When massive stars die, they explode in violent spectacles known as supernovae, specifically core-collapse supernovae. These cataclysmic events produce and distribute a large fraction of the heavy elements in the universe, but the properties of the massive stars that produce them have historically been difficult to measure. I have made new measurements constraining the masses of stars that have produced core-collapse supernovae, also known as supernova progenitors. I have done this by measuring the ages of stars at the location of supernova remnants: the nebulae of excited and enriched gas left behind by supernovae that have occurred over the past 20,000 years. Assuming the progenitor was associated with these stars, I am able to estimate the age of the star that exploded. Using theoretical models, I am able to infer the mass from this age. I used images taken by the Hubble Space Telescope to investigate the stars responsible for producing hundreds of these remnants in the nearby galaxy NGC 6946. In addition to the remnants of supernovae, this galaxy has hosted ten observed core-collapse supernovae within the past hundred years, leading to it being referred to as the “Fireworks Galaxy”. I was able to constrain the progenitor mass distribution for 175 remnants, eight of the historically observed supernovae, as well as the progenitor of the first direct black hole formation candidate in NGC 6946. I found the distribution of progenitor masses was consistent with mass distributions measured for massive stars in other galaxies, including our own Milky Way. These new measurements allow NGC6946 to be included for the first time in statistical studies of the masses of stars that produce supernovae.

I am furthering research in growing successful plant life in Martian regolith. The intention of this research is to amend Martian regolith so that it can support edible plant growth. We intend to counteract the characteristics that would impede plant growth and introduce low-mass additives that support plant growth. To simulate Martian ground “MMS-2” is used, this regolith is designed to be over 90% similar in chemical and textural composition to ground cover on Mars.Challenges noted from previous research include inhibited root growth, inefficient nutrient absorption, and impeded water uptake due to clay-like consistency. The additives selected to counteract these challenges include vermiculite to expand the soil and improve water circulation, and Biodyne™ microbes to enhance nutrient uptake. I chose to also incorporate mycorrhizal fungi to support and enhance root development and function. My previous research indicated that mycorrhizal fungi played a significant root development, plant height, and leaf growth. My current research is focusing on reproducing these results with more specimens and clear data points to further evaluate the impact of these additives. Rosemary was selected due to its ability to thrive in harsh, cold, and dry climates. As this plant is notoriously difficult to grow from seed, seeds were pre-sprouted to confirm viability prior to planting in the experimental pots in order to assure more specimens. Experimental regolith are treated with mycorrhizal fungi, Biodyne™, or both receive treatment mixed in water; all regolith and the control are based in a 30/70 earth soil to regolith blend with vermiculite. Plants treated with mycorrhizal fungi are anticipated to have darker and plentiful leaves; plants treated with Biodyne™ are anticipated to show straighter growth, plants treated with both are anticipated to be the most successful with both plentiful dark thick leaves and straight strong growth.

We are expanding previous research which manipulated the composition and chemistry of Mars regolith to better support plant life. The Mars regolith we are using is a 70% regolith 30% compost mix. Our focal plant for this experiment is Salvia rosmarinus (hereinafter rosemary) because of its resilience in cold climates and minimal water needs. Rosemary has been observed to have low germination success in both Earth soil and Mars regolith, to address this we sprouted seeds in water prior to transplant in order to guarantee seeds were viable rather than sowing rosemary directly in Mars regolith. We will ensure cotyledons have developed and stems have reached <5.0mm in length prior to transplanting in regolith to eliminate the variable of low seed viability. We hypothesize the amendments made to Mars regolith along with microbial and mycorrhizal additives will significantly improve rosemary development and growth. Plants we have previously attempted to grow in Mars regolith experienced difficulty establishing roots when transplanting cuttings and when grown from seed due to the clay-like consistency of the regolith. We have adjusted the previous experimental design by adding vermiculite to improve drainage and reduce regolith density, microbes to fixate Nitrogen and assist in nutrient uptake, and mycorrhizal fungi to encourage more effective root establishment and development. Previous experiments with Biodyne™ microbes and mycorrhizal fungi in Mars regolith have shown promising results in kale and rosemary. We anticipate the addition of Biodyne™ and mycorrhizal fungi, both independently and together, will improve root development, plant height, leaf growth, and chlorophyll richness. To test this, we will collect data bi-weekly by noting plant “greenness” and measuring plant height, leaf length, leaf count, and root growth.

