Publications

Planetary engulfment events have long been proposed as a lithium (Li) enrichment mechanism contributing to the population of Li-rich giants (A(Li) ≥ 1.5 dex). Using MESA stellar models and A(Li) abundance measurements obtained by the GALAH survey, we calculate the strength and observability of the surface Li enrichment signature produced by the engulfment of a hot Jupiter (HJ). We consider solar-metallicity stars in the mass range of 1–2 M⊙ and the Li supplied by a HJ of 1.0 MJ. We explore engulfment events that occur near the main-sequence turn-off (MSTO) and out to orbital separations of R⋆ ∼ 0.1 au = 22 R⊙. We map our results onto the Hertzsprung–Russell Diagram, revealing the statistical significance and survival time of Li enrichment. We identify the parameter space of masses and evolutionary phases where the engulfment of a HJ can lead to Li enrichment signatures at a 5σ confidence level and with meteoritic abundance strengths. The most compelling strengths and survival times of engulfment-derived Li enrichment are found among host stars of 1.4 M⊙ near the MSTO. Our calculations indicate that planetary engulfment is not a viable enrichment pathway for stars that have evolved beyond the subgiant branch. For these sources, observed Li enhancements are likely to be produced by other mechanisms, such as the Cameron–Fowler process or the accretion of material from an asymptotic giant branch companion. Our results do not account for second-order effects, such as extra mixing processes, which can further dilute Li enrichment signatures.

We introduce the AbacusHOD model and present two applications of AbacusHOD and the AbacusSummit simulations to observations. AbacusHOD is an HOD framework written in Python that is particle-based, multi-tracer, highly generalized, and highly efficient. It is designed specifically with multi-tracer/cosmology analyses for next generation large-scale structure surveys in mind, and takes advantage of the volume and precision offered by the new state-of-the-art AbacusSummit cosmological simulations. The model is also highly customizable and should be broadly applicable to any upcoming surveys and a diverse range of cosmological analyses. In this paper, we demonstrate the capabilities of the AbacusHOD framework through two example applications. The first example demonstrates the high efficiency and the large HOD extension feature set through an analysis full-shape redshift-space clustering of BOSS galaxies at intermediate to small scales (<30Mpc/h), assessing the necessity of introducing secondary galaxy biases (assembly bias). We find strong evidence for using halo environment instead of concentration to trace secondary galaxy bias, a result which also leads to a moderate reduction to the "lensing is low" tension. The second example demonstrates the multi-tracer capabilities of the AbacusHOD package through an analysis of the extended Baryon Oscillation Spectroscopic Survey (eBOSS) cross-correlation measurements between three different galaxy tracers, LRGs, ELGs, and QSOs. We expect the AbacusHOD framework, in combination with the AbacusSummit simulation suite, to play an important role in a simulation-based analysis of the up-coming Dark Energy Spectroscopic Instrument (DESI) datasets.

The periodic pulsations of stars teach us about their underlying physical process. We present a convolutional autoencoder-based pipeline as an automatic approach to search for out-of-distribution anomalous periodic variables within The Zwicky Transient Facility Catalog of Periodic Variable Stars (ZTF CPVS). We use an isolation forest to rank each periodic variable by its anomaly score. Our overall most anomalous events have a unique physical origin: they are mostly highly variable and irregular evolved stars. Multiwavelength data suggest that they are most likely Red Giant or Asymptotic Giant Branch stars concentrated in the Milky Way galactic disk. Furthermore, we show how the learned latent features can be used for the classification of periodic variables through a hierarchical random forest. This novel semi-supervised approach allows astronomers to identify the most anomalous events within a given physical class, significantly increasing the potential for scientific discovery.

We report a significant detection of the hot intergalactic medium in the filamentary bridge connecting the galaxy clusters Abell 399 and Abell 401. This result is enabled by a low-noise, high-resolution map of the thermal Sunyaev-Zeldovich signal from the Atacama Cosmology Telescope (ACT) and Planck satellite. The ACT data provide the 1.65′ resolution that allows us to clearly separate the profiles of the clusters, whose centres are separated by 37′, from the gas associated with the filament. A model that fits for only the two clusters is ruled out compared to one that includes a bridge component at >5σ. Using a gas temperature determined from Suzaku X-ray data, we infer a total mass of (3.3±0.7)×1014M⊙ associated with the filament, comprising about 8% of the entire Abell 399-Abell 401 system. We fit two phenomenological models to the filamentary structure; the favoured model has a width transverse to the axis joining the clusters of ∼1.9Mpc. When combined with the Suzaku data, we find a gas density of (0.88±0.24)×10−4cm−3, considerably lower than previously reported. We show that this can be fully explained by a geometry in which the axis joining Abell 399 and Abell 401 has a large component along the line of sight, such that the distance between the clusters is significantly greater than the 3.2Mpc projected separation on the plane of the sky. Finally, we present initial results from higher resolution (12.7" effective) imaging of the bridge with the MUSTANG-2 receiver on the Green Bank Telescope.

