inter-planetary coronal mass ejection, also known as an ICME, it disturbs the Earth’s magnetic field and, if it is powerful enough, the solar wind’s anti-parallel currents result in magnetic reconnection, and thus, a geo-magnetic storm. Recently, spacecraft observations showed that discrete structures, small spikes in the magnetic field, often accompany a coronal mass ejection that causes a geo-magnetic storm. Our objective is to observe these solar wind caused, geomagnetic events; look for discrete structures in the solar wind; and analyze their impact on the magnetosphere by measuring EM wave activity. To identify these solar wind events, we are first analyzing spacecraft data outside the magnetosphere. Here we observe the magnetic field of the solar wind. It is often the case that an ICME is reflected in the magnetic instruments with a specific signature. Then, we look at the recorded data here at Earth using the SYMH index, as well as the data recorded inside the magnetosphere using spacecraft such as RBSP A & B, MMS 1 & 2, and THEMIS A, D & E.
We can recognize these structures by their characteristic pattern or “signature” where these discrete structures are followed by a big “dip” in the magnetic field. The main challenge with these solar wind events is that there are not many clear-cut patterns. Sometimes, we can observe signature changes in the SYMH index, as well as discrete structures, but there are no subsequent geomagnetic storms.

Slides  |  Poster

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06. Mars Express Pickup Ion Observations as an Interplanetary Magnetic Field Proxy

Matthew W. Davis, Yaxue M. Dong and Robin P. Ramstad

The Martian atmosphere is susceptible to impinging solar wind and interplanetary magnetic field (IMF) as it lacks a global intrinsic magnetic field to shield it. This solar wind transfers energy to particles in the upper atmosphere allowing them to become ionized and eventually escaping to space through various processes. The Mars Atmosphere and Volatile Evolution Mission (MAVEN) has been studying these processes since its orbital insertion in 2014. These particles are also detected by ESA’s Mars Express (MEX) spacecraft using the Ion Mass Analyzer (IMA) instrument. Among the heavy ion species that are detected are O+, O2+, and CO2+. From these ions being detected along with MAVEN magnetometer (MAG) data, we have developed a proxy that derived upstream electric field directions and compared them to the ion flux directions. From this proxy we can determine the electric field directions and the corresponding IMF which will help with the study of long-term variation of plume and tail escape rates. The proxy has been tested on several pickup ion events spanning several months within two years and shows good consistence (~10°) between the heavy pickup ion flux and the upstream electric field directions in the MSO-yz plane. These results can then be utilized to further study and understand the orientation of the Martian induced magnetosphere for the decade that MEX has been collecting data before MAVEN arrived.

Slides  |  Poster

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07. Atmospheric Drag on Low Earth Orbit Satellite Swarms

Nicholas Moraga, Greg Lucas, Eric Sutton, Tzu-Wei Fang

Solar weather and CMEs have drastic impacts on the Earth’s atmosphere, specifically the thermosphere. The change in conditions alters the orbit of satellites and CubeSat swarms in LEO, which causes us to lose track of them. During extreme events, such as the 2003 geomagnetic storm, the thermosphere balloons out and increases in density. This creates problems for satellites in Low Earth Orbit, which experience a much larger drag force during such events. We know this changes their orbits, because of the satellites we temporarily lost track of in 2003. We can accurately model the changes in orbit that occur, based on data collected from the 2003 storm. With the data, we simulated CubeSat swarms passing through the thermosphere and tracked their new orbit, and quantified the change in trajectory. Based on the geometry of the satellites, we created a force model to find out the drag they would experience given a set of atmospheric conditions. We then input this set of data into the model for tracking orbits, and saw the changes step by step as the satellite traveled through the simulated conditions of the geomagnetic storm. Often, the increased density and energized particles cause the satellites to slow down, which causes problems for maintaining a stable orbit and trajectory. However, when we understand how the satellites change trajectory, we can find their new orbit without ever losing track of them. From understanding their change of course, we can warn other satellites of potential collisions, and make sure no other adverse events take place in LEO.

