ATLAS is one of the four major experiments at the Large Hadron Collider (LHC) at CERN. It ia general-purpose particle physics experiment run by physicists from all around the world. ATLAS physicists test the predictions of the Standard Model, which encapsulates our current understanding of what the building blocks of matter are and how they interact. These studies can lead to ground-breaking discoveries, such as that of the Higgs boson, physics beyond the Standard Model, and the development of new theories to better describe our universe. ATLAS is made up of many different instruments and subsystems, and this research focuses specifically on the Muon Spectrometer. Specifically within the Muon Spectrometer, this research analyzes data from Monitored Drift Tubes (MDTs). In short, muons are one of the very few things that get through the first three detectors within ATLAS, and MDTs work to trace the curved path of the muon as it passes through, which then allows for the calculation of its momentum. MDTs are filled with gas, and as the muon passes through a number of tubes, it leaves a trail of charged electrons that drift to the center of each tube. Recording the time of this drift process is what leads to the tracing the muon’s path. The goal of this research is to understand the issues that may develop in these drift tubes while everything is running and data is being taken. An example of this is a Single Event Upset (SEU). SEUs occur when a large energy deposition from one of the charged particles disrupts the functioning of the electronics, specifically, a state change of a logical element (a memory bit). The study will produce and compare a large variety of histograms from the beginning and ends of runs, and checking for any discrepancies. The research will look at different types of issues and their frequencies in 2018 proton-proton collision data.
Research Discipline(s): Physical Sciences
Herbig stars of spectral type Ae/Be are young stars of 1.5-10 solar masses. Current models of young stars’ temperatures and radii as a function of mass are not well calibrated. In binary star systems, a transit, or a periodic dip in apparent brightness of the star, occurs when the one star passes in front of the other star, blocking some of the light of the primary star, creating an eclipse effect. These eclipsing binary star systems allow us to obtain estimates for parameters of the orbiting objects such as the masses, radii, orbital period, orbital inclination, semi-major axis, et cetera. Obtaining these parameters for these young stars will allow for better calibration for models of young stars’ properties. In general, researching stars unlike our sun is important because it gives the context that our solar system exists in and gives us a broader understanding of the universe. We reduced, analyzed, and modeled the Herbigs’ light curves, which are plots of flux versus time, from the NASA TESS mission. We identified and examined the light curves of stars that showed evidence of orbiting companions using Python packages such as Batman, Eleanor, and Phoebe. Using these packages we were able to create and fit model light curves to the data and obtain estimates of parameters for our stars of interest.
As we continue to search the galaxy for habitable planets, our attention has turned to super-Earths, large rocky planets with the potential to support life. The increase in size and mass is expected to change several significant properties, including the planet’s magnetic field. Planetary magnetic dynamos are crucial for life to develop, and scientists have long debated the mechanics of dynamo formation and operation. Utilizing data on the planetary composition in our solar system and relevant material properties under high pressures and high temperatures, we will predict the likelihood for the development of a super-Earth dynamo. Earth, the largest of the four rocky planets in the Solar System, may have sustained its dynamo the longest, but Mercury, the smallest, is the only other planet to have a still-functioning dynamo. This indicates that size is only one of many factors that impacts dynamo evolution. Thus, various corroborating effects must be accounted for in our analysis. To model the internal dynamics of exoplanets, samples of core material will be compressed to high pressures. The experiments will offer data on whether a planetary core would be molten, how it would transfer heat and undergo changes with pressure. Additionally, models will be created to form predictions about the impact of mass, radius, and other properties on the formation of core dynamos. Once the extent of internal convection is defined, the model will allow for the estimation of a range of masses or radii for a super-Earth to produce a magnetic field.
After observing that the expected velocities of stars in the outer parts of galaxies is much faster than expected, scientists conclude that in order for stars in all locations of galaxies to move at the same velocity, another force must be present. This invisible matter, which does not exist with electromagnetic forces or gravitational forces, was named Dark Matter. The LUX-ZEPLIN (LZ) experiment, located at the Sanford Underground Research Facility in South Dakota, uses a time projection chamber (TCP) filled with several tons of liquified Xenon in order to directly detect dark matter particles. Surrounding the Xenon chamber is a second chamber containing thousands of gallons of water, ensuring that the experiment reaches maximum sensitivity. Ultimately, it is expected that dark matter will interact with the Xenon particles through WIMPs (weakly interacting massive particles). As this experiment progresses, we expect to collect large amounts of data regarding interactions between dark matter particles and Xenon particles. Though dark matter makes up almost one-third of all the matter in the universe, scientists have yet to directly detect it. Through graphically analyzing LZ’s data, we hope to detect and document dark matter’s direct interactions with Xenon. Furthermore, we hope to expand our understanding of dark matter’s properties, and to use them to explain both the development and the functionality of our universe as a whole.