The circumgalactic medium (CGM) is a massive reservoir of gas surrounding a galaxy in which density and temperature range several orders of magnitude and contains more mass than the galaxy itself–similar to a cloud engulfing the galaxy. The CGM also plays a substantial role in the life of its galaxy as it will govern the accretion of matter for the galaxy to continue star formation. To better learn about the CGM, many astronomers utilize simulations to test theories by comparing their simulation data with observational data. Yet, current simulations struggle to replicate the CGM and its breadth of properties accurately. This project uses the TEMPEST and Patient0 simulations of Milky Way-type galaxies and a novel analysis method–known as synthetic spectroscopy–to better understand the effects of cosmic rays on altering how the CGM gas is ionized and how cooler CGM gas moves within the cloud. Many simulations tend to omit cosmic ray physics, however, cosmic rays are believed to provide non-thermal pressure support which will change the ionization structure of the CGM. Through our use of synthetic spectroscopy, we extracted column density and velocity information of various ions, such as HI and OVI, from our simulation to generate velocity histograms and plots of column density versus distance from the galaxy. Ultimately, this provided us further insight into the impacts of cosmic rays on setting the ionization and kinematic properties of the CGM which will better inform us on galactic evolution.

NGC 6822 is a barred irregular galaxy located about 1.6 million light years away in the Sagittarius constellation. We are observationally identifying red supergiants (RSGs) in this galaxy to compare with stellar evolutionary models. Stellar evolutionary theory provides us with the expected quantity of RSG populations. The research conducted will allow for a comparison between observational data to theoretical expectations. Here, we propose a new sample of RSG candidates in NGC 6822 that can be utilized as an observational test of such theory. RSG stars are the coolest of the evolved massive stars and have K and M spectral types and temperatures below 4100 K. Typically, they can be up to a thousand times the radius of the Sun and are therefore highly luminous. To find them in NGC 6822, we first used parallax and proper motion values from the GAIA satellite to filter out foreground stars, before using the NIR color-magnitude diagram to eliminate lower-mass asymptotic giant branch star contaminants. Next we transformed the J and K magnitudes to effective temperatures and luminosities to create an HR diagram (HRD), and selected RSGs based on their position on the HRD. Currently, we are comparing our results to previous spectroscopically confirmed RSGs. In combination with population studies done by ourselves and others in the Local Group galaxies IC 10, M31, M33, and the Magellanic Clouds, we can test model predictions across a wide range of metallicities. Additionally, by locating a population of RSGs in NGC 6822, future possibilities for studying these massive stars with direct spectroscopic follow-up are created.

Star formation at the outer extremities of the Milky Way takes place under conditions much different from those in the rest of the Galaxy, giving us a window into how the process differs in a low-density, low-metallicity region. The Outer Scutum-Centaurus (OSC) spiral arm is the most distant molecular spiral arm in the Galaxy, lying about 15 kpc from the center of the Galaxy. In this study, we use the VLA radio array to observe 12 HII regions in the OSC, all of which had no previously existing continuum data. HII regions are areas of ionized hydrogen around massive stars and are the brightest objects in the radio spectrum across the Milky Way, making them perfect laboratories to study star formation in the outer Galaxy. These OSC HII regions represent the most distant known high-mass star formation regions in the Milky Way and give us an excellent laboratory for studying those processes in a low-density, low-metallicity environment. Our data let us identify radio continuum data for 7 HII regions in the OSC, as well as establish upper limits for the RMS associated with the other 5 observed nondetections. By assuming a single ionizing star for each region, we assign spectral types from O9 to O5.5 to these sources. Combined with existing data, we identify a total of 12 HII regions in the OSC Arm with continuum and spectral data. Further research would involve re-observing our nondetections to identify data for what are likely B-type ionizing sources. Obtaining meaningful data for those nondetections would allow us to classify more stars powering HII regions in the region, increasing the amount of information known about star formation in the extreme conditions of the OSC and potentially revealing new information about how O-type and B-type stars form differently in the same unique environment.

