A search for millicharged particles, a simple extension of the standard model, has been performed with the ArgoNeuT detector exposed to the Neutrinos at the Main Injector beam at Fermilab. The ...ArgoNeuT liquid argon time projection chamber detector enables a search for millicharged particles through the detection of visible electron recoils. We search for an event signature with two soft hits (MeV-scale energy depositions) aligned with the upstream target. For an exposure of the detector of 1.0×10^{20} protons on target, one candidate event has been observed, compatible with the expected background. This search is sensitive to millicharged particles with charges between 10^{-3}e and 10^{-1}e and with masses in the range from 0.1 to 3 GeV. This measurement provides leading constraints on millicharged particles in this large unexplored parameter space region.
MeV-scale energy depositions by low-energy photons produced in neutrino-argon interactions have been identified and reconstructed in ArgoNeuT liquid argon time projection chamber (LArTPC) data. ...ArgoNeuT data collected on the NuMI beam at Fermilab were analyzed to select isolated low-energy depositions in the TPC volume. The total number, reconstructed energies, and positions of these depositions have been compared to those from simulations of neutrino-argon interactions using the fluka Monte Carlo generator. Measured features are consistent with energy depositions from photons produced by deexcitation of the neutrino’s target nucleus and by inelastic scattering of primary neutrons produced by neutrino-argon interactions. This study represents a successful reconstruction of physics at the MeV scale in a LArTPC, a capability of crucial importance for detection and reconstruction of supernova and solar neutrino interactions in future large LArTPCs.
Mapping brain-behaviour associations is paramount to understand and treat psychiatric disorders. Standard approaches involve investigating the association between one brain and one behavioural ...variable (univariate) or multiple variables against one brain/behaviour feature ('single' multivariate). Recently, large multimodal datasets have propelled a new wave of studies that leverage on 'doubly' multivariate approaches capable of parsing the multifaceted nature of both brain and behaviour simultaneously. Within this movement, canonical correlation analysis (CCA) and partial least squares (PLS) emerge as the most popular techniques. Both seek to capture shared information between brain and behaviour in the form of latent variables. We provide an overview of these methods, review the literature in psychiatric disorders, and discuss the main challenges from a predictive modelling perspective. We identified 39 studies across four diagnostic groups: attention deficit and hyperactive disorder (ADHD, k = 4, N = 569), autism spectrum disorders (ASD, k = 6, N = 1731), major depressive disorder (MDD, k = 5, N = 938), psychosis spectrum disorders (PSD, k = 13, N = 1150) and one transdiagnostic group (TD, k = 11, N = 5731). Most studies (67%) used CCA and focused on the association between either brain morphology, resting-state functional connectivity or fractional anisotropy against symptoms and/or cognition. There were three main findings. First, most diagnoses shared a link between clinical/cognitive symptoms and two brain measures, namely frontal morphology/brain activity and white matter association fibres (tracts between cortical areas in the same hemisphere). Second, typically less investigated behavioural variables in multivariate models such as physical health (e.g., BMI, drug use) and clinical history (e.g., childhood trauma) were identified as important features. Finally, most studies were at risk of bias due to low sample size/feature ratio and/or in-sample testing only. We highlight the importance of carefully mitigating these sources of bias with an exemplar application of CCA.
Marine algae are instrumental in carbon cycling and atmospheric carbon dioxide (CO2) regulation. One group, coccolithophores, uses carbon to photosynthesize and to calcify, covering their cells with ...chalk platelets (coccoliths). How ocean acidification influences coccolithophore calcification is strongly debated, and the effects of carbonate chemistry changes in the geological past are poorly understood. This paper relates degree of coccolith calcification to cellular calcification, and presents the first records of size-normalized coccolith thickness spanning the last 14 Myr from tropical oceans. Degree of calcification was highest in the low-pH, high-CO2 Miocene ocean, but decreased significantly between 6 and 4 Myr ago. Based on this and concurrent trends in a new alkenone ɛp record, we propose that decreasing CO2 partly drove the observed trend via reduced cellular bicarbonate allocation to calcification. This trend reversed in the late Pleistocene despite low CO2, suggesting an additional regulator of calcification such as alkalinity.
Carbon isotope records from alkenone biomarkers (εp37:2) produced by haptophyte algae are frequently used for atmospheric CO2 paleobarometry, but this method has yielded inconsistent results during ...periods where CO2 variations are known independently. Recent syntheses of algal cultures have quantitatively demonstrated that εp37:2 indeed records CO2 information: εp37:2 increases as aqueous CO2 concentrations increase relative to carbon demand. However, interpretations of εp37:2 are complicated by irradiance, where higher irradiance yields higher εp37:2. Here we examine the roles of physiology and environment in setting εp37:2 in the ocean. We compile water‐column and sediment core‐top εp37:2 data and add new core‐top measurements, including estimates of cell sizes and growth rates of the alkenone‐producing population. In support of culture studies, we find irradiance to be a key control on εp37:2 in the modern ocean. We test a culture‐derived model of εp37:2 and find that the quantitative relationships calibrated in culture experiments can be used to predict εp37:2 in sediment samples. In water‐column samples, the model substantially overestimates εp37:2, largely resulting from higher irradiance at the depth of sample collection than the integrated light conditions under which the sampled biomass was produced and vertically mixed to the collection depth. We argue that the theory underpinning the conventional diffusive alkenone carbon isotope fractionation model, including the “b” parameter, is not supported by field data and should not be used to reconstruct past CO2 changes. Future estimates of CO2 from εp37:2 should use empirical or mechanistic models to quantitatively account for irradiance and cell size variations.
