Publications

Blazars emit a highly variable non-thermal spectrum. It is usually assumed that the same non-thermal electrons are responsible for the IR-optical-UV emission (via synchrotron) and the gamma-ray emission (via inverse Compton). Hence, the light curves in the two bands should be correlated. Orphan gamma-ray flares (i.e. lacking a luminous low-frequency counterpart) challenge our theoretical understanding of blazars. By means of large-scale two-dimensional radiative particle-in-cell simulations, we show that orphan gamma-ray flares may be a self-consistent by-product of particle energization in turbulent magnetically dominated pair plasmas. The energized particles produce the gamma-ray flare by inverse Compton scattering an external radiation field, while the synchrotron luminosity is heavily suppressed since the particles are accelerated nearly along the direction of the local magnetic field. The ratio of inverse Compton to synchrotron luminosity is sensitive to the initial strength of turbulent fluctuations (a larger degree of turbulent fluctuations weakens the anisotropy of the energized particles, thus increasing the synchrotron luminosity). Our results show that the anisotropy of the non-thermal particle population is key to modelling the blazar emission.

Stars with peculiar element abundances are important markers of chemical enrichment mechanisms. We present a simple method, tangent space projection (TSP), for the detection of X-enriched stars, for arbitrary elements X, even from blended lines. Our method does not require stellar labels, but instead directly estimates the counterfactual unrenriched spectrum from other unlabelled spectra. As a case study, we apply this method to the 6708 Å Li doublet in LAMOST DR5, identifying 8,428 Li-enriched stars seamlessly across evolutionary state. We comment on the explanation for Li-enrichement for different subpopulations, including planet accretion, nonstandard mixing, and youth.

Lyman-α (Lyα) emitting galaxies are powerful tools to probe the late stages of cosmic reionization. The observed sudden drop in Lyα fraction at z>6 is often interpreted as a sign of reionization, since the intergalactic medium (IGM) is more neutral and opaque to Lyα photons. Crucially, this interpretation of the observations is only valid under the assumption that galaxies themselves experience a minimal evolution at these epochs. By modelling Lyα radiative transfer effects in and around galaxies, we examine whether a change in the galactic properties can reproduce the observed drop in the Lyα fraction. We find that an increase in the galactic neutral hydrogen content or a reduction in the outflow velocity toward higher redshift both lead to a lower Lyα escape fraction, and can thus mimic an increasing neutral fraction of the IGM. We furthermore find that this change in galactic properties leads to systematically different Lyα spectra which can be used to differentiate the two competing effects. Using the CANDELSz7 survey measurements which indicate slightly broader lines at z∼6, we find that the scenario of a mere increase in the galactic column density towards higher z is highly unlikely. We also show that a decrease in outflow velocity is not ruled out by existing data but leads to more prominent blue peaks at z>6. Our results caution the use of Lyα observations to estimate the IGM neutral fraction without accounting for the potential change in the galactic properties, e.g., by mapping out the evolution of Lyα spectral characteristics.

We present the checkpointing scheme of Abacus, an N-body simulation code that allocates all persistent state in POSIX shared memory, or ramdisk. Checkpointing becomes as simple as copying files from ramdisk to external storage. The main simulation executable is invoked once per time step, memory mapping the input state, computing the output state directly into ramdisk, and unmapping the input state. The main executable remains unaware of the concept of checkpointing, with the top-level driver code launching a file-system copy between executable invocations when a checkpoint is needed. Since the only information flow is through files on ramdisk, the checkpoint must be correct so long as the simulation is correct. However, we find that with multi-GB of state, there is a significant overhead to unmapping the shared memory. This can be partially mitigated with multithreading, but ultimately, we do not recommend shared memory for use with a large state.

Statistical analysis of microbial genomic data within epidemiological cohort studies holds the promise to assess the influence of environmental exposures on both the host and the host-associated microbiome. The observational character of prospective cohort data and the intricate characteristics of microbiome data make it, however, challenging to discover causal associations between environment and microbiome. Here, we introduce a causal inference framework based on the Rubin Causal Model that can help scientists to investigate such environment-host microbiome relationships, to capitalize on existing, possibly powerful, test statistics, and test plausible sharp null hypotheses. Using data from the German KORA cohort study, we illustrate our framework by designing two hypothetical randomized experiments with interventions of (i) air pollution reduction and (ii) smoking prevention. We study the effects of these interventions on the human gut microbiome by testing shifts in microbial diversity, changes in individual microbial abundances, and microbial network wiring between groups of matched subjects via randomization-based inference. In the smoking prevention scenario, we identify a small interconnected group of taxa worth further scrutiny, including Christensenellaceae and Ruminococcaceae genera, that have been previously associated with blood metabolite changes. These findings demonstrate that our framework may uncover potentially causal links between environmental exposure and the gut microbiome from observational data. We anticipate the present statistical framework to be a good starting point for further discoveries on the role of the gut microbiome in environmental health.

