Exoplanets are any planet that’s outside of our solar system. There’s a myriad of ways to detect these planets, one of which is transit-timing variation. A planet is transiting when it passes in front of its star. If there is more than one planet in the system, there will be variations in the times at which it transits. This is due to gravitational perturbations from the planets on each other. This project focuses on the TRAPPIST-1 system, and more specifically Planet h of this system. We have the transit-timing variation (ttv) data for 7 planets in this system (named Planet b-h). We use an N-Body model to simulate the transit times, and compare this to the data to find the physical parameters of the system. An N-body model of 6 planets, and the data of 6 planets (excluding the Planet h’s data), is used to find the parameters. Then an N-body model of 7 planets is used with the data of the 6 planets. In comparing these models, we predict that the 7 body model will fit better. Although the 7th planet’s data is excluded, the signal from this planet will still be present in the ttvs of the other planets, due to the gravitational perturbations. If the model does fit better using 7 bodies in the N-body simulation but only data from 6 planets, we can see that the transit timing variations are strong enough to tell us that there is a 7th planet. This can be translated to other systems that have similar transit timing variation to see if adding another planet will create a better fit.

 Often, after a night of observation, the images that a telescope collects will have pairs of point sources so close together that the observer will not be able to resolve the distance between them. The observer may incorrectly conclude there is one source with the combined flux of the two real sources. This source confusion is the issue that we wanted to undertake. Accurate estimations of the flux of stars are very important: astronomers use these measurements for standard candles to measure distances to nearby celestial bodies, to discover the presence of binary systems or exoplanets, and generally in time-domain astronomy. We created computer models of two-star stellar images with varying fluxes, distances between sources, and Gaussian and Poisson distributions of noise. We then wrote maximum likelihood fits to find the threshold in which you can determine the existence of two stars. Through this method, we began to determine trends in this threshold as a function of source flux ratio and distance between sources, and the noise level. We then selected a fitting function that best modeled our simulated data, which can be used to predict the distance in which a user can tell two stars apart. In the future, we plan to test our method against existing catalogs of blended stars.

In order to compare observations of massive stars with theoretical predictions, stars must be classified accurately. Classification traditionally relies upon expensive new telescope observations. As the field of astronomy enters a new era of Big Data, Modern computational techniques may be used in place of these methods; however the results still require rigorous validation in order to be trusted. Recently, Dorn-Wallenstein et al. (2021) utilized a novel machine learning technique to classify a large sample of massive stars. This resulted in putative classifications for ~2550 stars. Our project serves as a follow-up to validate the results of Dorn-Wallenstein et al. and identify stars with rare evolutionary phases that are most useful for probing stellar evolution. We test the hypothesis that these classifications are reliable by obtaining new observations with the ARCES instrument mounted on the Apache Point Observatory 3.5-meter telescope. Using these data, we assigned classifications to the stars in our sample, focusing on identifying rare objects and evolved supergiants. To this end, we have developed custom software designed to navigate through the key features in our observations and allow for easy identification of an object's spectral type (i.e., its evolutionary stage). We find that our observations support the classifications made by Dorn-Wallenstein et al. Our future work will focus on expanding our sample with further observations in the Southern Hemisphere. This work is critical in order to prepare for the age of Big Data in astronomy.

Charged particles in the heliosphere can be continuously accelerated by interplanetary shocks and eventually escape from these shocks without returning to it. Acceleration and escape are highly intertwined and both contribute to the shaping of the particles’ momentum spectrum at the shock. The simplest model which describes this phenomenon is called Diffusive Shock Acceleration (DSA). DSA has been very successful in describing several observations. However, DSA does not include an energy-dependent escape from the foreshock region. We expand upon DSA by presenting a model for interplanetary shock acceleration which includes this energy-dependent particle escape. We analytically solve a one-dimensional transport equation with a diffusion coefficient and an escape time that describes both the turbulence self-generated by the shock and the far upstream pre-existing turbulence. We consider the case where a shock encounters a population of pre-existing charged particles with a power law energy distribution as measured by spacecraft. We find that at lower energies our solution is concave, whereas at higher energies it asymptotically approaches a power law whose slope depends on the original energy spectrum’s power law index and shock parameters. We fitted the solution obtained from this transport equation to shock data measured from multiple shock events collected by ACE/EPAM (the Electron, Proton, and Alpha Monitor aboard the Advanced Composition Explorer spacecraft). We find that for the shock events considered, our model’s best fit parameters match very well with the predicted values, obtained by using the measured shock parameters. From this model, we can better understand the mechanism of interplanetary shock acceleration and how this phenomenon energizes charged particles near the Sun and around other objects (for example, blazars and supernova remnants).