Key Points
Irradiance is a primary control on alkenone carbon isotope fractionation in the natural environment
Culture‐calibrated model can predict alkenone carbon isotope fractionation in core‐top sediment samples
Diffusive model “b” value is not correlated with growth rate: diffusive model should not be used for paleobarometry
Paleoceanographic studies often rely on abundance changes in microfossil species, with little consideration for characteristics such as organism size, which may also be related to environmental ...changes. Using a tropical Indian Ocean (TIO) core‐top data set, we test the Optimum size‐hypothesis (OSH), investigating whether relative abundance or environmental variables are better descriptors of planktonic foraminifera species' optimum conditions. We also investigate the environmental drivers of whole‐assemblage planktonic foraminiferal test size variation in the TIO. We use an automated imaging and sorting system (MiSo) to identify planktonic foraminiferal species, analyze their morphology, and quantify fragmentation rate using machine learning techniques. Machine model accuracy is confirmed by comparison with human classifiers (97% accuracy). Data for 33 environmental parameters were extracted from modern databases and, through exploratory factor analysis and regression models, we explore relationships between planktonic foraminiferal size and oceanographic parameters in the TIO. Results show that the size frequency distribution of most planktonic foraminifera species is unimodal, with some larger species showing multimodal distributions. Assemblage size95/5 (95th percentile size) increases with increasing species diversity, and this is attributed to vertical niche separation induced by thermal stratification. Our test for the OSH reveals that relative abundance is not a good predictor of species' optima and within‐species size95/5 response to environmental parameters is species‐specific, with parameters related to carbonate ion concentration, temperature, and salinity being primary drivers. At the species and assemblage levels, our analyses indicate that carbonate ion concentration and temperature play important roles in determining size trends in TIO planktonic foraminifera.
Plain Language Summary
In core‐top samples from the tropical Indian Ocean (TIO), we investigate the optimum size‐hypothesis, testing whether species' relative abundance or environmental parameter(s) are better descriptors of planktonic foraminifera species' optimum conditions. Further, we investigate the main environmental drivers of size variations in planktonic foraminifera at the assemblage‐level, given that temperature has been reported to primarily drive assemblage size trends. We use a state‐of‐the‐art machine (MiSo) to automatically identify planktonic foraminiferal species, analyze their size, and quantify fragmentation using machine learning techniques. When compared to identification carried out by human experts across 21 species, the machine classified the species accurately 97% of the time. The MiSo‐generated size data was similar to that by other researchers. The frequency distributions of the species' size spectra show that most species have distributions that form bell‐shaped curves. As species diversity increased, so did the assemblage size (95th percentile size); we attribute this observation to the effect of temperature‐dependent niche separation. We find that, in the TIO, environmental parameters are better descriptors of optimum conditions in planktonic foraminifera than relative abundance. Our results also reveal that size variation at the species and assemblage levels is mostly driven by ambient carbonate chemistry and temperature.
Key Points
Optimum size‐hypothesis holds true in planktonic foraminifera if one considers the main parameters driving each species' size distribution
Size variations in planktonic foraminifera are linked to species' niches and diversity does not increase with productivity
Within‐species size is driven by CO32− concentration, temperature, and salinity; assemblage size by CO32− concentration and temperature
The ArgoNeuT Collaboration presents the first measurements of inclusive muon neutrino charged current differential cross sections on argon. Obtained in the NuMI neutrino beam line at Fermilab, the ...flux-integrated results are reported in terms of outgoing muon angle and momentum. The data are consistent with the Monte Carlo expectation across the full range of kinematics sampled, 0°<θ(μ)<36° and 0<P(μ)<25 GeV/c. Along with confirming the viability of liquid argon time projection chamber technology for neutrino detection, the measurements allow tests of low-energy neutrino scattering models important for interpreting results from long baseline neutrino oscillation experiments designed to investigate CP violation and the orientation of the neutrino mass hierarchy.
The capabilities of liquid argon time projection chambers (LArTPCs) to reconstruct the spatial and calorimetric information of neutrino events have made them the detectors of choice in a number of ...experiments, specifically those looking to observe electron neutrino (νe) appearance. The LArTPC promises excellent background rejection capabilities, especially in this “golden” channel for both short and long baseline neutrino oscillation experiments. We present the first experimental observation of electron neutrinos and antineutrinos in the ArgoNeut LArTPC, in the energy range relevant to DUNE and the Fermilab Short Baseline Neutrino Program. We have selected 37 electron candidate events and 274 gamma candidate events, and measured an 80% purity of electrons based on a topological selection. Additionally, we present a separation of electrons from gammas using calorimetric energy deposition, demonstrating further separation of electrons from background gammas.
We report the first electron neutrino cross section measurements on argon, based on data collected by the ArgoNeuT experiment running in the GeV-scale NuMI beamline at Fermilab. A flux-averaged νe + ...νe total and a lepton angle differential cross section are extracted using 13 νe and νe events identified with fully automated selection and reconstruction. We employ electromagnetic-induced shower characterization and analysis tools developed to identify νe / νe-like events among complex interaction topologies present in ArgoNeuT data ( ⟨ E νe ⟩ = 4.3 GeV and ⟨ E νe ⟩ = 10.5 GeV ). The techniques are widely applicable to searches for electron-flavor appearance at short and long baseline using liquid argon time projection chamber technology. Notably, the data-driven studies of GeV-scale νe / νe interactions presented here probe an energy regime relevant for future DUNE oscillation physics.
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