Publish or perish is an expression describing the pressure on academics to consistently publish research to ensure a successful career in academia. With a global pandemic that has changed the world, how has it changed academic productivity? Here we s
how that academics are posting just as many publications on the arXiv pre-print server as if there were no pandemic: 168,630 were posted in 2020, a +12.6% change from 2019 and $+1.4sigma$ deviation above the predicted 162,577 $pm$ 4,393. However, some immediate impacts are visible in individual research fields. Conference cancellations have led to sharp drops in pre-prints, but laboratory closures have had mixed effects. Only some experimental fields show mild declines in outputs, with most being consistent on previous years or even increasing above model expectations. The most significant change is a 50% increase ($+8sigma$) in quantitative biology research, all related to the COVID-19 pandemic. Some of these publications are by biologists using arXiv for the first time, and some are written by researchers from other fields (e.g., physicists, mathematicians). While quantitative biology pre-prints have returned to pre-pandemic levels, 20% of the research in this field is now focussed on the COVID-19 pandemic, demonstrating a strong shift in research focus.
The Phoenix stellar stream has a low intrinsic dispersion in velocity and metallicity that implies the progenitor was probably a low mass globular cluster. In this work we use Magellan/MIKE high-dispersion spectroscopy of eight Phoenix stream red gia
nts to confirm this scenario. In particular, we find negligible intrinsic scatter in metallicity ($sigma(mathrm{[Fe~II/H]}) = 0.04^{+0.11}_{-0.03}$) and a large peak-to-peak range in [Na/Fe] and [Al/Fe] abundance ratios, consistent with the light element abundance patterns seen in the most metal-poor globular clusters. However, unlike any other globular cluster, we also find an intrinsic spread in [Sr II/Fe] spanning $sim$1 dex, while [Ba II/Fe] shows nearly no intrinsic spread ($sigma(mathrm{[Ba~II/H]}) = {0.03}^{+0.10}_{-0.02}$). This abundance signature is best interpreted as slow neutron capture element production from a massive fast-rotating metal-poor star ($15-20 mathrm{M}_odot$, $v_mathrm{ini}/v_mathrm{crit} = 0.4$, $[mathrm{Fe/H}] = -3.8$). The low inferred cluster mass suggests the system would have been unable to retain supernovae ejecta, implying that any massive fast-rotating metal-poor star that enriched the interstellar medium must have formed and evolved before the globular cluster formed. Neutron capture element production from asymptotic giant branch stars or magneto-rotational instabilities in core-collapse supernovae provide poor fits to the observations. We also report one Phoenix stream star to be a lithium-rich giant ($A(mathrm{Li}) = 3.1 pm 0.1$). At $[mathrm{Fe/H}] = -2.93$ it is among the most metal-poor lithium-rich giants known.
The nanohertz gravitational wave background (GWB) is believed to be dominated by GW emission from supermassive black hole binaries (SMBHBs). Observations of several dual active galactic nuclei (AGN) strongly suggest a link between AGN and SMBHBs, giv
en that these dual AGN systems will eventually form bound binary pairs. Here we develop an exploratory SMBHB population model based on empirically constrained quasar populations, allowing us to decompose the GWB amplitude into an underlying distribution of SMBH masses, SMBHB number density, and volume enclosing the GWB. Our approach also allows us to self-consistently predict the GWB amplitude and the number of local SMBHB systems. Interestingly, we find the local number density of SMBHBs implied by the common-process signal in the NANOGrav 12.5-yr dataset to be roughly five times larger than previously predicted by other models. We also find that at most $sim 25 %$ of SMBHBs can be associated with quasars. Furthermore, our quasar-based approach predicts $gtrsim 95%$ of the GWB signal comes from $z lesssim 2.5$, and that SMBHBs contributing to the GWB have masses $gtrsim 10^8 M_odot$. We also explore how different empirical galaxy-black hole scaling relations affect the local number density of GW sources, and find that relations predicting more massive black holes decrease the local number density of SMBHBs. Overall, our results point to the important role that a measurement of the GWB will play in directly constraining the cosmic population of SMBHBs, as well as their connections to quasars and galaxy mergers.