The circumgalactic medium (CGM) is the extended gaseous halo that typically surrounds the visible parts of a galaxy - the beautiful spiraling disks that one may recognize from an image captured by the Hubble Space Telescope. Although the CGM harbors no stars, making it only faintly illuminated by the interior galaxy and background sources, it is believed to contain far more mass than the galaxy itself. Understanding the CGM is thus key to understanding galaxy formation and address questions such as: Why are some galaxies red and some blue? Why are we only observing ~20% of the baryons (regular matter, not dark matter) we should observe in galaxies? How do galaxies like our own sustain star formation, enabling the particle diversity we see everywhere around us? The way we currently observe the CGM’s properties is through absorption spectroscopy by using a bright background light source (quasar) and observing how the light is absorbed as it travels through the CGM before reaching our telescopes. However, this limits our observations to only a “pinhole” view of the CGM’s properties. With the advent of instruments sensitive enough to observe the light emitted from the CGM, we will be able to create so-called “emission-maps” of the full state of the gas - including temperature, density, and element abundances. Our project has identified what wavelengths (ionic lines) are most observable with more sensitive telescopes, some of which are already underway. I have run computer simulations in the Cloudy program to identify these emission lines and developed models estimating their intensity. The culmination of our work shows that many of these CGM emission lines are detectable with feasible instruments in the near future, laying the groundwork to justify future missions targeting these specific lines in order to investigate the most pressing questions of galaxy evolution.

Massive binary star systems influence the formation and evolution of galaxies through production of energy and heavy elements. These systems can evolve into X-ray producing high mass binaries: a neutron star or black hole accreting material from a high-mass companion. While high-energy mass accretion makes these sources especially important, it makes them exceedingly difficult to model. One extremely useful characteristic for testing models of their formation and evolution is their temporal variability. By observing the properties of these sources over time, we can directly compare observed variability against predictions from models. M33, a large spiral galaxy just under three million light-years away from the Earth, is home to a collection of these X-ray binaries, last catalogued in 2015. With the goal of constraining the variability of the binaries, I have analyzed five separate epochs of Chandra imaging data taken after the production of this catalogue. I compiled a preliminary list of observed X-ray sources in the images and used positions of the previously cataloged sources to correct the astrometry, ensuring consistent alignment among observations. I then selected the most accurate position estimate for each source by-eye. Combining data from all aligned observations, I have now extracted a catalogue of reliable source detections, as well as source properties such as position and flux. With these values, I have assessed source validity and created lightcurves to study their variability characteristics. Through this work, I have found a total of 56 bright sources that meet my criteria for validity, of which 49 sources were previously identified in literature and 7 sources are new, appearing only in our observations. Looking at the characteristics of both new sources and previously observed sources, I will constrain source variability to determine how much the X-ray binaries of M33 changed in brightness over the course of the observations.

This research project involves analyzing planetesimal accretion through the use of an N-body simulation. A terrestrial planet passes through many stages of growth including: dust grains, pebbles, planetesimals, embryos, to planets. This study focuses on the formation process between planetesimals and embryos. Current simulations demonstrating terrestrial formation use parameters similar to those of our own solar system. This investigation attempts to envision this process at a more “bunched” up scale, such as in the case of the Trappist-1 system. Our inner solar system, a radial distance from the Sun to Mars, is about 25 times larger than the entire Trappist-1 system, meaning that its planets were formed much closer to its star. Through the use of N-body simulations, we can begin to understand the unknown formation of this system as well as others with similar characteristics. These N-body simulations are processed through the University of Washington’s supercomputer Hyak, approximating the motion of the particles that represent the planetesimals and detect if any are in a collision course. Two short period simulations were run using a number of sophisticated collision models that differ in how the particles interact and formation efficiency. The previous collision model used parameters calculated in 2005. The second newer model uses parameters from 2021, which I have programmed into the model's initial condition files. The study’s purpose is to compare the outputs of the collision models through a variety of quantitative and qualitative factors, concentrating on particle growth and runaway growth. More specifically, the data is measured through plots that depict the semi major axis vs. eccentricity, max mass over mean mass as a function of time, and the ratio of collisions that result in a merger. This will later lead to the investigation of which models can accurately replicate terrestrial formations such as the Trappist-1 system.