Publications

Galactic double neutron star systems have a tight mass distribution around ∼1.35M⊙, but the mass distribution of all known pulsars is broader. Here we reconstruct the Alsing, et al. (2018) bimodal mass distribution of pulsars observed in binary systems, incorporating data from observations of J0740+6620 which were not available at the time of that work. Because J0740+6620 is an outlier in the mass distribution with non-negligible uncertainty in its mass measurement, its mass receives a large correction from the population, becoming mJ0740+6620=2.03+0.10−0.08M⊙ (median and 68\% CI). Stochastic samples from our population model, including population-informed pulsar mass estimates, are available \href{https://github.com/farr/AlsingNSMassReplication}{at this https URL}.

Electron ratchets are non-equilibrium electronic devices that break inversion symmetry to produce currents from non-directional and random perturbations, without an applied net bias. They are characterized by strong parameter dependence, where small changes in operating conditions lead to large changes in the magnitude and even direction of the resulting current. This high sensitivity makes electron ratchets attractive research subjects, but leads to formidable challenges in their deeper study, and particularly to their useful application. This perspective reviews the progress that was made in the field starting from the first experimental electron ratchets in the late 1990s, and how the field spawned multiple designs with very different properties. We discuss the possible uses of electron ratchets in sensing and energy harvesting, and the specific issues encountered when idealized behavior meets complex reality. We promote an application-driven approach where complexity is not necessarily detrimental and argue that a system level perspective would be beneficial over reductionism. We highlight several promising research directions, which revolve around the intentional study of complex effects, and the modeling of realistic devices.

We explore the chemodynamical properties of a sample of barred galaxies in the Auriga magneto-hydrodynamical cosmological zoom-in simulations, which form boxy/peanut (b/p) bulges, and compare these to the Milky Way (MW). We show that the Auriga galaxies which best reproduce the chemodynamical properties of stellar populations in the MW bulge have quiescent merger histories since redshift z∼3.5: their last major merger occurs at tlookback>12Gyrs, while subsequent mergers have a stellar mass ratio of ≤1:20, suggesting an upper limit of a few percent for the mass ratio of the recently proposed Gaia Sausage/Enceladus merger. These Auriga MW-analogues have a negligible fraction of ex-situ stars in the b/p region (30%) and exhibit X-shaped age and abundance distributions. Examining the discs in our sample, we find that in some cases a star-forming ring forms around the bar, which alters the metallicity of the inner regions of the galaxy. Further out in the disc, bar-induced resonances lead to metal-rich ridges in the Vϕ−r plane -- the longest of which is due to the Outer Lindblad Resonance. Our results suggest the Milky Way has an uncommonly quiet merger history, which leads to an essentially in-situ bulge, and highlight the significant effects the bar can have on the surrounding disc.

To enable large-scale analyses of regulatory logic in model species, we developed DeepArk (https://DeepArk.princeton.edu), a set of deep learning models of the cis-regulatory codes of four widelystudied species: Caenorhabditis elegans, Danio rerio, Drosophila melanogaster, and Mus musculus. DeepArk accurately predicts the presence of thousands of different context-specific regulatory features, including chromatin states, histone marks, and transcription factors. In vivo studies show that DeepArk can predict the regulatory impact of any genomic variant (including rare or not previously observed), and enables the regulatory annotation of understudied model species.

defects affecting 3% of newborns. The human kidney develops over a 30-week period in which a nephron progenitor pool gives rise to around a million nephrons. To establish a framework for human nephrogenesis, we spatially resolved a stereotypical process by which equipotent nephron progenitors generate a nephron anlagen, then applied data-driven approaches to construct three-dimensional protein maps on anatomical models of the nephrogenic program. Single cell RNA sequencing identified novel progenitor states which were spatially mapped to the nephron anatomy enabling the generation of functional gene-networks predicting interactions within and between nephron cell-types. Network mining identified known developmental disease genes and predicts new targets of interest. The spatially resolved nephrogenic program made available through the Human Nephrogenesis Atlas (https://sckidney.flatironinstitute.org/) will facilitate an understanding of kidney development and disease, and enhance efforts to generate new kidney structures.