Despite a large amount of research devoted to astronomy and black holes, many open questions remain about these celestial objects. It is unknown how matter behaves around black holes, and specifically, how the strong gravity of the black hole affects its surroundings. These objects are observed at different wavelengths (X-ray, optical, etc), and the question we seek to address is whether the characteristics of variability at different wavelengths are correlated with each other or with other black hole properties. This study compares X-ray light curves, that are emitted near the black hole, with visible light curves, that are emitted at larger distances from the hole. A list of black holes, from the paper “X-ray variability of 104 active galactic nuclei”, was used as the sample for this project. The corresponding lightcurves were obtained from the ASAS-SN sky survey and subsequently, the lightcurves had their outliers removed. The variability of the lightcurves was then characterized by measuring the normalization and index of the power spectral density (PSD). We then correlated our measurements with the mass and luminosity of the black holes as obtained from the literature. We found the index to not correlate with the mass and luminosity. This study adds nuance to our understanding of black holes. Future studies can explore the ASAS-SN dataset further and extend their search to binary black holes. There is a lot of research going into this area and these results can help contribute to a future project that looks to understand these objects further.
Despite the many studies conducted and research findings produced which illustrate a stronger understanding of cosmic functions within the universe, there is still very little known about the processes of galaxy growth and evolution. Researching the distinct mechanisms by which galaxies grow and evolve will further elucidate the state of the universe following the Big Bang and the creation of celestial bodies. This project takes a chemical approach in studying the mystery of galaxy growth and formation by focusing on the composition of large scale gas reservoirs around galaxies with supermassive black holes known as quasars. These gas reservoirs provide fuel for supermassive black hole growth, yet also harbor information regarding the events which induce galaxy evolution. This project uses Python graphical user interfaces and data from the Sloan Digital Sky Survey to measure the incidence of gas observed around quasars. Through measuring the Magnesium II absorption strength within foreground and background quasar spectra, and then running a statistical analysis of the data, this project intends to determine the trends that exist between gas and quasar properties, and what drives the correlations between these and that of their chemical composition.
Though the phenomenon of solar wind has been studied for years and we have data regarding its composition, velocity, and temperature, among other things, there is still much to learn about its evolution and how its internal forces interact. Our team aimed to create an empirical model that can accurately predict the behaviour of variables such as temperature and velocity in the solar wind as a function of distance. We created two different models, one using a Markov Chain Monte Carlo system, and another using gradient descent. By repetition and comparing results against a database of previously recorded solar wind measurements, parameters in a starting equation are allowed to randomly change, and the best fitting model is iteratively chosen. These results will not only give insight into the evolution of solar wind, but they will pave the way for other, more advanced techniques to produce more accurate models. Additionally, understanding the evolution of solar wind will provide guidance on the design of instrumentation to be launched aboard Solar Orbiter satellites.
Though the phenomenon of solar wind has been studied for years and we have data regarding its composition, velocity, and temperature, among other things, there is still much to learn about its evolution and how its internal forces interact. Our team aimed to create an empirical model that can accurately predict the behavior of variables such as plasma density, temperature and velocity in the solar wind as a function of distance from the Sun. We created two different models, one using a Markov Chain Monte Carlo system, and another using gradient descent. By comparing results against a database of previously recorded solar wind measurements, parameters in a starting equation are allowed to randomly change, and the best fitting model is iteratively chosen. These results will not only give insight into the evolution of solar wind, but they will pave the way for other, more advanced techniques to produce more accurate models. Additionally, understanding the evolution of solar wind will provide guidance on the design of instrumentation to be launched in future space missions.
To build the model for COVID-19 confirmed and death cases, our group used several approaches to predict the data. We first build the model by Ridge Regression and it turns out to be pretty good on ordinary input. However, for states like MI which have a large fluctuation in data, Ridge Regression performs poorly. Then, we start to use the Neural Network approach and it turns out to be better at unordinary data; however, it has the problem of overfitting. After discussion, we add the social mobility data into account and it greatly reduces the error. Currently, we are working on the approach to predict state-level and add all the models up to predict the whole US.
The Los Alamos nEDM experiment measures the electric dipole moment of a neutron, which could improve our understanding of the dominance of matter versus antimatter in the universe. For the experiment, we need to know the magnetic field and track the neutron spin along its path from the neutron source to the experiment. The magnetic field was calculated with a commercial program, which integrates currents in magnet windings. A component of the field was reverse engineered by comparing it to a map of the magnetic field of two coaxial solenoids. We altered the current, windings, lengths, and inner and outer radii for both solenoids. It was determined that a radii ratio of 0.55 and a winding ratio of 6:5 yielded an excellent match to the data. The solenoid field combined with calculations of fields produced by a combination of additional coils will be used to generate the fields over the full neutron path and used to predict the effect on the neutron spin and guide design modifications.