Publish or perish

April was an unusually busy month for me for proof-checking, with five papers published:

Moiré Lattice in One-Dimensional Synthetic Frequency Dimension

This work led by collaborators at Shanghai Jiao Tong University analyzes frequency domain synthetic photonic lattices from a transfer matrix perspective, finding that the interplay between an incommensurate time modulation and off-resonant ring modes (not captured by the usual tight binding approximation) can be used to generate flat bands in a simple system of two coupled ring resonators.

Band relaxation triggered by modulational instability in topological photonic lattices

This was a long-delayed follow-up to our work on modulational instability in topological photonic lattices which we initiated during the start of the covid pandemic. Initially we had tried to understand the nonlinear wave dynamics in terms of an effective thermalization process, but it turned out that we could not observe any genuine thermalization within an experimentally-feasible time scale. This paper presents a detailed characterization of the long-lived pre-thermal state that is generated by the modulational instability. While our studies in this area are entirely theoretical / numerical, the dynamics of complex multimode nonlinear optical systems are now starting to be studied in a variety of experimental platforms, reviewed in Nature Physics last year.

Unravelling quantum chaos using persistent homology and Pseudospin-2 in photonic chiral borophene

I posted about these two papers when they were posted to arXiv late last year. The former made it into Physical Review E after a round of revisions. The latter was rejected by Nature Communications before being resubmitted to Photonics Research and accepted for publication after one round of revisions.

Topological data analysis and machine learning

This was a challenging review to prepare, given the need to concisely capture both the surprisingly-long history of applications of topological data analysis to physics (from the early 2000s) and a more recent wave of papers combining TDA with machine learning techniques. While it is far from perfect I hope it can still be a useful anchor for ongoing research in this area.

I'm hoping to have the next set of (now overdue) drafts finished and on arXiv sometime in June. Watch this space!