Adrita Khan
Greetings, everyone! I am Adrita (আদৃতা) from Bangladesh. I will be joining the University of Bonn as an incoming M.Sc. in Astrophysics student (Winter 2026), supported by the Bonn-Cologne Graduate School of Physics and Astronomy (BCGS) Scholarship. I completed my B.Sc. (Hons.) at the Universiti Sains Malaysia, majoring in Physics and minoring in Astronomy. Before beginning my studies at Bonn, I am completing an M.Sc. in Applied Mathematics and Computational Science at North South University (Dec 2024 – Sep 2026), where my thesis focuses on missing data imputation for irregular astronomical light curves.
My research interests lie in Theoretical and Observational Cosmology, Large-Scale Structure, Dark Matter and Dark Energy, Modified Gravity, Cosmic Inflation, High-Energy, Multimessenger and Astroparticle Physics, and Astrostatistics and Machine Learning. My current research focuses on modified gravity theories through cross-correlation analyses of the Cosmic Microwave Background (CMB) and galaxy data.
My long-term research goal is to advance precision cosmology at the intersection of theory and observation, particularly using data from next-generation surveys such as the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) Dark Energy Science Collaboration (DESC), the Euclid mission, the Dark Energy Spectroscopic Instrument (DESI), the Simons Observatory, CMB-S4, and the Atacama Cosmology Telescope (ACT). I use computational approaches including machine learning, Bayesian inference, and cosmological emulation to extract constraints on the universe's origin, evolution, and matter–energy composition. I am committed to fostering diversity, equity, and inclusion within the academic community.
Education
Master of Science in Astrophysics
Rheinische Friedrich‑Wilhelms‑Universität Bonn
Bonn, Germany
Incoming · Winter 2026
Master of Science in Applied Mathematics and Computational Science
North South University
Department of Mathematics & Physics, Dhaka, Bangladesh • Dec 2024 – Sep 2026
Coursework spanning numerical methods, PDE modelling, computational linear algebra, CFD, parallel computing, and machine learning for data science.
Bachelor of Science (Honours) in Physics
Universiti Sains Malaysia
School of Physics, Penang, Malaysia • Graduated 2023
Developed comprehensive expertise in quantum mechanics, astrophysics, mathematical physics, and computational methods. Specialized in astronomy with focus on astrophysical phenomena and observational techniques.
Featured Research
Testing Modified Gravity Theories with Cross-Correlation
This project calculates and analyzes galaxy–galaxy power spectra (Cℓgg) and galaxy–CMB lensing cross-power spectra (Cℓκg) using models including nDGP, e-mantis, and Bacco emulators. The goal is to forecast the ability of surveys like LSST/DESC to constrain modified gravity theories such as f(R) and DGP through cross-correlation techniques. This approach breaks parameter degeneracies and reduces systematic errors by combining uncorrelated datasets. The analysis examines whether observed power spectra contain signatures of modified gravity that differ from standard ΛCDM predictions. Such analyses help constrain alternative theories of gravity on cosmological scales.
View ProjectPublications
E-PCN: Jet Tagging with Explainable Particle Chebyshev Networks Using Kinematic Features
† These authors contributed equally to this work. ∗ Corresponding author
Preprint
arXiv:2512.07420 | December 2025
We introduce the Explainable Particle Chebyshev Network (E-PCN), a graph neural network extending the Particle Chebyshev Network (PCN) for interpretable jet classification in high-energy collider experiments. E-PCN integrates kinematic variables by constructing four graph representations per jet, each weighted by a distinct variable: angular separation (Δ), transverse momentum (kt), momentum fraction (z), and invariant mass squared (m²). Using Gradient-weighted Class Activation Mapping (Grad-CAM), we determine which kinematic variables dominate classification outcomes.
