Darshan Patil
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kashish shrivastav
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Mohammad Adnan
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mohyware
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Pratham
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Wow! I still can't believe I'm actually in Google Summer of Code! This was a dream, and now here I am, officially past the Community Bonding Period and diving headfirst into the Coding Phase.
What’s Happening Right Now?
Right now, I’m sketching out the basic structure of the application interface. The first big task? Fetching event files from specific sources. Sounds simple, right? Well… not quite
I’m very excited to begin this journey. Since childhood, I’ve been deeply fascinated by astronomy, and today, I finally get to express that passion through writing and sharing my learning experiences with such a great community.
This week started off on a truly inspiring note. On May 23rd, I had the opportunity to meet my mentors—individuals with deep expertise and experience in the field. At first, I was a bit nervous and hesitant. But the moment the session began, all my fear started fading away.
We began with short introductions and then quickly moved on to discussing plans for the upcoming work. I had a few doubts—some of which I felt were too basic or weird to ask—but I’m so glad I did! My mentors answered them with such clarity and patience. They even gave me valuable suggestions for improving one of my current functions (called recpiec) and helped us finalize a workflow to proceed with.
Later in the week, I spent time diving deep into articles to strengthen my understanding. One article that particularly stood out was authored by one of my mentors:
This article taught me about the problem of dead time in time-series data, especially when analyzing periodic signals using periodograms. Dead time refers to gaps or inactive intervals in data collection, which can significantly distort frequency analysis. The article introduced a technique using Fourier Amplitude Differences to correctly normalize these periodograms, ensuring the results remain accurate and meaningful. This method was new to me and absolutely fascinating!
Along with that, I also explored Bartlett's method—a signal processing technique used to reduce the variance of power spectral density estimates. It involves dividing the signal into non-overlapping segments, computing a periodogram for each, and averaging them. This approach provides a smoother, more reliable estimate of the spectrum, which is especially useful in noisy datasets (which are quite common in astronomy!).
Inspired by this, I also implemented the Hann window in my code to taper the signal before applying spectral analysis. This windowing technique helps minimize spectral leakage by reducing the discontinuities at the edges of the signal. I was surprised to see how much of a difference it made in the clarity of results!
In this post, I'll be discussing my GSoC'24 project, the goals set, work done, and future scope
As the sun sets on the Google Summer of Code 2024, it's time to reflect on our exploration of Active Galactic Nuclei (AGN) light curve interpolation using advanced neural networks. Over the course of this project, we ventured into the complexities of AGN data, developing and refining models to better predict and understand the erratic behaviors of these celestial objects.
Overview of the Project
Our journey began with the goal of enhancing the accuracy of AGN light curve predictions. We employed custom Bidirectional Recurrent Neural Networks (BRNNs), coupled with an interpretative neural network layer, aiming to leverage both past and future context in our predictions.
Final Results
In our last phase, we meticulously tested our BRNN model against traditional linear interpolation and K-Nearest Neighbors (KNN) methods:
Damn it was an eventful 3-4 months, from crying to why the code is not working to enjoying the small success of getting the plots. I am grateful that I got the opportunity to be in GSOC and get to know the wonderful mentors Matteo and Gullo. Thank you for guiding me. Let's see what the final term evaluation beholds for me..Fingers crossed.
Also regardless of how the evaluation turns out to be, I am gonna continue contributing to the project as much as I can.
RADIS describes itself as ‘a fast line-by-line code for high resolution infrared molecular spectra’. My project focussed on adding support for atomic line databases to RADIS, which has up till now catered only for molecular databases. Atomic lines differ significantly from molecular lines in how they are affected by Lorentzian broadening and how non-equilibrium spectra are handled.
The main goal was adding support for the Kurucz atomic database, which is now complete. This laid the basic structure for adding new atomic databases, and a PR is now open for adding NIST.