Publications

1. Publication Title:
“In Search of an Interaction in the Dark Sector through Gaussian Process and ANN Approaches”

🧑‍🔬Authors:

• Mazaharul Adebin

• Guo-Jian Wang

• Yin-Zhe Ma 

• Supriya Pan 

Research Highlights:

• We used Gaussian Process Regression (GP) and Artificial Neural Networks (ANN), two powerful non-parametric methods to study how dark matter and dark energy might interact, without assuming fixed models. The analysis is based on real cosmic data:

  1. Cosmic Chronometers (CC):

  2. Pantheon+ Supernovae Type Ia and combined data

• Our main goal was to look for signs of interaction between dark matter and dark energy, which could give clues about how the universe evolves over time.

• We also examined how this interaction depends on the dark energy equation-of-state parameter.

Corresponding Link:

 Journal link:

https://doi.org/10.1093/mnras/staf762 

ArXiv link:

https://arxiv.org/abs/2505.04336 

2. Publication Title:
“When Dark Matter Heats Up: A Model-Independent Search for Non-Cold Behavior”

 🧑‍🔬Authors:

• Mazaharul Abedin 

• Luis A. Escamilla

• Supriya Pan

• Eleonora Di Valentino 

• Weiqiang Yang

 Research Highlights:

• In this article, we reconstruct the dark matter equation of state (DM EoS) using two distinct approaches: a non-parametric method and a model-independent approach, avoiding fixed assumptions. Observational data include Cosmic Chronometers, Pantheon+ Supernovae, and BAO measurements from DESI DR1 and DR2, providing wide coverage across cosmic time.

• The study examines whether dark matter could have a small pressure instead of being completely pressureless, as commonly assumed in standard models.

• Findings suggest that while the evidence for a dynamic DM EoS is mild, it cannot be fully ruled out, pointing to possible refinements in our understanding of dark matter.

Corresponding Link:

ArXiv link:

https://arxiv.org/abs/2505.09470