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  1. Abstract The Millimeter-wave Intensity Mapping Experiment (mmIME) recently reported a detection of excess spatial fluctuations at a wavelength of 3 mm, which can be attributed to unresolved emission of several CO rotational transitions between z ∼ 1 and 5. We study the implications of these data for the high-redshift interstellar medium using a suite of state-of-the-art semianalytic simulations that have successfully reproduced many other submillimeter line observations across the relevant redshift range. We find that the semianalytic predictions are mildly in tension with the mmIME result, with a predicted CO power ∼3.5 σ below what was observed. We explore some simple modifications to the models that could resolve this tension. Increasing the molecular gas abundance at the relevant redshifts to ∼10 8 M ⊙ Mpc −3 , a value well above that obtained from directly imaged sources, would resolve the discrepancy, as would assuming a CO–H 2 conversion factor α CO of ∼1.5 M ⊙ K −1 (km s −1 ) −1 pc 2 , a value somewhat lower than is commonly assumed. We go on to demonstrate that these conclusions are quite sensitive to the detailed assumptions of our simulations, highlighting the need for more careful modeling efforts asmore »more intensity mapping data become available.« less
    Free, publicly-accessible full text available April 1, 2023
  2. Abstract Submillimeter emission lines produced by the interstellar medium (ISM) are strong tracers of star formation and are some of the main targets of line intensity mapping (LIM) surveys. In this work we present an empirical multiline emission model that simultaneously covers the mean, scatter, and correlations of [C ii ], CO J = 1–0 to J = 5–4, and [C i ] lines in the redshift range 1 ≤ z ≤ 9. We assume that the galaxy ISM line emission luminosity versus halo mass relations can be described by double power laws with redshift-dependent lognormal scatter. The model parameters are then derived by fitting to the state-of-the-art semianalytic simulation results that have successfully reproduced multiple submillimeter line observations at 0 ≤ z ≲ 6. We cross-check the line emission statistics predicted by the semianalytic simulation and our empirical model, finding that at z ≥ 1 our model reproduces the simulated line intensities with fractional error less than about 10%. The fractional difference is less than 25% for the power spectra. Grounded on physically motivated and self-consistent galaxy simulations, this computationally efficient model will be helpful in forecasting ISM emission-line statistics for upcoming LIM surveys.
    Free, publicly-accessible full text available April 1, 2023
  3. Free, publicly-accessible full text available May 11, 2023
  4. The scientific literature sometimes considers music an abstract stimulus, devoid of explicit meaning, and at other times considers it a universal language. Here, individuals in three geographically distinct locations spanning two cultures performed a highly unconstrained task: they provided free-response descriptions of stories they imagined while listening to instrumental music. Tools from natural language processing revealed that listeners provide highly similar stories to the same musical excerpts when they share an underlying culture, but when they do not, the generated stories show limited overlap. These results paint a more complex picture of music’s power: music can generate remarkably similar stories in listeners’ minds, but the degree to which these imagined narratives are shared depends on the degree to which culture is shared across listeners. Thus, music is neither an abstract stimulus nor a universal language but has semantic affordances shaped by culture, requiring more sustained attention from psychology.
    Free, publicly-accessible full text available January 25, 2023
  5. Purugganan, Michael (Ed.)
    Abstract The deleterious effects of inbreeding have been of extreme importance to evolutionary biology, but it has been difficult to characterize the complex interactions between genetic constraints and selection that lead to fitness loss and recovery after inbreeding. Haploid organisms and selfing organisms like the nematode Caenorhabditis elegans are capable of rapid recovery from the fixation of novel deleterious mutation; however, the potential for recovery and genomic consequences of inbreeding in diploid, outcrossing organisms are not well understood. We sought to answer two questions: 1) Can a diploid, outcrossing population recover from inbreeding via standing genetic variation and new mutation? and 2) How does allelic diversity change during recovery? We inbred C. remanei, an outcrossing relative of C. elegans, through brother-sister mating for 30 generations followed by recovery at large population size. Inbreeding reduced fitness but, surprisingly, recovery from inbreeding at large populations sizes generated only very moderate fitness recovery after 300 generations. We found that 65% of ancestral single nucleotide polymorphisms (SNPs) were fixed in the inbred population, far fewer than the theoretical expectation of ∼99%. Under recovery, 36 SNPs across 30 genes involved in alimentary, muscular, nervous, and reproductive systems changed reproducibly across replicates, indicating that strong selectionmore »for fitness recovery does exist. Our results indicate that recovery from inbreeding depression via standing genetic variation and mutation is likely to be constrained by the large number of segregating deleterious variants present in natural populations, limiting the capacity for recovery of small populations.« less
    Free, publicly-accessible full text available January 1, 2023
