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Volcanic eruptions deposit Fe-bearing volcanic ash in the ocean, thereby increasing biological productivity. The increased organic matter in areas of high biological productivity uses up oxygen as this organic matter decays and sinks through the water column. Past living beings, like foraminifera, ate organic matter that was carbon-rich and sometimes had metals absorbed into their carbon, creating coatings inside and outside their shells. These coatings can tell us about how biological productivity was affected before, during, and after the volcanic eruption. The studied cores are from the northwest Pacific Ocean and are close to geologically young volcanoes that are not well understood. The two cores that we focused on were VM28-309 and VM36-15 both taken by the Vema research ship. We studied the relationship between ash deposition and biological productivity by looking at all the ash layers in both cores. We found that in most of the ash layers, there were black or dark-colored foraminifera with coatings inside and outside the shells that were often carbon-rich and sometimes metal-rich. We attribute this coating to the increase of organic matter in surface waters when there was deposition of large amounts of volcanic ash. We also found high concentrations of Barium metal in VM28-309. Barium (Ba) is a biological marker because most or all Ba originates from the organic matter contained in sediments. We found that ash layers containing the finest materials (<38 micrometers in size) had the highest Ba content. For accurate results, we must sample above and below ash layers and select more sediment cores in the area. Also, Barium corrections must be done using data on biogenic silica contents. Loss on ignition (LOI) data will give us an estimate of the total organic carbon in each sample- allowing a second direct assessment of the increase in biological productivity produced by the deposition of volcanic ash. 

Known as a bio-limiting metal, high abundances of iron in sea water can amplify biological productivity. The growth of diatoms and other photosynthetic organisms increases, providing more food for grazing organisms like foraminifera. The net result is more organic matter in surface waters and ultimately in surface sediments. Existing satellite data show increases in ocean chlorophyll in areas affected by volcanic eruptions. We infer from this that iron derived from volcanic ash does increase biological productivity. However, the relative increase in productivity is unknown. We examined 3 sediment cores from the Equatorial Western Pacific to analyze the relationship between volcanic ash and biological productivity: RC14-44, RC14-66, and RC14-67. All contain black or dark-colored foraminifera within ash layers and white-shelled foraminifera outside ash layers. We attribute the dark material outside and inside the foraminifera to organic carbon and metals. In our cores, some foraminifera are covered in iron sulfide (FeS), which could be pyrite, and contain large amounts of carbon as well as high abundances of aluminum and silicon. We examined barium concentrations to gain further knowledge of biological productivity at specific core depths as barium is a marker for primary productivity. We found that barium levels within ash layers increased at least ten-fold. Within ash layers, we also noticed that the ashes with higher amounts of fine silt and clay sized material have the greatest increase in barium content, perhaps related to explosion size. This pattern of increases in Ba, metals and organic carbon within ash layers compared to surrounding sediments shows that volcanic ash deposition increases marine productivity. For future research, measuring markers for biological productivity like biogenic silica content and loss on ignition (LOI) within and outside ash layers would further clarify the relationship between volcanic ash deposition and biological productivity. 

The COVID-19 pandemic has mainstreamed human mobility data into the public domain, with research focused on understanding the impact of mobility reduction policies as well as on regional COVID-19 case prediction models. Nevertheless, current research on COVID-19 case prediction tends to focus on performance improvements, masking relevant insights about when mobility data does not help, and more importantly, why, so that it can adequately inform local decision making. In this article, we carry out a systematic analysis to reveal the conditions under which human mobility data provides (or not) an enhancement over individual regional COVID-19 case prediction models that do not use mobility as a source of information. Our analysis— focused on U.S. county-based COVID-19 case prediction models—shows that (1) at most, 60% of counties improve their performance after adding mobility data; (2) the performance improvements are modest, with median correlation improvements of approximately 0.13; (3) improvements were lower for counties with higher Black, Hispanic, and other non-White populations as well as low-income and rural populations, pointing to potential bias in the mobility data negatively impacting predictive performance; and (4) different mobility datasets, predictive models, and training approaches bring about diverse performance improvements. 

