# Exploring first-order phase transitions with population annealing

## TL;DR

This paper investigates the effectiveness of population annealing, a hybrid Monte Carlo method, in studying first-order phase transitions, specifically in the two-dimensional Potts model with q > 4.

## Contribution

It provides preliminary insights into using population annealing to analyze complex first-order phase transitions in systems with challenging free-energy landscapes.

## Key findings

- Population annealing can potentially handle first-order transitions more efficiently.
- Preliminary results on the 2D Potts model with q > 4 show promise.
- Further research is needed to confirm effectiveness.

## Abstract

Population annealing is a hybrid of sequential and Markov chain Monte Carlo methods geared towards the efficient parallel simulation of systems with complex free-energy landscapes. Systems with first-order phase transitions are among the problems in computational physics that are difficult to tackle with standard methods such as local-update simulations in the canonical ensemble, for example with the Metropolis algorithm. It is hence interesting to see whether such transitions can be more easily studied using population annealing. We report here our preliminary observations from population annealing runs for the two-dimensional Potts model with $q > 4$, where it undergoes a first-order transition.