Many tools for dealing with compositional ‘ ’omics’ data produce feature-wise values that can be ranked in order to describe features’ associations with some sort of variation. These values include differentials (which describe features’ associations with specified covariates) and feature loadings (which describe features’ associations with variation along a given axis in a biplot). Although prior work has discussed the use of these ‘rankings’ as a starting point for exploring the log-ratios of particularly high- or low-ranked features, such exploratory analyses have previously been done using custom code to visualize feature rankings and the log-ratios of interest. This approach is laborious, prone to errors and raises questions about reproducibility. To address these problems we introduce Qurro, a tool that interactively visualizes a plot of feature rankings (a ‘rank plot’) alongside a plot of selected features’ log-ratios within samples (a ‘sample plot’). Qurro’s interface includes various controls that allow users to select features from along the rank plot to compute a log-ratio; this action updates both the rank plot (through highlighting selected features) and the sample plot (through displaying the current log-ratios of samples). Here, we demonstrate how this unique interface helps users explore feature rankings and log-ratios simply and effectively.

Sr2MoO4 is isostructural to the unconventional superconductor Sr2RuO4 but with two electrons instead of two holes in the Mo/Ru-t2g orbitals. Both materials are Hund's metals, but while Sr2RuO4 has a van Hove singularity in close proximity to the Fermi surface, the van Hove singularity of Sr2MoO4 is far from the Fermi surface. By using density functional plus dynamical mean-field theory we determine the relative influence of van Hove and Hund's metal physics on the correlation properties. We show that theoretically predicted signatures of Hund's metal physics occur on the occupied side of the electronic spectrum of Sr2MoO4, identifying Sr2MoO4 as an ideal candidate system for a direct experimental confirmation of the theoretical concept of Hund's metals via photoemission spectroscopy.

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 a catalog of 510 radio-loud active galactic nuclei (AGNs, primarily blazars) and 287 dusty star-forming galaxies (DSFGs) detected by the Atacama Cosmology Telescope at $\gt 5\sigma $ significance in frequency bands centered on 148 GHz (2 mm), 218 GHz (1.4 mm), and 277 GHz (1.1 mm), from a 480 deg2 strip centered at R.A. 00h on the celestial equator with additional 360 deg2 shallower auxiliary fields at other longitudes. The combination of the deepest available 218 GHz wide-field imaging, our 277 GHz data, and multiband filtering results in the most sensitive wide-field millimeter-wave DSFG selection to date, with rms noise level referenced to 218 GHz reaching below 2 mJy. We have developed new techniques to remove Galactic contamination (including evidence for CO (2−1) line emission) from the extragalactic catalog, yielding a catalog of 321 Galactic sources in addition to the extragalactic catalog. We employ a new flux debiasing method that accounts for the heterogeneous sample selection in the presence of Galactic cuts. We present the spectral properties and source counts of the AGNs and DSFGs. The DSFG spectra depart from the Rayleigh–Jeans regime of an optically thin modified blackbody between 218 and 277 GHz, consistent with optically thick emission or an additional cold dust component. For AGNs with 148 and 218 GHz flux density >50 mJy, we estimate the interyear rms fractional deviation in flux density due to source variability to be 40% with a 0.98 interband correlation coefficient. We provide source counts for AGNs in the range of 8–2870 mJy and for DSFGs in the range of 8–90 mJy. Our DSFG counts probe both the brighter, lensed population and the fainter, unlensed population. At 277 GHz we report the first measurements of source counts at these flux densities, finding an excess above most model count predictions. Finally, we present 30 of the brightest DSFGs that were selected for multifrequency study as candidate high-z lensed systems.

The goal of this study is to present the development of a machine learning based approach that utilizes phase space alone to separate the Gaia DR2 stars into two categories: those accreted onto the Milky Way from those that are in situ. Traditional selection methods that have been used to identify accreted stars typically rely on full 3D velocity, metallicity information, or both, which significantly reduces the number of classifiable stars. The approach advocated here is applicable to a much larger portion of Gaia DR2. A method known as "transfer learning" is shown to be effective through extensive testing on a set of mock Gaia catalogs that are based on the FIRE cosmological zoom-in hydrodynamic simulations of Milky Way-mass galaxies. The machine is first trained on simulated data using only 5D kinematics as inputs and is then further trained on a cross-matched Gaia/RAVE data set, which improves sensitivity to properties of the real Milky Way. The result is a catalog that identifies around 767,000 accreted stars within Gaia DR2. This catalog can yield empirical insights into the merger history of the Milky Way and could be used to infer properties of the dark matter distribution.