The radio emission anomaly coincident with the 2016 glitch of the Vela pulsar may be caused by a star quake that launches Alfvén waves into the magnetosphere, disturbing the original radio emitting region. To quantify the lifetime of the Alfvén waves, we investigate a possible energy loss mechanism, the conversion of Alfvénwaves into fast magnetosonic waves. Using axisymmetric force-free simulations, we follow the propagation of Alfvén waves launched from the stellar surface with small amplitude into the closed zone of a force-free dipolar pulsar magnetosphere. We observe mode conversion happening in the ideal force-free regime. The conversion efficiency during the first passage of the Alfvén wave through the equator can be large, for waves that reach large amplitudes as they travel away from the star, or propagate on the field lines passing close to the Y-point. However, the conversion efficiency is reduced due to dephasing on subsequent passages and considerable Alfvén power on the closed field lines remains. Thus while some leakage into the fast mode happens, we need detailed understanding of the original quenching in order to say whether mode conversion alone can lead to reactivation of the pulsar on a short timescale.

Chromosome segregation—the partitioning of genetic material into two daughter cells—is one of the most crucial processes in cell division. In all Eukaryotes, chromosome segregation is driven by the spindle, a microtubule-based, self-organizing subcellular structure. Extensive research performed over the past 150 years has identified numerous commonalities and contrasts between spindles in different systems. In this review, we use simple coarse-grained models to organize and integrate previous studies of chromosome segregation. We discuss sites of force generation in spindles and fundamental mechanical principles that any understanding of chromosome segregation must be based upon. We argue that conserved sites of force generation may interact differently in different spindles, leading to distinct mechanical mechanisms of chromosome segregation. We suggest experiments to determine which mechanical mechanism is operative in a particular spindle under study. Finally, we propose that combining biophysical experiments, coarse-grained theories, and evolutionary genetics will be a productive approach to enhance our understanding of chromosome segregation in the future.

Chromosome segregation—the partitioning of genetic material into two daughter cells—is one of the most crucial processes in cell division. In all Eukaryotes, chromosome segregation is driven by the spindle, a microtubule-based, self-organizing subcellular structure. Extensive research performed over the past 150 years has identified numerous commonalities and contrasts between spindles in different systems. In this review, we use simple coarse-grained models to organize and integrate previous studies of chromosome segregation. We discuss sites of force generation in spindles and fundamental mechanical principles that any understanding of chromosome segregation must be based upon. We argue that conserved sites of force generation may interact differently in different spindles, leading to distinct mechanical mechanisms of chromosome segregation. We suggest experiments to determine which mechanical mechanism is operative in a particular spindle under study. Finally, we propose that combining biophysical experiments, coarse-grained theories, and evolutionary genetics will be a productive approach to enhance our understanding of chromosome segregation in the future

To exploit the power of next-generation large-scale structure surveys, ensembles of numerical simulations are necessary to give accurate theoretical predictions of the statistics of observables. High-fidelity simulations come at a towering computational cost. Therefore, approximate but fast simulations, surrogates, are widely used to gain speed at the price of introducing model error. We propose a general method that exploits the correlation between simulations and surrogates to compute fast, reduced-variance statistics of large-scale structure observables without model error at the cost of only a few simulations. We call this approach Convergence Acceleration by Regression and Pooling (CARPool). In numerical experiments with intentionally minimal tuning, we apply CARPool to a handful of GADGET-III N-body simulations paired with surrogates computed using COmoving Lagrangian Acceleration (COLA). We find ∼100-fold variance reduction even in the non-linear regime, up to kmax≈1.2 hMpc−1 for the matter power spectrum. CARPool realises similar improvements for the matter bispectrum. In the nearly linear regime CARPool attains far larger sample variance reductions. By comparing to the 15,000 simulations from the Quijote suite, we verify that the CARPool estimates are unbiased, as guaranteed by construction, even though the surrogate misses the simulation truth by up to 60% at high k. Furthermore, even with a fully configuration-space statistic like the non-linear matter density probability density function, CARPool achieves unbiased variance reduction factors of up to ∼10, without any further tuning. Conversely, CARPool can be used to remove model error from ensembles of fast surrogates by combining them with a few high-accuracy simulations.

Evaluating microbial community composition through next-generation sequencing has become increasingly accessible. However, metagenomic sequencing data sets provide researchers with only a snapshot of a dynamic ecosystem and do not provide information about the total microbial number, or load, of a sample. Additionally, DNA can be detected long after a microorganism is dead, making it unsafe to assume that all microbial sequences detected in a community came from living organisms. By combining relic DNA removal by propidium monoazide (PMA) with microbial quantification with flow cytometry, we present a novel workflow to quantify live microbial load in parallel with metagenomic sequencing. We applied this method to unstimulated saliva samples, which can easily be collected longitudinally and standardized by passive collection time. We found that the number of live microorganisms detected in saliva was inversely correlated with salivary flow rate and fluctuated by an order of magnitude throughout the day in healthy individuals. In an acute perturbation experiment, alcohol-free mouthwash resulted in a massive decrease in live bacteria, which would have been missed if we did not consider dead cell signal. While removing relic DNA from saliva samples did not greatly impact the microbial composition, it did increase our resolution among samples collected over time. These results provide novel insight into the dynamic nature of host-associated microbiomes and underline the importance of applying scale-invariant tools in the analysis of next-generation sequencing data sets.