Slides  |  Poster

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08. Explainable AI for Solar Flare Prediction

Cody Feldhaus and Wendy Carande

Solar flares have the potential to disrupt Earth’s technology infrastructure and negatively impact the health and safety of space travelers. Consequently, predicting these events is of great interest to the scientific community and a benefit to society as a whole. The complexity of solar flare data combined with the fact that the physical processes behind solar flare occurrence are still not currently well understood suggests that the flare prediction problem is a good candidate for the application of machine learning techniques. To that end, machine learning algorithms have already been shown to be effective in forecasting solar flares, but it is often unclear why a particular model has made a given prediction. Not only does this lack of transparency make it difficult to better understand what causes solar flares, but it also poses potential challenges for having our predictions trusted. These enigmatic “black box” models raise similar concerns in other machine learning applications across many different fields, fueling an industry-wide desire to have improved models whose predictions are less mysterious. Enhancing our models with Explainable Artificial Intelligence (XAI), an emerging paradigm in machine learning grounded in the principles of accountability, responsibility, and transparency, has the potential to remove the shroud of uncertainty around our results, deepening our understanding of flares in general and strengthening the reliability of our predictions. Using Local Interpretable Model-Agnostic Explanations (LIME), a Python library for explaining machine learning models, we investigated the integration of XAI with flare prediction models and explored the potential benefits of a transparent model with explanations for its decisions that are easily understood. LIME’s highly interpretable diagrams show which features contributed positively or negatively to a particular prediction and quantify the relative importance of different features in the decision, allowing for transparency for experts and non-experts alike.

Poster

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09. Data Augmentation of Magnetograms for Solar Flare Prediction using Generative Adversarial Networks

Allison Liu and Wendy Carande

Currently, it is a national priority in the United States to forecast space weather events like solar flares because of their impacts to life on Earth. High energy bursts of radiation originating from solar flares have the potential to disrupt critical infrastructure systems, including the power grid and GPS and radio communications. Because machines offer insights into patterns present in data that are undetectable by the human eye, it is desirable to use machine learning to aid in the prediction of solar flares. However, the magnetogram data used for solar flare forecasting—taken by the Solar and Heliospheric Observatory/Michelson Doppler Interferometer (SOHO/MDI) and the NASA Solar Dynamic Observatory/Helioseismic and Magnetic Imager (SDO/HMI) instruments—are incompatible due to differences in the cadence, resolution, and size of the data. Many studies focus on data from a single instrument, which disregards decades worth of potential training data that is necessary to understand solar cycles. In this work, we used Generative Adversarial Networks (GANs) to upconvert the historic SOHO/MDI dataset to SDO/HMI quality to create a standardized training data set. We implemented a standard pix2pix GAN for image translation and compared it with modern cycleGAN and CUT GAN techniques. The resulting combined, higher-quality data set will be used to improve the predictive power of current solar flare forecasting models.

Slides  |  Poster

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10. Statistical investigation of the erosion and refilling of the plasmasphere – machine learning based approach

James Lende, Xiangning Chu, Jacob Bortnik, Richard Denton, Xin Cao, Donglai Ma

The density and composition of Earth’s Plasmasphere strongly influences wave growth and propagation, as well as energetic particle scattering. This affects Earth-to-space communication and killer electron behavior. The plasmaspheric dynamics are both time-dependent and history-dependent. Previous empirical plasma density models are generally based on statistical averages and are limited in their capability to make accurate predictions of the dynamic state of Earth’s plasmasphere. With recent advances in machine learning techniques, we are able to more accurately quantify complex global processes and nonlinear responses to driving conditions, especially during geomagnetic storms. This project presents a three-dimensional dynamic electron density model based on an artificial neural network. This model uses a feedforward neural network which was generated using electron densities from the satellite missions of CRRES, ISEE, IMAGE, and POLAR. The three-dimensional electron density model takes spacecraft location as well as time series of solar wind and geomagnetic indices (flow speed, SYM-H, AL, and AE) obtained from NASA’s OMNI database as inputs. The model can predict out-of-sample data with a correlation coefficient of 0.92, meaning over 90% of the variations are captured. The three-dimensional model was applied to a number of magnetic storms, and it successfully reconstructed the expected plasmaspheric dynamics. We carried out a statistical analysis of the plasmaspheric erosion rates and refilling processes during these geomagnetic storms using the ML-based reconstruction, which show interesting and inspiring results. The statistical results are consistent with previous studies, while it has some interesting differences in both long term and short term responses. It shows how machine learning techniques might be used to help understand the physics and insight discovery, as well as advance the state-of-the-art space weather prediction.

