Astronomy, Cosmology, and Astrophysics
Optimizing Astrochemistry Model Parameters Through Deep Learning Methods
Presenter: Zachary Mark Stomski
Faculty Sponsor: Andrew Burkhardt
School: Worcester State University
Research Area: Astronomy, Cosmology, and Astrophysics
Location: Poster Session 2, 11:30 AM - 12:15 PM: Room 163 [C13]

Determining optimal physical conditions for astrochemical modeling is challenging due to the complexity and interdependence of the parameter space that governs them. Many of these parameters influence molecular abundances in complex, nonlinear ways, making manual tuning difficult and often subjective. Traditionally, researchers have addressed this challenge by adjusting only a subset of parameters deemed most influential, which could lead to biased or suboptimal results. To address this, we developed and trained a deep learning neural network to optimize physical parameters based on observed molecular abundances of astrophysical sources. The model creates predicted results using the three-phase NAUTILUS astrochemical model, which can produce accurate results of an astronomical source's molecular abundances over time given input parameters. By running NAUTILUS simulations with the model's predictions and comparing the results with observational data, the neural network iteratively refines parameter estimates to achieve the best possible fit. This approach streamlines the process for producing parameters, reduces human bias in parameter selection, enables more consistent and reliable simulations, and ultimately promotes comparability across publications. While our primary focus was fitting parameters for TMC-1, the model is adaptable to any astronomical source given the appropriate data. Using this method, we were able to half the average loss from outputted abundances compared to previous research's best parameter sets, and found major shifts in the parameters space that were required in order to do so. The model was unable to optimize to a physical parameter space without the use of restricting normalization techniques.

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Simulating the Chemistry of Interstellar Shocks
Presenter: Devis Kadi
Faculty Sponsor: Andrew Burkhardt
School: Worcester State University
Research Area: Astronomy, Cosmology, and Astrophysics
Location: Poster Session 2, 11:30 AM - 12:15 PM: Room 163 [C14]

Space is filled with cold, dark clouds of gas and tiny dust grains. These dust grains are completely covered in thick layers of ice. Scientists believe that this ice holds the basic chemical building blocks of life. When new stars are born inside these dark clouds, the process is very violent. The new stars create powerful, fast moving shock waves that slam into the surrounding gas and dust.

When these shock waves hit the icy dust grains, the impact is huge. The crash heats up the ice and blasts it off the dust grains, turning it straight into gas. This sudden change starts rapid and extreme chemical reactions that would not happen otherwise. My research project uses advanced computer simulations to study exactly how this violent process works.

In the past, many computer models left out important details, like non diffusion chemistry and the effects of intense radiation. My project improves on this past work. By running simulations at many different shock wave speeds, I track how new, complex chemicals form over time. I run the tests both with and without these specific chemical and radiation processes, and then I compare the results to see what changes.

Ultimately, this research helps us clearly understand how complex organic molecules, the very things needed for life, can form and survive in the extremely harsh and freezing environment of deep space.
Standard Curriculum Within Introduction to Astronomy Courses
Presenter: Jake Edward Krisiak
Faculty Sponsor: Andrew Burkhardt
School: Worcester State University
Research Area: Astronomy, Cosmology, and Astrophysics
Location: Poster Session 2, 11:30 AM - 12:15 PM: Room 163 [C15]

There is an issue in the historiography of Astronomy of Eurocentrism, and this Eurocentrism is overshadowing the massive contributions made by other civilizations throughout history. In the Intro to Astronomy, there is this gap in the teaching of Astronomy's history, and I have developed an in-class activity to help fix this issue by researching 6 civilizations (Greece, Egypt, Babylonia, Mayans, Umayyad Caliphates, India) and outlining questions about what contributions they made throughout their existence.
A Python Implementation of Gauss's Method for Determining Asteroid Orbits
Presenter: Milagros Tamara Giraldo
Faculty Sponsor: Jennifer Winters
School: Bridgewater State University
Research Area: Astronomy, Cosmology, and Astrophysics
Location: Poster Session 3, 1:15 PM - 2:00 PM: Campus Center Auditorium [A27]

Accurately predicting the orbital trajectory of celestial objects is essential for precise spacecraft navigation, planning planetary missions, avoiding potential collisions with space debris, and studying the long-term stability of planetary systems. Gauss’s method for orbital determination provides a way to predict the path of a celestial body accurately using a small number of observations. This research focuses on understanding the mathematical and physical foundations of Gauss's method. Based on this understanding, we developed a fast, user-friendly implementation of Gauss’s method in python that incorporates modern coding practices and code efficiency. Our implementation allows the analysis of multiple sets of data within a few seconds, which allows us to easily generate statistics and compare results. Our work provides the opportunity to study Gauss's method itself, which allows us to explore deeper questions, such as the ideal spacing between observations, when the method is most accurate, and in which cases it fails to produce valid results. Using our Python implementation, we can investigate why and how these issues occur.
For this project, we created a GitHub repository open to the public that includes the Python implementation, along with a short paper that walks through the Gauss method, its mathematical assumptions, and its derivation, with the intent of making the method accessible and understandable to anyone, regardless of experience.