Mitochondrial metabolism is of central importance to diverse aspects of cell and developmental biology. Defects in mitochondria are associated with many diseases, including cancer, neuropathology, and infertility. Our understanding of mitochondrial metabolism in situ and dysfunction in diseases are limited by the lack of techniques to measure mitochondrial metabolic fluxes with sufficient spatiotemporal resolution. Herein, we developed a new method to infer mitochondrial metabolic fluxes in living cells with subcellular resolution from fluorescence lifetime imaging of NADH. This result is based on the use of a generic coarse-grained NADH redox model. We tested the model in mouse oocytes and human tissue culture cells subject to a wide variety of perturbations by comparing predicted fluxes through the electron transport chain (ETC) to direct measurements of oxygen consumption rate. Interpreting the fluorescence lifetime imaging microscopy measurements of NADH using this model, we discovered a homeostasis of ETC flux in mouse oocytes: perturbations of nutrient supply and energy demand of the cell do not change ETC flux despite significantly impacting NADH metabolic state. Furthermore, we observed a subcellular spatial gradient of ETC flux in mouse oocytes and found that this gradient is primarily a result of a spatially heterogeneous mitochondrial proton leak. We concluded from these observations that ETC flux in mouse oocytes is not controlled by energy demand or supply, but by the intrinsic rates of mitochondrial respiration.

The brain effortlessly solves blind source separation (BSS) problems, but the algorithm it uses remains elusive. In signal processing, linear BSS problems are often solved by Independent Component Analysis (ICA). To serve as a model of a biological circuit, the ICA neural network (NN) must satisfy at least the following requirements: 1. The algorithm must operate in the online setting where data samples are streamed one at a time, and the NN computes the sources on the fly without storing any significant fraction of the data in memory. 2. The synaptic weight update is local, i.e., it depends only on the biophysical variables present in the vicinity of a synapse. Here, we propose a novel objective function for ICA from which we derive a biologically plausible NN, including both the neural architecture and the synaptic learning rules. Interestingly, our algorithm relies on modulating synaptic plasticity by the total activity of the output neurons. In the brain, this could be accomplished by neuromodulators, extracellular calcium, local field potential, or nitric oxide.

Recently introduced shadow tomography protocols use “classical shadows” of quantum states to predict many target functions of an unknown quantum state. Unlike full quantum state tomography, shadow tomography does not insist on accurate recovery of the density matrix for high rank mixed states. Yet, such a protocol makes multiple accurate predictions with high confidence, based on a moderate number of quantum measurements. One particular influential algorithm, proposed by Huang et al. [Huang, Kueng, and Preskill, Nat. Phys. 16, 1050 (2020)], requires additional circuits for performing certain random unitary transformations. In this paper, we avoid these transformations but employ an arbitrary informationally complete positive operator-valued measure and show that such a procedure can compute k-bit correlation functions for quantum states reliably. We also show that, for this application, we do not need the median of means procedure of Huang et al. Finally, we discuss the contrast between the computation of correlation functions and fidelity of reconstruction of low rank density matrices.

After the detection of gravitational waves from compact binary coalescences, the search for transient gravitational-wave signals with less well-defined waveforms for which matched filtering is not well-suited is one of the frontiers for gravitational-wave astronomy. Broadly classified into "short" ≲1 \,s and "long" ≳1 \,s duration signals, these signals are expected from a variety of astrophysical processes, including non-axisymmetric deformations in magnetars or eccentric binary black hole coalescences. In this work, we present a search for long-duration gravitational-wave transients from Advanced LIGO and Advanced Virgo's third observing run from April 2019 to March 2020. For this search, we use minimal assumptions for the sky location, event time, waveform morphology, and duration of the source. The search covers the range of 2 -- 500~s in duration and a frequency band of 24−2048 Hz. We find no significant triggers within this parameter space; we report sensitivity limits on the signal strength of gravitational waves characterized by the root-sum-square amplitude hrss as a function of waveform morphology. These hrss limits improve upon the results from the second observing run by an average factor of 1.8.

Learning to detect content-independent transformations from data is one of the central problems in biological and artificial intelligence. An example of such problem is unsupervised learning of a visual motion detector from pairs of consecutive video frames. Rao and Ruderman formulated this problem in terms of learning infinitesimal transformation operators (Lie group generators) via minimizing image reconstruction error. Unfortunately, it is difficult to map their model onto a biologically plausible neural network (NN) with local learning rules. Here we propose a biologically plausible model of motion detection. We also adopt the transformation-operator approach but, instead of reconstruction-error minimization, start with a similarity-preserving objective function. An online algorithm that optimizes such an objective function naturally maps onto an NN with biologically plausible learning rules. The trained NN recapitulates major features of the well-studied motion detector in the fly. In particular, it is consistent with the experimental observation that local motion detectors combine information from at least three adjacent pixels, something that contradicts the celebrated Hassenstein-Reichardt model.

Deep convolutional neural networks (CNNs) for image denoising are usually trained on large datasets. These models achieve the current state of the art, but they have difficulties generalizing when applied to data that deviate from the training distribution. Recent work has shown that it is possible to train denoisers on a single noisy image. These models adapt to the features of the test image, but their performance is limited by the small amount of information used to train them. Here we propose “GainTuning”, in which CNN models pre trained on large datasets are adaptively and selectively adjusted for individual test images. To avoid overfitting, GainTuning optimizes a single multiplicative scaling parameter (the “Gain”) of each channel in the convolutional layers of the CNN. We show that GainTuning improves state-of-the-art CNNs on standard image-denoising benchmarks, boosting their denoising performance on nearly every image in a held-out test set. These adaptive improvements are even more substantial for test images differing systematically from the training data, either in noise level or image type. We illustrate the potential of adaptive denoising in a scientific application, in which a CNN is trained on synthetic data, and tested on real transmission-electronmicroscope images. In contrast to the existing methodology, GainTuning is able to faithfully reconstruct the structure of catalytic nanoparticles from these data at extremely low signal-to-noise ratios.