Hey Dev Community! I am so excited to share a major pivot in my research journey. While my previous work (2025–2026) was all about the digital world—specifically detecting misinformation in high-variance network environments—I recently started asking myself a big question: Is the extraction of truth from noise a universal mathematical challenge?

To find out, I looked away from the screen and up at the stars! 🌌

I’ve just released my latest project, AstroNet-Lite, a dual-path Convolutional Neural Network designed to find exoplanets in the chaotic, high-noise data from NASA’s TESS satellite.

What Makes This Research Different?
If you've followed my "Edge NLP" work, you know I’m obsessed with lightweight, hardware-aware optimization. This time, I took those same principles and applied them to Astrophysics. Here is how this phase of my work stands out:

A New Kind of "Noise": Instead of network variance, I’m now battling photon noise, stellar variability, and instrument-induced trends that mask the tiny 1% dip in light caused by an exoplanet.

The Dual-Scale Architecture: Unlike monolithic classifiers, AstroNet-Lite uses two distinct convolutional paths to decouple spatial features:

The Global View: Uses large kernels (size 7) to understand the "Star" and its natural cycles.

The Local View: Uses small kernels (size 3) to "Zoom" in on the "Planet," identifying the sharp entry (ingress) and exit (egress) points of a transit.

Extreme Efficiency: I’m proving that "Big Science" doesn't need "Big Compute". This model is only 312 KB—smaller than a single high-resolution photograph!

Real-World Validation: I successfully navigated the "Sim-to-Real" gap. After training on synthetic physics-based data, I used Savitzky-Golay detrending to identify actual confirmed planets in NASA’s archives, like TOI-700 d and WASP-126 b.

Why This Matters 🌍
Operating within the bandwidth constraints of rural Tripura, I wanted to show that the search for another Earth isn't just for government agencies with supercomputers. By democratizing these tools, any student with a curious mind and an efficient algorithm can join the frontier of discovery.

Whether it's a deceptive claim in a digital feed or a planet orbiting a distant sun, the engineering challenge is the same: distinguishing the signal from the noise.

Special Thanks & Resources
A huge thank you to the open-science community and the NASA TESS archive for making this data accessible to independent researchers everywhere. This journey from NLP to Astrophysics has been a whirlwind, and I'm so grateful for the support!

Read the New Paper:

AstroNet-Lite: A Dual-Scale Convolutional Framework for Automated Exoplanet Discovery (https://doi.org/10.5281/zenodo.18405183)

Check Out My Previous Works:

Democratizing Truth: Optimizing Transformer Models for Client-Side Misinformation Detection (https://zenodo.org/records/17879430)

Neural Network Quantization for Edge Deployment — Field Validation (https://doi.org/10.5281/zenodo.18140944)

I'd love to hear your thoughts!