Slides  |  Poster

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11. Modeling the Variability of the Sun’s Total Solar Irradiance through Supervised Machine Learning Techniques

Ashley Lieber, Laura Sandoval and Josh Elliott

With the Sun being Earth’s main source of energy and heat, it is critical that we understand how and why it varies. The Sun’s irradiance changes over its 11-year solar cycle driven by variabilities in the magnetic field. The primary objective of this project was to create a machine learning regression model that predicts the total solar irradiance (TSI) and to analyze the contributions of solar features in driving change of the Sun’s TSI. This improves upon the previous model by incorporating machine learning techniques on a wider array of data.
This model uses data of solar features visible in intensitygrams and line-of-sight magnetograms from the Helioseismic and Magnetic Imager (HMI) instrument aboard the Solar Dynamics Observatory (SDO). This data includes HARP data which gives information on active regions (AR) on the Sun’s surface and SHARP data which provides space weather parameters. The TSI values were measured by Total Irradiance Monitors (TIM) on two separate satellites. In order to make the data compatible between the two satellites, we utilized TSI composite which minimizes the systemic differences between the datasets and adjusts the data accordingly without introducing more error.
Initially, we utilized the machine learning technique of multiple linear regression to have the model predict an irradiance value for a given day based on given solar features. To ascertain the sunspot area, we performed image processing on the intensitygrams from the SDO HMI. Additionally, seventeen space weather parameters from the SHARP data were added to the model. Due to the number of features, a correlation matrix was created to eliminate features that were highly correlated and determine how features correlated with TSI. Preliminary results show that training the model on these features yields a root mean squared error of 0.052 showing that the model is performing as expected in this early stage. A residual plot was also created to showcase the relation between the actual TSI values and the predicted TSI values.

Slides  |  Poster

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12. Classification of Solar Wind Discontinuities Using Supervised Machine Learning

Amanda Joy Camarata, Andres Munoz-Jaramillo and Anna Jungbluth

The solar wind is a constant ejection of charged particles from the Sun’s corona that permeates the entire solar system. Because these particles can interfere with our assets in space, including communications and GPS, we want to better understand how the solar wind develops from the Sun to the Earth. Solar magnetic discontinuities describe the boundaries between magnetic flux tubes. Discontinuities can occur at many different time scales, but in contrast to large-scale events such as Coronal Mass Ejections, many discontinuities occur orders of magnitude more frequently and on smaller time scales. As a result, detecting these small-scale magnetic discontinuities is challenging. Traditional ways of identifying discontinuities include hand-labeling by experts or applying heuristic approaches that are computationally expensive and result in human bias. Our work shows that supervised machine learning can be used to create labels identifying discontinuities for predetermined time intervals efficiently and accurately. In this work, we leverage data from the WIND spacecraft, which has been taking in situ solar wind measurements since 2005. We use one day’s worth of expert hand-labeled data to train the neural network to identify whether a magnetic discontinuity exists in a given time interval. We offset the limited availability of labeled training data by employing novel techniques of supervised learning, such as contrastive learning and hard example mining. These methods improve the accuracy of our model and enable the accurate identification of magnetic discontinuities.

Slides  |  Poster

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13. Cataloging Magnetic Field Structures within the Solar Wind

Mavis Stone, Andres Munoz-Jaramillo and Anna Jungbluth

The solar wind is a constant stream of plasma structured by the solar magnetic field that is radially ejected from our Sun to the boundaries of our solar system. Organizations such as NASA and ESA have gathered nearly half a century of data on solar wind, but much of it has yet to be analyzed for improved understanding on solar wind evolution. So far, heliophysicists have primarily focused on understanding specific structures such as interplanetary coronal mass ejections and large-scale discontinuities; however, there exist many statistically significant structures that have yet to be discovered. In this work, we create a new, unsupervised framework designed to catalog both known and unknown structures using magnetic field time series data from the three-year-old Parker Solar Probe. We combine iSAX indexing and HDB Scan clustering to identify, retrieve, and cluster similar magnetic field structures, a challenge that would otherwise be impossible. More specifically, we perform preliminary clustering on similar solar wind structures with 0.005% of the operations traditional clustering would require. Our method can be used on other time series data including, but not limited to: plasma velocity, density, and electron composition, all of which can offer further insight into space weather and its impact on Earth. Additionally, the great size, detail, and level of organization of our catalog can expedite efforts to learn more about the origins and evolution of the solar wind. Beyond the scope of our work, this easily reproducible framework can be applied to other fields of research aiming to analyze large amounts of time series data.

