Python | J. Andrew Casey-Clyde

Quasars can Signpost Supermassive Black Hole Binaries

Wed, 29 May 2024 00:00:00 +0000

🚀 Using Quasars to Uncover Black Hole Pairs 🌌

As the lead on this project, I explored how quasars—exceptionally bright objects powered by black holes—can help identify pairs of merging supermassive black holes. By integrating data from gravitational waves, quasar activity patterns, and galaxy observations, I developed a framework to estimate how often quasars host these black hole pairs.

Key Outcomes:

Found that quasars are up to seven times more likely than other galaxies to host black hole pairs, highlighting them as priority targets for future studies.
Improved regression analysis efficiency by 300x, enabling faster and more scalable data analysis.

Why It Matters for Industry: This project demonstrates my ability to combine diverse datasets, optimize computational performance, and uncover insights in complex systems—skills directly relevant to data science, machine learning, and analytics roles.

Let’s connect to discuss how these approaches can solve challenges in your industry! 🌟

Learn Python

Tue, 24 Oct 2023 00:00:00 +0000

Hugo Blox Builder is designed to give technical content creators a seamless experience. You can focus on the content and the Hugo Blox Builder which this template is built upon handles the rest.

Embed videos, podcasts, code, LaTeX math, and even test students!

On this page, you’ll find some examples of the types of technical content that can be rendered with Hugo Blox.

Video

Teach your course by sharing videos with your students. Choose from one of the following approaches:

Youtube:

{{< youtube w7Ft2ymGmfc >}}

Bilibili:

{{< bilibili id="BV1WV4y1r7DF" >}}

Video file

Videos may be added to a page by either placing them in your assets/media/ media library or in your page’s folder, and then embedding them with the video shortcode:

{{< video src="my_video.mp4" controls="yes" >}}

Podcast

You can add a podcast or music to a page by placing the MP3 file in the page’s folder or the media library folder and then embedding the audio on your page with the audio shortcode:

{{< audio src="ambient-piano.mp3" >}}

Try it out:

Test students

Provide a simple yet fun self-assessment by revealing the solutions to challenges with the spoiler shortcode:

{{< spoiler text="👉 Click to view the solution" >}}
You found me!
{{< /spoiler >}}

renders as

👉 Click to view the solution

You found me 🎉

Math

Hugo Blox Builder supports a Markdown extension for $\LaTeX$ math. You can enable this feature by toggling the math option in your config/_default/params.yaml file.

To render inline or block math, wrap your LaTeX math with {{< math >}}$...${{< /math >}} or {{< math >}}$$...$${{< /math >}}, respectively.

We wrap the LaTeX math in the Hugo Blox math shortcode to prevent Hugo rendering our math as Markdown.

Example math block:

{{< math >}}
$$
\gamma_{n} = \frac{ \left | \left (\mathbf x_{n} - \mathbf x_{n-1} \right )^T \left [\nabla F (\mathbf x_{n}) - \nabla F (\mathbf x_{n-1}) \right ] \right |}{\left \|\nabla F(\mathbf{x}_{n}) - \nabla F(\mathbf{x}_{n-1}) \right \|^2}
$$
{{< /math >}}

renders as

$$\gamma_{n} = \frac{ \left | \left (\mathbf x_{n} - \mathbf x_{n-1} \right )^T \left [\nabla F (\mathbf x_{n}) - \nabla F (\mathbf x_{n-1}) \right ] \right |}{\left \|\nabla F(\mathbf{x}_{n}) - \nabla F(\mathbf{x}_{n-1}) \right \|^2}$$

Example inline math {{< math >}}$\nabla F(\mathbf{x}_{n})${{< /math >}} renders as $\nabla F(\mathbf{x}_{n})$ .

Example multi-line math using the math linebreak (\\):

{{< math >}}
$$f(k;p_{0}^{*}) = \begin{cases}p_{0}^{*} & \text{if }k=1, \\
1-p_{0}^{*} & \text{if }k=0.\end{cases}$$
{{< /math >}}

renders as

$$ f(k;p_{0}^{*}) = \begin{cases}p_{0}^{*} & \text{if }k=1, \\ 1-p_{0}^{*} & \text{if }k=0.\end{cases} $$

Code

Hugo Blox Builder utilises Hugo’s Markdown extension for highlighting code syntax. The code theme can be selected in the config/_default/params.yaml file.

```python
import pandas as pd
data = pd.read_csv("data.csv")
data.head()
```

renders as

import pandas as pd
data = pd.read_csv("data.csv")
data.head()

Inline Images

{{< icon name="python" >}} Python

renders as

Python

The NANOGrav 15 yr Data Set: Looking for Signs of Discreteness in the Gravitational-wave Background

Thu, 01 Aug 2019 00:00:00 +0000

🚀 Unlocking the Secrets of Gravitational Waves 🌌

As project lead for a collaboration of over 100 scientists, I investigated how patterns in gravitational waves—the ripples in space-time caused by merging supermassive black holes—can reveal new insights about the Universe. Using data from the NANOGrav 15-year dataset, I built models to detect unexpected signals and studied how individual black hole pairs contribute to the larger wave background.

📊 Key Outcomes:

Identified subtle deviations in wave patterns, possibly linked to individual black hole mergers.
Discovered a frequency range where signals from individual events or early-Universe phenomena could emerge.
Published our findings in a top-tier journal, highlighting their significance to the field.

💡 Relevance for Industry: This project showcases my ability to transform complex, noisy datasets into meaningful insights—skills directly applicable to AI, machine learning, and predictive modeling challenges in industry.

Let’s connect to explore how advanced data science can drive innovation in your field! 🌟

Galaxy Classification with Neural Networks in the Sloan Digital Sky Survey

Fri, 26 Apr 2019 00:00:00 +0000

🚀 Using Neural Networks to Classify Galaxies 🌌

I led the development and analysis of a project that applied machine learning techniques to classify galaxy shapes using imaging data from the Sloan Digital Sky Survey (SDSS). This work explored how convolutional neural networks (CNNs) can handle the rapidly growing datasets in astronomy, enabling scalable and efficient classification of galaxy morphologies.

Key Outcomes:

Built and trained a CNN on over 300,000 galaxy images.
Demonstrated the potential of neural networks for large-scale image classification tasks, even with limited training data.
Identified avenues for improvement, including expanding datasets, optimizing model configurations, and parallelizing computations for scalability.

Why It Matters for Industry: This project highlights my experience in developing machine learning pipelines, optimizing performance, and handling large datasets—skills directly applicable to data science, machine learning, and research-driven innovation.

Let’s connect to discuss how these techniques can create value in your organization! 🌟

Python | J. Andrew Casey-Clyde

Quasars can Signpost Supermassive Black Hole Binaries

Learn Python

Video

Podcast

Test students

Math

Code

Inline Images

Did you find this page helpful? Consider sharing it 🙌

The NANOGrav 15 yr Data Set: Looking for Signs of Discreteness in the Gravitational-wave Background

Galaxy Classification with Neural Networks in the Sloan Digital Sky Survey