Alignment of BCGs and their Clusters

Using HSC Data

PDF link: Thesis

This is my Princeton thesis conducted over my Senior year. For this project, I worked with Michael Strauss. The project in question involves an investigation of the Binggeli effect - the tendency for Brightest Cluster Galaxies to align with their clusters, which has fascinating implications for cluster formation and evolution. The full abstract of my thesis is as follows:

In this thesis, we examine the alignment between Brightest Cluster Galaxies (BCGs) and the orientation of their clusters. In particular, we are aiming to determine whether their alignment is dependent on redshift, which other studies (discussed in the introduction) have suggested. To do this, we use the Hyper Suprime-Cam (HSC) s18a v2 catalogue to analyse 13373 clusters at 0.1 < z < 1.2. We then calculate the axial ratio and the orientation angle for both the BCGs and their host clusters. The initial results using these 13373 clusters show no correlation between alignment and redshift. We then impose certain criteria, selecting clusters with a large enough cluster size and a low enough redshift due to uncertainty in those objects (N > 30, z < 0.8), as well as the imposition that the BCG must have an axial ratio far enough from unity so that it has a well-defined orientation (α < 0.8). Doing so still reveals no evidence for alignment at all, and certainly no evidence for redshift dependence. We additionally find no correlation between dominance and alignment. Further work is recommended to assess if there are any statistical or technical problems that may be affecting these results, as well as a more thorough review of the prior evidence suggesting that there is a redshift dependence.

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Plasma Physics

Diagnosing Particle-in-Cell codes

For my Applied and Computational Mathematics Paper, I worked with Matthew Kunz on a plasma physics project. The project involved learning basic plasma physics, learning about a Particle-in-Cell code, and then attempting to diagnose the problems in this code.

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Orbital Dynamics

Understanding Chaos and Resonance

PDF link: Link to Junior Paper

This is my second Junior Paper conducted over my Junior Spring at Princeton. I worked with Adrian Price-Whelan for this Junior Paper. The formal title of the paper is "Chaos and Resonance in the Inner Stellar Halo from the Galactic Bar". The abstract of this (unpublished) paper is as follows:

This paper investigates the resonant and chaotic orbit structure of orbits in a barred galaxy such as our Milky Way consisting of a flat disc modelled by a Miyamoto-Nagai Potential, a bar modelled by a Long-Murali Potential, and a dark matter halo modelled by a Navarro-Frank-White (NFW) potential. Specifically, this paper computes a chaos indicator by integrating orbits over a set time period and computing orbital frequencies in the first and second half of this period, which can then be used to determine how stable or chaotic an orbit is. From this, the distribution of chaotic orbits in real space is plotted for varying Jacobi Energies in both the x-y and x-z plane, which in turn can be used to search for various resonant orbits and to assess their stability, including Lindblad resonances. The models used in this paper have applications to the analysis of chaotic dispersal of tidal debris, the analysis of moving groups, as well as the constraint of the dark matter distribution of the Milky Way, which are prospective future areas of research.

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Exoplanet Atmospheres

Investigating surface temperatures

PDF link: Link to Junior Paper

This is my first Junior Paper conducted over my Junior Fall at Princeton. I worked with Adam Burrows on an introductory project to better understand the topic of exoplanets. More specifically, this project sought to investigate the various factors that affects the surface temperature of an exoplanet. The abstract of this (unpublished) paper is as follows:

When designing a General Circulation Model (GCM) for an exoplanet, there are a multitude of different factors that must be considered to create an accurate climatic model, with primary factors being stellar irradiance, atmospheric composition, and atmospheric thickness. Other factors include rotational frequency and tidal locking in combination with climatic feedback mechanisms. This paper seeks to investigate one aspect of GCMs: the modelling of surface temperatures. The paper begins by building a simple model for calculating the surface temperature of a planet, and then improves upon the model by considering the effect of atmospheric composition upon the optical depth of various hypothetical atmospheres, including 100% H2, N2, O2, O3, CO2, H2O, and CH4 atmospheres, as well as various mixtures of these compounds. In addition the Earth's atmosphere is modelled to a reasonable degree of accuracy, and further suggestions are made as to how the model could be improved upon. This temperature model is then used to give an approximation to the boundaries of the habitable zone given various properties of the planet, including mass and atmospheric composition.

