Publications

Recent modeling of Neutron Star Interior Composition Explorer(NICER) observations of the millisecond pulsar PSR J0030+0451 suggests that the magnetic field of the pulsar is non-dipolar. We construct a magnetic field configuration where foot points of the open field lines closely resemble the hotspot configuration from NICER observations. Using this magnetic field as input, we perform force-free simulations of the magnetosphere of PSR J0030+0451, showing the three-dimensional structure of its plasma-filled magnetosphere. Making simple and physically motivated assumptions about the emitting regions, we are able to construct the multi-wavelength lightcurves that qualitatively agree with the corresponding observations. The agreement suggests that multipole magnetic structures are the key to modeling this type of pulsars, and can be used to constrain the magnetic inclination angle and the location of radio emission.

We present results from offline searches of Fermi Gamma-ray Burst Monitor (GBM) data for gamma-ray transients coincident with the compact binary coalescences observed by the gravitational-wave (GW) detectors Advanced LIGO and Advanced Virgo during their first and second observing runs. In particular, we perform follow-up for both confirmed events and low significance candidates reported in the LIGO/Virgo catalog GWTC-1. We search for temporal coincidences between these GW signals and GBM triggered gamma-ray bursts (GRBs). We also use the GBM Untargeted and Targeted subthreshold searches to find coincident gamma-rays below the on-board triggering threshold. This work implements a refined statistical approach by incorporating GW astrophysical source probabilities and GBM visibilities of LIGO/Virgo sky localizations to search for cumulative signatures of coincident subthreshold gamma-rays. All search methods recover the short gamma-ray burst GRB 170817A occurring ~1.7 s after the binary neutron star merger GW170817. We also present results from a new search seeking GBM counterparts to LIGO single-interferometer triggers. This search finds a candidate joint event, but given the nature of the GBM signal and localization, as well as the high joint false alarm rate of 1.1×10−6 Hz, we do not consider it an astrophysical association. We find no additional coincidences.

Nonsymmorphic symmetries protect Dirac line nodes in square-net materials. This phenomenon has been most prominently observed in ZrSiS. Here, we systematically study the symmetry-protected nodal fermions that result from different ways of embedding the square net into a larger unit cell. Surprisingly, we find that a nonsymmorphic space group is not a necessary condition for a filling enforced semimetal: symmorphic space groups can also host nodal fermions that are enforced by band folding and electron count, that is, a combination of a particular structural motif combined with electron filling. We apply the results of this symmetry analysis to define an algorithm, which we utilize to find square-net materials with nodal fermions in specific symmorphic space groups. We highlight one result of this search, the compound ThGeSe, which we discuss in the context of nodal fermions. Finally, we discuss how band folding can impose constraints on band connectivity beyond the connectivity of single elementary band representations.

The interaction of strong laser fields with matter intrinsically provides powerful tools to image transient dynamics with an extremely high spatiotemporal resolution. Here, we study strong-field ionisation of laser-aligned molecules and show a full real-time picture of the photoelectron dynamics in the combined action of the laser field and the molecular interaction. We demonstrate that the molecule has a dramatic impact on the overall strong-field dynamics: it sets the clock for the emission of electrons with a given rescattering kinetic energy. This result represents a benchmark for the seminal statements of molecular-frame strong-field physics and has strong impact on the interpretation of self-diffraction experiments. Furthermore, the resulting encoding of the time-energy relation in molecular-frame photoelectron momentum distributions shows the way of probing the molecular potential in real-time and accessing a deeper understanding of electron transport during strong-field interactions.

We present a beyond-mean-field approach to predict the nature of organic polariton lasing, accounting for all relevant photon modes in a planar microcavity. Starting from a microscopic picture, we show how lasing can switch between polaritonic states resonant with the maximal gain, and those at the bottom of the polariton dispersion. We show how the population of non-lasing modes can be found, and by using two-time correlations, we show how the photoluminescence spectrum (of both lasing and non-lasing modes) evolves with pumping and coupling strength, confirming recent experimental work on the origin of blueshift for polariton lasing.

Previous work on symmetric group equivariant neural networks generally only considered the case where the group acts by permuting the elements of a single vector. In this paper we derive formulae for general permutation equivariant layers, including the case where the layer acts on matrices by permuting their rows and columns simultaneously. This case arises naturally in graph learning and relation learning applications. As a specific case of higher order permutation equivariant networks, we present a second order graph variational encoder, and show that the latent distribution of equivariant generative models must be exchangeable. We demonstrate the efficacy of this architecture on the tasks of link prediction in citation graphs and molecular graph generation.

Recently, the twist angle between adjacent sheets of stacked van der Waals materials emerged as a new knob to engineer correlated states of matter in two-dimensional heterostructures in a controlled manner, giving rise to emergent phenomena such as superconductivity or correlated insulating states. Here,we use an ab initio based approach to characterize the electronic properties of twisted bilayer MoS2. We report that, in marked contrast to twisted bilayer graphene, slightly hole-doped MoS2 realizes a strongly asymmetric px-py Hubbard model on the honeycomb lattice, with two almost entirely dispersionless bands emerging due to destructive interference. We study the collective behavior of twisted bilayer MoS2 in the presence of interactions, and characterize an array of different magnetic and orbitally-ordered correlated phases,which may be susceptible to quantum fluctuations giving rise to exotic, purely quantum, states of matter.

Consistent estimation of associations in microbial genomic survey count data is fundamental to microbiome research. Technical limitations, including compositionality, low sample sizes, and technical variability, obstruct standard application of association measures and require data normalization prior to estimating associations. Here, we investigate the interplay between data normalization and microbial association estimation by a comprehensive analysis of statistical consistency. Leveraging the large sample size of the American Gut Project (AGP), we assess the consistency of the two prominent linear association estimators, correlation and proportionality, under different sample scenarios and data normalization schemes, including RNA-seq analysis work flows and log-ratio transformations. We show that shrinkage estimation, a standard technique in high-dimensional statistics, can universally improve the quality of association estimates for microbiome data. We find that large-scale association patterns in the AGP data can be grouped into five normalization-dependent classes. Using microbial association network construction and clustering as examples of exploratory data analysis, we show that variance-stabilizing and log-ratio approaches provide for the most consistent estimation of taxonomic and structural coherence. Taken together, the findings from our reproducible analysis workflow have important implications for microbiome studies in multiple stages of analysis, particularly when only small sample sizes are available.

With biomedical research transitioning into data-rich science, machine learning provides a powerful toolkit for extracting knowledge from large-scale biological data sets. The increasing availability of comprehensive kidney omics compendia (transcriptomics, proteomics, metabolomics, and genome sequencing), as well as other data modalities such as electronic health records, digital nephropathology repositories, and radiology renal images, makes machine learning approaches increasingly essential for analyzing human kidney data sets. Here, we discuss how machine learning approaches can be applied to the study of kidney disease, with a particular focus on how they can be used for understanding the relationship between genotype and phenotype.

Hermann Bondi's 1952 paper "On spherically symmetrical accretion" is recognized as one of the foundations of accretion theory. Although Bondi later remarked that it was "not much more than an examination exercise", his mathematical analysis of spherical accretion on to a point mass has found broad use across fields of astrophysics that were embryonic or non-existent at the time of the paper's publication. In this non-technical review, I describe the motivations for Bondi's work, and briefly discuss some of the applications of Bondi accretion in high energy astrophysics, galaxy formation, and star formation.