@misc{islam2025epcn,
title = {{E-PCN}: Jet Tagging with Explainable Particle
{Chebyshev} Networks Using Kinematic Features},
author = {Islam, Md Raqibul and Khan, Adrita and
Hossain, Mir Sazzat and Siddiqui, Choudhury
Ben Yamin and Hossan, Md.\ Zakir and
Khan, Tanjib and Momen, M.\ Arshad and
Ali, Amin Ahsan and Rahman, AKM Mahbubur},
year = {2025},
eprint = {2512.07420},
archivePrefix = {arXiv},
primaryClass = {hep-ph},
url = {https://arxiv.org/abs/2512.07420}
}
RGC: A Radio AGN Classifier Based on Deep Learning. I. A Semi-Supervised Model for the VLA Images of Bent Radio AGNs
Preprint
arXiv:2510.22190 | October 2025
We present RGC (Radio Galaxy Classifier), a deep learning-based semi-supervised model designed for classifying bent radio active galactic nuclei (AGN) from Very Large Array (VLA) images. This work represents an important step toward automated classification of radio AGN, enabling more efficient processing of large-scale radio surveys.
@misc{hossain2025rgc,
title = {{RGC}: A Radio {AGN} Classifier Based on Deep
Learning. {I}. A Semi-Supervised Model for the
{VLA} Images of Bent Radio {AGN}s},
author = {Hossain, M.S. and Shahal, M.S.H. and
Khan, A. and Asad, K.M.B. and Saikia, P. and
Akter, F. and Ali, A. and Amin, M.A. and
Momen, A. and Hasan, M. and Rahman, A.K.M.M.},
year = {2025},
eprint = {2510.22190},
archivePrefix = {arXiv},
primaryClass = {astro-ph.GA},
url = {https://arxiv.org/abs/2510.22190}
}
RGC-Bent: A Novel Dataset for Bent Radio Galaxy Classification
2025 IEEE International Conference on Image Processing (ICIP 2025)
arXiv:2505.19249 | May 2025
We introduce RGC-Bent, a curated dataset of 639 radio galaxy images designed to support machine learning-based classification of bent radio AGN. ConvNeXT achieves the highest F1-scores for both NAT and WAT sources, demonstrating the effectiveness of advanced machine learning models in classifying bent radio AGN.
@inproceedings{hossain2025rgcbent,
title = {{RGC-Bent}: A Novel Dataset for Bent Radio
Galaxy Classification},
author = {Hossain, Mir Sazzat and Asad, Khan Muhammad Bin
and Saikia, Payaswini and Khan, Adrita and
Iftee, Md Akil Raihan and Rajib, Rakibul Hasan
and Momen, Arshad and Amin, Md Ashraful and
Ali, Amin Ahsan and Rahman, AKM Mahbubur},
booktitle = {2025 IEEE International Conference on Image
Processing (ICIP)},
year = {2025},
eprint = {2505.19249},
archivePrefix = {arXiv},
primaryClass = {astro-ph.IM},
doi = {10.1109/ICIP57928.2025.11084387},
url = {https://arxiv.org/abs/2505.19249}
}
Research and Teaching Affiliations
News
Technical Proficiency
Python • C/C++ • Mathematica • MATLAB • R • SQL • Bash/Shell scripting
NumPy • SciPy • Pandas • scikit-learn • Astropy • Astroquery • PyTorch • TensorFlow
CAMB • CCL • HEALPix • NaMaster • emcee • pocoMC • corner • GetDist • TOPCAT • SAOImage DS9
CosmoPower • bacco • e-mantis • nDGP
MCMC • Bayesian inference • Fisher forecasting • ML/DL frameworks • Feature engineering • Hyperparameter optimization
Linux/Unix • Git/GitHub • Docker • LaTeX • HPC clusters & parallelization
Research Interests
- Observational and Theoretical Cosmology
- Large-Scale Structure (LSS)
- Dark Matter and Dark Energy
- Modified Gravity Models
- Precision Cosmology
- Universe's Origin and Evolution
- Cosmological Simulations and Modeling
- Stellar Formation and Evolution
- Stellar Properties
- Astro-Particle and High Energy Physics
- Particle Phenomenology
- Multimessenger Astronomy
- Astrostatistics and Machine Learning
- Computational Methods in Nuclear Physics