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Deforestation around Wood Pellet Plants

Investigating forest loss due to wood pellet plants in the US

PDF link: Link to Paper (to be added upon publication)

I worked with Tim Searchinger on this research project to investigate the impact of wood pellet plants in the United States on local forest loss. The abstract of this (to be published) paper is as follows:

In this paper, we analyse the impact of wood pellet production on local forest loss around wood pellet plants in the United States. We investigated and complied various datasets of wood pellet plants in the US, focusing on data from the EIA Densified Biomass Fuel Report (EIA-63C) in December 2019 for our main analysis. By analysing the year-on-year deforestation within set radii around wood pellet plants, we observe that certain wood pellet plants appear to see a moderate increase in local deforestation upon the start of their operation that is beyond the natural increase in deforestation for the state as a whole, although this trend is not ubiquitous. Additionally, this trend is observed for wood pellet plants as a whole when analysing the largest ten wood pellet plants by capacity, and also for a broader selection of 30 wood pellet plants, with ARIMA analysis on the pre-operation data forecasting a lower forest loss rate than what is observed. When an increase in forest loss is observed, it tends not to be observed close to the facility, but rather further away at scales of $10\textrm{km} < r < 25\textrm{km}$. Analysis of satellite imagery indicates potential areas that may have seen deforestation due to local wood pellet plant activity, but further work is recommended to verify that this forest loss is not due to other unrelated factors.

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The Ultimate Guide to Geoguessr

Using HSC Data

This is blog 1 (Geoguessr), coming soon!

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Image and Speech Recognition Attempts in Python

This is blog 2, coming soon!

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Resources for Learning how to Code

Python is the main language of choice in Physics and Astrophysics these days. To begin, I would recommend installing Jupyter Notebook. To do so, go here:

Here are some links for learning Python:

• https://www.learnpython.org/
• https://www.codecademy.com/learn/learn-python
• https://docs.python-guide.org/intro/learning/

It is important to get a taste of the libraries in Python. Here are some tutorials for some of the most useful:

• Numpy - https://numpy.org/devdocs/user/
• Matplotlib - https://towardsdatascience.com/matplotlib-tutorial-learn-basics-of-pythons-powerful-plotting-library-b5d1b8f67596
• Scipy - https://www.scipy.org/getting-started.html
• Pandas - https://www.learndatasci.com/tutorials/python-pandas-tutorial-complete-introduction-for-beginners/
• Astropy - http://learn.astropy.org/tutorials.html

While I'm here, I should note that I am developing a Python tutorial which can be accessed on my website/GitHub. Give it a go!

SQL

SQL is a common language needed to pull information from databases. It’s quite simple, and to that effect I think Codecademy does a good job.

https://www.codecademy.com/learn/learn-sql - especially the queries section

C

C is an older programming language that is still highly popular in the world today, and is often seen in certain physics projects. I commonly see this when looking at large computational simulations (in plasma physics and cosmological models).

To learn C, I would recommend this website: https://www.learn-c.org/en/Welcome

C doesn’t require an IDE like Python, but there is apparently a way of coding in C with Jupyter notebook if you like:

https://xuedong.github.io/programming/2017/12/27/jupyter-c-kernel.html

MATLAB

MATLAB is a relatively popular programming language in Physics, and is usually picked because of how the language treats vectors and matrix operations. You have to pay to download MATLAB, and it’s hideously expensive, so unless you’re a student of a university where you can download their version for free, don’t bother. In my experience MATLAB is also a marmite language: you either love it or you hate it. I am in the dislike camp as I find Python to be a lot more intuitive, but in case you want/need to learn, have a look at: https://matlabacademy.mathworks.com/

Java

Java is another popular language. I personally haven’t seen it used in scientific research at all, but I have had to learn it for computer science classes.

I would install DrJava. To do so, go here:

Then, there are a couple of ways to learn Java. I personally used the textbook Computer Science: An Interdisciplinary Approach by Robert Sedgewick and Kevin Wayne, as this also teaches concepts in computer science. The website to complement the book is https://introcs.cs.princeton.edu/java/home/. There is also a Python version of this (https://introcs.cs.princeton.edu/python/home/) if you're looking to compare and contrast the programming languages.

Another guide to Java is here: https://www.geeksforgeeks.org/java-how-to-start-learning-java/

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A Guide to Astrophotography

This is blog 4, coming soon!

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Designing a Good Research Poster

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Resources for the Subject GREs

Upon my studying for the subject GREs, I noticed that there is a bizarre dearth of resources that are accessible without paying hefty sums of money. In addition, many of the resources are of varying quality, and sometimes require more prior knowledge than one might like. I decided to rectify this situation by creating a number of guides and resources for the various subject GREs. This has included writing my own textbook on the Biology GRE, a set of notes for the Physics GREs (still in progress), and creating answer guides to a range of GRE practice tests.

GRE Biology:

• GRE Biology Guide (textbook)
• GRE Biology Practice Test Questions
• GRE Biology Practice Test Answers

GRE Physics:

• GRE Physics Guide (textbook)
• GRE Physics Practice Test Questions
• GRE Physics Practice Test Answers

GRE Chemistry:

• GRE Chemistry Practice Test Questions
• GRE Chemistry Practice Test Answers

GRE Mathematics:

• GRE Mathematics Practice Test Questions
• GRE Mathematics Practice Test Answers

GRE Psychology:

• GRE Psychology Practice Test Questions
• GRE Psychology Practice Test Answers

Please email me at sgmoore@alumni.princeton.edu if you have any comments or questions, find any errors, believe that the notes can be improved in some way, or if you have a request. I hope to write a guide for the Psychology GRE next.

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