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Determining Rotation Periods for Stars in Two M-Dwarf Binary Star Systems
Presenter: Casey Rush
Faculty Sponsor: Jennifer Winters
School: Bridgewater State University
Research Area: Astronomy, Cosmology, and Astrophysics
Location: Poster Session 3, 1:15 PM - 2:00 PM: Campus Center Auditorium [A28]

It is well established that stellar rotation is dependent on age and mass for single and widely separated binary systems of M Dwarfs (stars 10-60% the mass of our Sun). However, about a quarter of M Dwarfs have stellar companions, most of which are closer than 50 times the Sun to Earth distance. We know less about these systems due to challenges in their analysis. A remaining question is whether there is a separation where these dependencies no longer hold. Thus, it is important to determine rotation periods for stars in binary systems.
We analyzed Transiting Exoplanet Survey Satellite (TESS) light curves for M-dwarf binaries GJ 896AB (20.2LY) and LSPM J2240-4031AB (38.8LY). We detected two rotation periods for each system but were unable to match them to their stellar components from TESS data alone due to the large pixels on the TESS cameras. Because these systems are resolvable at ground-based facilities, we used the Cerro Tololo Inter-American Observatory (CTIO) 0.9m telescope to obtain photometric data. We performed relative photometry to match period detections to their sources. We also analyzed rotational broadening measurements from high-resolution spectrographs. For GJ 896AB, we determined rotation periods of 1.07 and 0.40 days for the primary star and companion respectively. For LSPM J2240-4931AB, we are still unable to determine which rotation period corresponds to which star. Our results add to our growing understanding of rotation periods in M-dwarf binary systems.
What Powers the ‘Little Red Dots’ Discovered by the James Webb Space Telescope?
Presenter: Jackson Gilbert Mello Wetherbee
Faculty Sponsor: Min Yun
School: UMass Amherst
Research Area: Astronomy, Cosmology, and Astrophysics
Location: Poster Session 4, 2:15 PM - 3:00 PM: Room 165 [D10]

Little Red Dots are a new classification of high redshift sources discovered by the James Webb Space Telescope. These sources are named after their small size and red color. The nature of these sources is still unknown, as they exhibit traits similar to those of an Active Galactic Nucleus. They also however fail to exhibit certain traits that are characteristic of an Active Galactic Nuclei and some scientists believe other origins for these sources like dusty star formation caused by a massive star cluster. In this project I will show the upper limits of different radio properties of little red dots gathered from the COSMOS 2024 catalog. We will replicate the methods found in the literature to significantly reduce the known upper limits for radio flux, Active Galactic Nuclei power, and Star Formation Rates from these sources. By combining deep radio survey data with careful statistical stacking techniques, this study aims to improve sensitivity beyond previous analyses and place tighter constraints on these upper limits. These results will hopefully help us get a better idea for the nature of these sources as radio waves can offer us valuable information and could narrow down the possibilities for the origin of these sources. Ultimately, refining these limits will clarify whether their emission is dominated by accretion onto supermassive black holes or by compact, dusty star-forming regions in the early universe.
Log-Normal as the New Norm? Testing a Log-Normal Model for Multiphase X-Ray Spectra
Presenter: Daniel Owen Kidwell
Faculty Sponsor: DANIEL Q. WANG
School: UMass Amherst
Research Area: Astronomy, Cosmology, and Astrophysics
Location: Poster Session 4, 2:15 PM - 3:00 PM: Room 165 [D11]

The center of the Milky Way is the closest system that we can probe to deepen our understanding of how galactic nuclei operate. This region hosts some of the most energetic processes in astrophysics including, stellar winds, shocks, magnetic recombination, producing a highly multiphase plasma with a broad temperature distribution. Spectral analysis of these regions is often simplified to isothermal and fit using an APEC plasma model. This approach while convenient, does not capture the complexities of these regions. Our goal is to test a more physically motivated approach to fitting multiphase plasma by utilizing a log-normal model allowing us to use a range of temperatures instead of just one. 

We use hydrodynamic simulations of the stellar winds in the galactic center to simulate observations of multiphase plasma. Using the Python packages PyXSim and SOXS, we create synthetic spectra that are then analyzed in XSPEC. Each is fit with both an isothermal APEC model and our log-normal model to facilitate direct comparison. We expect that this model will perform much better than the APEC model and trace out the spectra more accurately, as well as recover metallicities that agree with simulation input. Testing out models like this that are physically motivated can result in newfound methodologies that further our understanding of these complex systems. Better model fitting can lead to unveiled dynamics and fundamental parameters once shrouded behind a simple generalization.

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