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14. Increasing the Accessibility of Solar Science through Interactive Demonstration

Benjamin Miller, Tishanna Ben, John Williams, Claire Raftery

Spectropolarimetry is one of the primary scientific techniques used by the NSF’s Daniel K. Inouye Solar Telescope to measure the coronal magnetic field of the Sun. It is vital to our future understanding of solar and space weather phenomena. However, despite spectropolarimetry’s importance in the astronomical community, the complexity of the technique makes it almost completely inaccessible to the general public.
By increasing public awareness of the impact that the Inouye Telescope has on astronomy research we can help to foster trust in our communities. Therefore, we have designed a fun demonstration that translates the topic of spectropolarimetry for a general audience. The demonstration engages participants in a kinesthetic, interactive activity analogous to processes that spectropolarimetry is based upon. By taking part in this activity and engaging physically with the science, participants will be more engaged and achieve a longer lasting understanding of spectropolarimetry and its role in the Inouye Solar Telescope. In doing so, we will show that incorporating interactive elements into scientific demonstrations increases understanding of complex topics and can be vital to encouraging engagement with science from the general public.

Slides  |  Poster

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15. Exploration of the physical and spectroscopic characteristics of solar coronal rain

Kalista Tyson, Daniela Lacatus, Alin Paraschiv, Paul Bryans

The correlation between the phenomena known as coronal rain and the unknown origin of the Sun’s heating and cooling mechanisms has become a popular area for exploration in recent years. New capabilities have enabled us to model and observe the solar corona giving us the ability to follow the evolution of solar structures in more detail. The implications of understanding how plasma flows are triggered and how they move along closed magnetic field entities will be explored. Using spectroscopic data obtained from Interface Region Imaging Spectrograph (IRIS), we are able to analyze the Mg II, Si IV and C II emission lines to further interpret different magnetic structures which allows us to also study plasma in different regimes. We have extracted spectroscopic characteristics of the emission lines for an off-limb prolonged coronal rain event taking place above a quiescent active region system. Additionally, we augmented our dataset with SDO/AIA coronal observations, highlighting corresponding higher temperature structures. We report a case where recurring coronal rain episodes occur over timescales of several hours. The line width maps showed what appeared to be a standard loop structure. The intensity maps and doppler shift maps actually revealed a multitude of differently oriented, and sometimes turbulent structures. This aspect of this research is something that we aim to continue exploring in the future.

Slides  |  Poster

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16. Analyzing the Structure of Coronal Loops in MURaM Radiation MHD Simulations

Mia David, M. Rempel, and A. Malanushenko

Coronal loops are emission features that trace out parts of the solar magnetic field in the corona, and as such they provide important information about the magnetic and thermal structures of the solar corona. However, observations of these features are limited by the resolution of the instruments used to study them. Observations with higher resolution tend to find finer strands (for example, HiC found finer strands than AIA), which raises the question whether we resolve the finest strands with the currently available highest resolution instruments (such as HiC). In this project, we address this from a modeling perspective and look to answer the following questions. Does the number of strands identified in synthetic AIA and HiC observations depend on the resolution of the numerical simulation? How many strands remain hidden in current observations that may otherwise be evident in future higher resolution observations? We look at MURaM simulations of a simple bipolar active region that are available at three different numerical resolutions. We emulate observables at various resolutions, including one which exceeds that of current instruments. We compare the results with real data by studying an active region observed by SDO/AIA. We find that the number of strands found in synthetic AIA does not depend on the resolution of the simulation, and that it is a small fraction of the strands found in the native resolution of the simulation. The number of strands seen in synthetic HiC is a factor of 2-4 higher than that in synthetic AIA and increases moderately with the resolution of the simulation. We find that the number of strands found in both the synthetic and real data depends on the viewing angle. We also report statistical properties of these strands.

Poster

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17. Spectral Runway: An Analysis of Solar Balmer Lines Through Observations and Models

Emilie F. Burnham, Serena Criscuoli, Adam Kowalski, Jerry Harder and Randy Meisner