  6. Accurate multidecadal radiative flux records are vital to understand Arctic amplification and constrain climate model uncertainties. Uncertainty in the NASA Clouds and the Earth’s Radiant Energy System (CERES)-derived irradiances is larger over sea ice than any other surface type and comes from several sources. The year-long Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition in the central Arctic provides a rare opportunity to explore uncertainty in CERES-derived radiative fluxes. First, a systematic and statistically robust assessment of surface shortwave and longwave fluxes was conducted using in situ measurements from MOSAiC flux stations. The CERES Synoptic 1degree (SYN1deg) product overestimates the downwelling shortwave flux by +11.40 Wm–2 and underestimates the upwelling shortwave flux by –15.70 Wm–2 and downwelling longwave fluxes by –12.58 Wm–2 at the surface during summer. In addition, large differences are found in the upwelling longwave flux when the surface approaches the melting point (approximately 0°C). The biases in downwelling shortwave and longwave fluxes suggest that the atmosphere represented in CERES is too optically thin. The large negative bias in upwelling shortwave flux can be attributed in large part to lower surface albedo (–0.15) in satellite footprint relative to surface sensors. Additionally, the results show thatmore »the spectral surface albedo used in SYN1deg overestimates albedo in visible and mid-infrared bands. A series of radiative transfer model perturbation experiments are performed to quantify the factors contributing to the differences. The CERES-MOSAiC broadband albedo differences (approximately 20 Wm–2) explain a larger portion of the upwelling shortwave flux difference than the spectral albedo shape differences (approximately 3 Wm–2). In addition, the differences between perturbation experiments using hourly and monthly MOSAiC surface albedo suggest that approximately 25% of the sea ice surface albedo variability is explained by factors not correlated with daily sea ice concentration variability. Biases in net shortwave and longwave flux can be reduced to less than half by adjusting both albedo and cloud inputs toward observed values. The results indicate that improvements in the surface albedo and cloud data would substantially reduce the uncertainty in the Arctic surface radiation budget derived from CERES data products.« less
    Free, publicly-accessible full text available January 1, 2023
  7. Arctic amplification (AA) is a coupled atmosphere-sea ice-ocean process. This understanding has evolved from the early concept of AA, as a consequence of snow-ice line progressions, through more than a century of research that has clarified the relevant processes and driving mechanisms of AA. The predictions made by early modeling studies, namely the fall/winter maximum, bottom-heavy structure, the prominence of surface albedo feedback, and the importance of stable stratification have withstood the scrutiny of multi-decadal observations and more complex models. Yet, the uncertainty in Arctic climate projections is larger than in any other region of the planet, making the assessment of high-impact, near-term regional changes difficult or impossible. Reducing this large spread in Arctic climate projections requires a quantitative process understanding. This manuscript aims to build such an understanding by synthesizing current knowledge of AA and to produce a set of recommendations to guide future research. It briefly reviews the history of AA science, summarizes observed Arctic changes, discusses modeling approaches and feedback diagnostics, and assesses the current understanding of the most relevant feedbacks to AA. These sections culminate in a conceptual model of the fundamental physical mechanisms causing AA and a collection of recommendations to accelerate progress towards reducedmore »uncertainty in Arctic climate projections. Our conceptual model highlights the need to account for local feedback and remote process interactions within the context of the annual cycle to constrain projected AA. We recommend raising the priority of Arctic climate sensitivity research, improving the accuracy of Arctic surface energy budget observations, rethinking climate feedback definitions, coordinating new model experiments and intercomparisons, and further investigating the role of episodic variability in AA.« less
    Free, publicly-accessible full text available February 9, 2023
  8. High-resolution optical imaging systems are quickly becoming universal tools to characterize and quantify microbial diversity in marine ecosystems. Automated detection systems such as convolutional neural networks (CNN) are often developed to identify the immense number of images collected. The goal of our study was to develop a CNN to classify phytoplankton images collected with an Imaging FlowCytobot for the Palmer Antarctica Long-Term Ecological Research project. A medium complexity CNN was developed using a subset of manually-identified images, resulting in an overall accuracy, recall, and f1-score of 93.8%, 93.7%, and 93.7%, respectively. The f1-score dropped to 46.5% when tested on a new random subset of 10,269 images, likely due to highly imbalanced class distributions, high intraclass variance, and interclass morphological similarities of cells in naturally occurring phytoplankton assemblages. Our model was then used to predict taxonomic classifications of phytoplankton at Palmer Station, Antarctica over 2017-2018 and 2018-2019 summer field seasons. The CNN was generally able to capture important seasonal dynamics such as the shift from large centric diatoms to small pennate diatoms in both seasons, which is thought to be driven by increases in glacial meltwater from January to March. Moving forward, we hope to further increase the accuracy of ourmore »model to better characterize coastal phytoplankton communities threatened by rapidly changing environmental conditions.« less
  9. Free, publicly-accessible full text available November 30, 2022
  10. Free, publicly-accessible full text available December 9, 2022