Chemical tagging seeks to identify unique star formation sites from present-day stellar abundances. Previous techniques have treated each abundance dimension as being statistically independent, despite theoretical expectations that many elements can
be produced by more than one nucleosynthetic process. In this work we introduce a data-driven model of nucleosynthesis where a set of latent factors (e.g., nucleosynthetic yields) contribute to all stars with different scores, and clustering (e.g., chemical tagging) is modelled by a mixture of multivariate Gaussians in a lower-dimensional latent space. We use an exact method to simultaneously estimate the factor scores for each star, the partial assignment of each star to each cluster, and the latent factors common to all stars, even in the presence of missing data entries. We use an information-theoretic Bayesian principle to estimate the number of latent factors and clusters. Using the second Galah data release we find that six latent factors are preferred to explain N = 2,566 stars with 17 chemical abundances. We identify the rapid- and slow-neutron capture processes, as well as latent factors consistent with Fe-peak and alpha-element production, and another where K and Zn dominate. When we consider N ~ 160,000 stars with missing abundances we find another 7 factors, as well as 16 components in latent space. Despite these components showing separation in chemistry that is explained through different yield contributions, none show significant structure in their positions or motions. We argue that more data, and joint priors on cluster membership that are constrained by dynamical models, are necessary to realise chemical tagging at a galactic-scale. We release software that allows for model parameters to be optimised in seconds given a fixed number of latent factors, components, and $10^7$ abundance measurements.
Theoretical models of stellar evolution predict that most of the lithium inside a star is destroyed as the star becomes a red giant. However, observations reveal that about 1% of red giants are peculiarly rich in lithium, often exceeding the amount i
n the interstellar medium or predicted from the Big Bang. With only about 150 lithium-rich giants discovered in the past four decades, and no distinguishing properties other than lithium enhancement, the origin of lithium-rich giant stars is one of the oldest problems in stellar astrophysics. Here we report the discovery of 2,330 low-mass (1 to 3$,M_odot$) lithium-rich giant stars, which we argue are consistent with internal lithium production that is driven by tidal spin-up by a binary companion. Our sample reveals that most lithium-rich giants have helium-burning cores ($80^{+7}_{-6}%$), and that the frequency of lithium-rich giants rises with increasing stellar metallicity. We find that while planet accretion may explain some lithium-rich giants, it cannot account for the majority that have helium-burning cores. We rule out most other proposed explanations as the primary mechanism for lithium-rich giants, including all stages related to single star evolution. Our analysis shows that giants remain lithium-rich for only about two million years. A prediction from this lithium depletion timescale is that most lithium-rich giants with a helium-burning core have a binary companion.
The orbits, atmospheric parameters, chemical abundances, and ages of individual stars in the Milky Way provide the most comprehensive illustration of galaxy formation available. The Tycho-Gaia Astrometric Solution (TGAS) will deliver astrometric para
meters for the largest ever sample of Milky Way stars, though its full potential cannot be realized without the addition of complementary spectroscopy. Among existing spectroscopic surveys, the RAdial Velocity Experiment (RAVE) has the largest overlap with TGAS ($gtrsim$200,000 stars). We present a data-driven re-analysis of 520,781 RAVE spectra using The Cannon. For red giants, we build our model using high-fidelity APOGEE stellar parameters and abundances for stars that overlap with RAVE. For main-sequence and sub-giant stars, our model uses stellar parameters from the K2/EPIC. We derive and validate effective temperature $T_{rm eff}$, surface gravity $log{g}$, and chemical abundances of up to seven elements (O, Mg, Al, Si, Ca, Fe, Ni). We report a total of 1,685,851 elemental abundances with a typical precision of 0.07 dex, a substantial improvement over previous RAVE data releases. The synthesis of RAVE-on and TGAS is the most powerful data set for chemo-dynamic analyses of the Milky Way ever produced.