Spectral analysis provides a glimpse into the physical properties of stellar atmospheres, which includes temperature, density, magnetic signatures, and so on. Balmer lines specifically are used as proxies for atmospheric activity, as they have been used to determine stellar effective temperatures, and used to constrain stellar atmospheric models. Here, we are interested in the variations of balmer lines induced by stellar surface magnetism, which is known to affect the atmospheres of orbiting planets and is a factor in determining their habitability. As direct solar measurements and spatially resolved stellar spectra are not always available, models are vital to the understanding of the magnetic contribution to stellar spectral variability. In this context, the Sun offers a unique opportunity for direct observations of the effects of magnetic features on spectral irradiance and further validation with state-of-the-art models. In this study we utilize high spatial resolution spectroscopic observations obtained at the Dunn Solar Telescope to investigate how surface magnetism affects the shape of Balmer line profiles, specifically H-alpha and H-gamma. Observational results are then compared with theoretical spectra obtained with the Rybiki and Hummer synthesis code using two sets of one-dimensional solar atmospheric models (each describing different types of quiet and active regions) published in Fontenla et al. 1999 and Fontenla et al. 2011, respectively. At this point, it seems that the 2011 models are an overall better representation of our quiet sun observations than the 1999 models, for the 1999 models have far deeper line profiles than would be expected for the quiet sun atmospheric structures that were observed. By determining the atmospheric models that best fit the observations, our results provide important information for improving the understanding of the solar atmosphere and for the modeling of stellar spectral variability that would, in turn, impact the search for habitable exoplanets.

Slides  |  Poster

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18. Investigating the Relationship Between Coronal Loop Geometry and Polarized Emission

Mia Gironda, Tom Schad, Lucas Tarr, and Tetsu Anan

Polarimetry of spectral lines is a promising technique for studying the magnetism of the solar corona. By studying the polarization emitted from individual coronal loops, we can gain a better understanding of the sun’s magnetic field in these structures, globally, and the production of solar eruptions, which impact Earth. The data for this project is taken by the Mauna Loa Solar Observatory using the Coronal Multichannel Polarimeter instrument, which observes coronal emission lines and measures its polarization. Here, we study the Fe XIII 1074 and 1079 nm polarized emission lines. Our goal is to develop a new means of studying coronal features using this spectral line. We implemented the Rolling Hough Transform, used to calculate the angles of the coronal loop in a specified coordinate frame. We then found the angles with respect to the solar limb (the inclination angles), based on the assumption that the loops lie in the plane of the sky. This allows us to investigate the loop’s observed properties relative to the theory of polarized line formation. We analyzed the relationship between the azimuthal difference (the minimum angles between the Rolling Hough Transform angles and the angles collected by the Coronal Multichannel Polarimeter instrument) and the inclination angles. We expected this difference to either be 0 or 90–changing when the inclination angle is 54°, known as the van-Vleck angle. Instead, we saw a linear relationship, which begs the question of where these extraneous points are coming from. We’re in the preliminary stages of analyzing the relationship between the linear polarization fraction (the polarization of the 1074nm emission line divided by its intensity) and the inclination angles. We compared our data to the same relationship of an idealized Fe XIII emission line. Our findings follow the same trend. These similarities provide information about the electron density and evidence that the polarization is changing at the van-Vleck angle.

Slides  |  Poster

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19. Using characteristics of solar granulation to predict solar flux emergences

Hallie Dodge, Lucas Tarr, Tetsu Anan and Tom Schad

Flux emergence dramatically alters the magnetic field configuration of the sun and drives solar activity. The ability to predict magnetic flux emergence from changes in solar granulation patterns would be an extremely useful observational tool. We applied a downhill tessellation algorithm to characterize the properties of the granules and trace changes in patterns over time. The specific properties we traced were area, eccentricity, and orientation. Using observational data from IBIS, we refined our granulation detection function and then applied it to a time series of simulation data from Muram. There were two notable shifts in these granulation patterns over time. Firstly, eccentricity patterns trended towards more elongated at the beginning of flux emergence and secondly, granule orientations became more aligned.

Slides  |  Poster

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20. Spatio-temporal Characterization of Hot Chromospheric Fibrils

Parker Lamb, Gianna Cauzzi, Kevin Reardon and Benoit Tremblay

While the solar chromosphere has been studied extensively, the exact mechanism(s) by which it is heated above radiative equilibrium temperatures remain unknown. To the solar physics community, this is known as the “chromospheric heating problem”. Within this study, we look at chromospheric features known as “fibrils”, enigmatic temperature and density variations in the solar chromosphere, often observed in multiple diagnostics like UV or optical lines, and the millimetric continuum. Through analysis of the H-alpha spectral line, these fibrils are found to be the site of significant heating within the chromosphere. We use image recognition techniques to study the characteristics of these hot fibrils, including their length, number density, width and proximity to magnetically active regions. This allows us to place constraints on possible heating mechanisms within the chromosphere.

Slides  |  Poster