A Journey of Human Advancement
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som.tyagi.21@gmail.com
The NASA High School Aerospace Scholars (HAS) program provided a challenging, multi-stage introduction to aerospace systems and planetary science. The four modules replicated a distinct mission development phase, combining systems engineering, space environment analysis, and scientific modeling. I used orbital mechanics in planning missions, worked with atmospheric data sets in remote sensing labs, created sustainable space habitats through life-support reasoning, and topped it off with using all of these skills into a long-duration human mission concept. The formal NASA structure compelled me to cut across disciplines, translating space science into operational plans—a space experience that mimicked actual space operation.
As an officer at my school's American Rocketry Challenge club, I co-coordinate the systems design and prototyping process for our national team. I designed and tested modular payload bays, integrated altitude trackers, and coordinated simulation runs with OpenRocket and CAD modeling tools. Beyond engineering, I helped to coordinate our design review process, managed part procurement, and coordinated operations on launch day. This experience did not only refine my aerospace technical skill but also taught me how to operate real-time systems with physical constraints and time limits.
For this independent astrophysics research project, I investigated how phenomena after supernovae could lead to the formation or ejection of planetary-mass bodies. With simulations of stellar collapse and gravitational encounter, I experimented with potential scenarios in which debris disks might coalesce into rogue planets or destabilize existing planetary systems. This work integrates core physics, dynamical modeling, and observational data to demonstrate how catastrophic stellar outbursts can create new celestial bodies indirectly. This research also broadened my understanding of planet formation under non-traditional conditions.
In this project, I applied unsupervised machine learning techniques to astrophysical light curve data so that it was possible to distinguish between genuine supernovae and giant stellar eruptions, such as those which occur from luminous blue variables (LBVs). I built a clustering-based model based on principal component analysis (PCA) and k-means to identify characteristic temporal structures in the brightness data. The model successfully found photometric features that could distinguish between eruptive and explosive stellar behaviors, even from noisy observations. This interdisciplinary work brought together astronomy, statistics, and machine learning—demonstrating how computational approaches can accelerate astrophysical research.
In collaboration with the International Astronomical Search Collaboration (IASC), I was part of a NASA-partnered project to screen raw astronomical image data from the Pan-STARRS observatory. Using a systematic search with Astrometrica, I was successful in detection and reporting of previously unmonitored main-belt asteroids and this was later confirmed and listed. The discovery process involved searching for motion, cross-checking positional data with fields of stars and adherence to MPC reporting standards. It was practical experience with accuracy and patience of observational astronomy and object monitoring.
A Journey of Human Advancement
som.tyagi.21@gmail.com