This project a follow-up study to the research conducted by my mentors, Trever Dorn-Wallenstein and Dr. Emily Levesque on the use of a Support Vector Machine (SVM) classifier to classify massive stars (Dorn-Wallenstein et al. 2021). My project is to verify that the SVM classifier sorted all the stars correctly by analyzing high-resolution spectroscopic observations of the stars visible from the Apache Point Observatory, and possibly other telescopes in the future. A support vector machine is a supervised learning model used in many fields for classification, regression, and outliers detection. In the original project, a support vector machine took a table with ‘features’ for each star (here a feature is a color or magnitude or an estimate of the star’s variability) and found the N-dimensional plane in the feature-space that best separates each class from all the other classes. Simply put, you might imagine that if you had a bunch of red and blue stars with color and brightness/magnitude measurements, that plane would be a vertical line in the Hertzsprung-Russelldiagram with all the hotter blue stars to the left and all the cooler red stars to the right. The SVM algorithm’s job was to figure out the parameters that best described each category or, in other words, a general description of what classification is. There are lots of ways to accomplish this, an SVM is just one particular way to calculate what the mathematically “best” plane in the feature space is to separate classes. My project is to go through the catalog generated by the SVM algorithm from the paper and verify whether the stars were sorted correctly. Many stars in the catalog have a pre-existing classification that can be verified, but many are not classified and the scope of my project is to identify, observe and classify them.

The Vera C. Rubin Observatory's proposed Legacy Survey of Space and Time (LSST) is expected to vastly expand our knowledge of moving objects present in the Solar System. Roughly 60 petabytes of astronomical data will be produced over the 10 year campaign, with which several million Solar System Objects are expected to be discovered. The new data analysis pipelines developed to handle the massive throughput generated by the LSST require testing on simulated datasets to ensure robustness and evaluate performance. I present a survey simulator that I have codeveloped with colleagues at the NASA Jet Propulsion Laboratory. The software can ingest real or modeled orbits of Solar System Objects and produces a corresponding catalog with billions of detections. Running the survey simulator on objects currently known as well as synthetic models of the Solar System, I have created a dataset that will play a vital role in the testing of LSST pipelines and create more extensive estimates of discovery rates for the various minor planet populations of the Solar System in order to confirm current expectations.

Modern astronomy predominantly consists of analyzing large data sets from automated sky surveys. The largest survey project, the Legacy Survey of Space and Time (LSST), is currently under construction at the Vera C. Rubin Observatory. One of its goals is to create a catalog of smaller objects such as asteroids and comets in the Solar System. The LSST Solar System Object catalog and other LSST-sourced data critically rely on the ability to predict object positions, as well as to recognize and to link previously unknown ones. Object positions are calculated with software known as integrators. There are several well-known integrators in the solar system dynamics community: JPL Horizons, OpenOrb, and OrbFit. They are credited as acceptable for calculating positions, however, they have not been rigorously compared to one another. This project addresses this issue. For my research, I built an automated system that compares the most popular integrators by testing them on a set of known objects. These objects are picked to explore both usual and unusual regions in space. The system compares the object positions by evaluating a number of metrics (e.g., on-sky distance, position vector 3D distance, and others), and will visualize the results in form of a dashboard. This allows for the assessment of various integration package suitability as a function of population to be integrated, as well as tracking their performance in an automated fashion as improvements and changes are made. The most important product of my work is the clear definition of each integrator's bounds of applications. Currently, this is the only comprehensive comparison of its kind. Using my comparison, other scientists will be able to decide what integrator to use for their specific use case. Given the broad implications, this work will prove to be invaluable to the astronomical community as a whole.

Astronomy of the 21st century is driven by large data sets collected by large automated sky surveys. The largest survey project currently being built is the Legacy Survey of Space and Time (LSST), which will be a 10-year survey of the southern sky, expected to discover 5.5 million small bodies in our Solar System. The greater scientific community needs to know what the research potential and scope of the data LSST will collect. I am building a database -- accessible at http://ls.st/ssdb -- of simulated LSST observations of asteroids in our Solar System. My work delves into simulation accuracy, big data analysis, and database management. This dataset consists of individual observations, an orbit catalog, and a catalog of physical and observational characteristics. Using simulations from the University of Washington’s Data Intensive Research in Astrophysics and Cosmology Institute (DiRAC) and scripting in Python, I am attempting to accurately present this data and areas of possible research before LSST becomes operational. This project will be integral to preparing for research projects that will analyze actual LSST data.