Some satellite data show an increase in ocean chlorophyll in areas affected by volcanic eruptions. These increases in ocean color are thought to reflect an increase in photosynthetic activity by phytoplankton. These increases in primary production have been attributed to iron (Fe) from volcanic ash, particularly in high-latitude regions where primary productivity is limited by low Fe (the iron fertilization hypothesis). However, photosynthesis also appears to increase in the tropical ocean, for example in the Sunda and Ryukyu arcs and the Bismarck Sea, areas usually not thought to be iron limited. To examine the effects of volcanic ejecta on productivity in other areas, we examine relationships between ash deposition and biological productivity in three cores, RC14-44 (Sunda arc), VM28-309 (Ryukyu arc) and VM33-116 (Bismarck Sea). These cores contain volcanic ash layers with black or dark-colored foraminifera, different from the bright white foraminifera found outside of the ash layers. This dark coloration results primarily from organic carbon. In RC14-44, some foraminifera are coated with FeS and also contain high amounts of internal carbon. In VM28-309 and VM33-116, some foraminifera are filled with organic carbon rich materials, or have coatings rich in carbon. Occasionally, there are local enrichments in Fe within the foraminifera, indicative of extensive redox cycling. We attribute this carbon to increased biological productivity in these intervals. Barium (Ba) concentrations, a proxy for primary productivity because most or all Ba originates from organic matter contained in the sediment, is also enriched by up to 30-fold in the sediments containing ash. The ash layers with the highest amounts of fine material exhibit the largest enrichments in Ba, suggesting ash texture may influence the resulting changes in marine productivity. Overall, we find clear evidence that ash depositions increase both primary production and carbon export to sediments. Loss on ignition (LOI) and biogenic silica contents between and within ash layers, are potentially useful to further examine both the coupling between production and carbon burial, and the influence of ash deposition on phytoplankton community structure. 

Many causal and policy effects of interest are defined by linear functionals of high-dimensional or non-parametric regression functions. Root-n consistent and asymptotically normal estimation of the object of interest requires debiasing to reduce the effects of regularization and/or model selection on the object of interest. Debiasing is typically achieved by adding a correction term to the plug-in estimator of the functional, which leads to properties such as semi-parametric efficiency, double robustness, and Neyman orthogonality. We implement an automatic debiasing procedure based on automatically learning the Riesz representation of the linear functional using Neural Nets and Random Forests. Our method only relies on black-box evaluation oracle access to the linear functional and does not require knowledge of its analytic form. We propose a multitasking Neural Net debiasing method with stochastic gradient descent minimization of a combined Riesz representer and regression loss, while sharing representation layers for the two functions. We also propose a Random Forest method which learns a locally linear representation of the Riesz function. Even though our method applies to arbitrary functionals, we experimentally find that it performs well compared to the state of art neural net based algorithm of Shi et al. (2019) for the case of the average treatment effect functional. We also evaluate our method on the problem of estimating average marginal effects with continuous treatments, using semi-synthetic data of gasoline price changes on gasoline demand. Code available at github.com/victor5as/RieszLearning. 

The magnetar SGR 1935+2154 is the only known Galactic source of fast radio bursts (FRBs). FRBs from SGR 1935+2154 were first detected by the Canadian Hydrogen Intensity Mapping Experiment (CHIME)/FRB and the Survey for Transient Astronomical Radio Emission 2 in 2020 April, after the conclusion of the LIGO, Virgo, and KAGRA Collaborations’ O3 observing run. Here, we analyze four periods of gravitational wave (GW) data from the GEO600 detector coincident with four periods of FRB activity detected by CHIME/FRB, as well as X-ray glitches and X-ray bursts detected by NICER and NuSTAR close to the time of one of the FRBs. We do not detect any significant GW emission from any of the events. Instead, using a short-duration GW search (for bursts ≤1 s) we derive 50% (90%) upper limits of 10⁴⁸(10⁴⁹) erg for GWs at 300 Hz and 10⁴⁹(10⁵⁰) erg at 2 kHz, and constrain the GW-to-radio energy ratio to ≤10¹⁴−10¹⁶. We also derive upper limits from a long-duration search for bursts with durations between 1 and 10 s. These represent the strictest upper limits on concurrent GW emission from FRBs.

 

Gravitational lensing by massive objects along the line of sight to the source causes distortions to gravitational wave (GW) signals; such distortions may reveal information about fundamental physics, cosmology, and astrophysics. In this work, we have extended the search for lensing signatures to all binary black hole events from the third observing run of the LIGO-Virgo network. We search for repeated signals from strong lensing by (1) performing targeted searches for subthreshold signals, (2) calculating the degree of overlap among the intrinsic parameters and sky location of pairs of signals, (3) comparing the similarities of the spectrograms among pairs of signals, and (4) performing dual-signal Bayesian analysis that takes into account selection effects and astrophysical knowledge. We also search for distortions to the gravitational waveform caused by (1) frequency-independent phase shifts in strongly lensed images, and (2) frequency-dependent modulation of the amplitude and phase due to point masses. None of these searches yields significant evidence for lensing. Finally, we use the nondetection of GW lensing to constrain the lensing rate based on the latest merger-rate estimates and the fraction of dark matter composed of compact objects.