Embedded-Cluster Calculations in a Numeric Atomic Orbital Density-Functional Theory Framework

We integrate the all-electron electronic structure code FHI-aims into the general ChemShell package for solid-state embedding (QM/MM) calculations. A major undertaking in this integration is the implementation of pseudopotential functionality into FHI-aims to describe cations at the QM/MM boundary through effective core potentials and therewith prevent spurious overpolarization of the electronic density. Based on numeric atomic orbital basis sets, FHI-aims offers particularly efficient access to exact exchange and second order perturbation theory, rendering the established QM/MM setup an ideal tool for hybrid and double-hybrid level DFT calculations of solid systems. We illustrate this capability by calculating the reduction potential of Fe in the Fe-substituted ZSM-5 zeolitic framework and the reaction energy profile for (photo-)catalytic water oxidation at TiO2(110).Comment: 12 pages, 4 figure

Unbiased Cosmological Parameter Estimation from Emission Line Surveys with Interlopers

The galaxy catalogs generated from low-resolution emission line surveys often contain both foreground and background interlopers due to line misidentification, which can bias the cosmological parameter estimation. In this paper, we present a method for correcting the interloper bias by using the joint-analysis of auto- and cross-power spectra of the main and the interloper samples. In particular, we can measure the interloper fractions from the cross-correlation between the interlopers and survey galaxies, because the true cross-correlation must be negligibly small. The estimated interloper fractions, in turn, remove the interloper bias in the cosmological parameter estimation. For example, in the Hobby-Eberly Telescope Dark Energy Experiment (HETDEX) low-redshift ($z<0.5$) [O II] $\lambda3727${\AA} emitters contaminate high-redshift ($1.9<z<3.5$) Lyman-$\alpha$ line emitters. We demonstrate that the joint-analysis method yields a high signal-to-noise ratio measurement of the interloper fractions while only marginally increasing the uncertainties in the cosmological parameters relative to the case without interlopers. We also show the same is true for the high-latitude spectroscopic survey of Wide-Field Infrared Survey Telescope (WFIRST) mission where contamination occurs between the Balmer-$\alpha$ line emitters at lower redshifts ($1.1<z<1.9$) and Oxygen ([O III] $\lambda5007${\AA}) line emitters at higher redshifts ($1.7<z<2.8$).Comment: 36 pages, 26 figure

Supervoid Origin of the Cold Spot in the Cosmic Microwave Background

We use a WISE-2MASS-Pan-STARRS1 galaxy catalog to search for a supervoid in the direction of the Cosmic Microwave Background Cold Spot. We obtain photometric redshifts using our multicolor data set to create a tomographic map of the galaxy distribution. The radial density profile centred on the Cold Spot shows a large low density region, extending over 10's of degrees. Motivated by previous Cosmic Microwave Background results, we test for underdensities within two angular radii, $5^\circ$, and $15^\circ$. Our data, combined with an earlier measurement by Granett et al 2010, are consistent with a large $R_{\rm void}=(192 \pm 15)h^{-1} Mpc$ $(2\sigma)$ supervoid with $\delta \simeq -0.13 \pm 0.03$ centered at $z=0.22\pm0.01$. Such a supervoid, constituting a $\sim3.5 \sigma$ fluctuation in the $\Lambda CDM$ model, is a plausible cause for the Cold Spot.Comment: 4 pages, 2 figures, Proceedings of IAU 306 Symposium: Statistical Challenges in 21st Century Cosmolog

Amyloid-β nanotubes are associated with prion protein-dependent synaptotoxicity

Growing evidence suggests water-soluble, non-fibrillar forms of amyloid-β protein (Aβ) have important roles in Alzheimer’s disease with toxicities mimicked by synthetic Aβ1–42. However, no defined toxic structures acting via specific receptors have been identified and roles of proposed receptors, such as prion protein (PrP), remain controversial. Here we quantify binding to PrP of Aβ1–42 after different durations of aggregation. We show PrP-binding and PrP-dependent inhibition of long-term potentiation (LTP) correlate with the presence of protofibrils. Globular oligomers bind less avidly to PrP and do not inhibit LTP, whereas fibrils inhibit LTP in a PrP-independent manner. That only certain transient Aβ assemblies cause PrP-dependent toxicity explains conflicting reports regarding the involvement of PrP in Aβ-induced impairments. We show that these protofibrils contain a defined nanotubular structure with a previously unidentified triple helical conformation. Blocking the formation of Aβ nanotubes or their interaction with PrP might have a role in treatment of Alzheimer’s disease

Using GAMA to probe the impact of small-scale galaxy physics on nonlinear redshift-space distortions

We present improved modelling of the redshift-space distortions of galaxy clustering that arise from peculiar velocities. We create mock galaxy catalogues in the framework of the halo model, using data from the Bolshoi project. These mock galaxy populations are inserted into the haloes with additional degrees of freedom that govern spatial and kinematical biases of the galaxy populations relative to the dark matter. We explore this generalised halo model with an MCMC algorithm, comparing the predictions to data from the Galaxy And Mass Assembly (GAMA) survey, and thus derive one of the first constraints on the detailed kinematic degrees of freedom for satellite galaxies within haloes. With this approach, the distortions of the redshift-space galaxy autocorrelations can be accounted for down to spatial separations close to 10 kpc, opening the prospect of improved RSD measurements of the perturbation growth rate by the inclusion of data from nonlinear scales.Comment: 19 pages, 10 figures, comments are welcom

One-class support vector machines for detecting population drift in deployed machine learning medical diagnostics

Machine learning (ML) models are increasingly being applied to diagnose and predict disease, but face technical challenges such as population drift, where the training and real-world deployed data distributions differ. This phenomenon can degrade model performance, risking incorrect diagnoses. Current detection methods are limited: not directly measuring population drift and often requiring ground truth labels for new patient data. Here, we propose using a one-class support vector machine (OCSVM) to detect population drift. We trained a OCSVM on the Wisconsin Breast Cancer dataset and tested its ability to detect population drift on simulated data. Simulated data was offset at 0.4 standard deviations of the minimum and maximum values of the radius_mean variable, at three noise levels: 5%, 10% and 30% of the standard deviation; 10,000 records per noise level. We hypothesised that increased noise would correlate with more OCSVM-detected inliers, indicating a sensitivity to population drift. As noise increased, more inliers were detected: 5% (27 inliers), 10% (486), and 30% (851). Therefore, this approach could effectively alert to population drift, supporting safe ML diagnostics adoption. Future research should explore OCSVM monitoring on real-world data, enhance model transparency, investigate complementary statistical and ML methods, and extend applications to other data types

Identification of Anomalous E+A Galaxies in GAMA Using an Isolation Forest

We implement an outlier detection model, an Isolation Forest (iForest), to uncover anomalous objects in the Galaxy and Mass Assembly Fourth Data Release (GAMA DR4). The iForest algorithm is an unsupervised Machine Learning (ML) technique. The data we use is spectroscopic and photometric data from GAMA DR4, which compiles information for over 300,000 objects. We select two samples of galaxies to isolate, high signal-to-noise galaxies, to analyse the iForest’s robustness, and E+A galaxies, to study the extremes of their population. We result in six sub-samples of spectroscopic, photometric and combined data isolations, finding 101 anomalous objects, 50 % of which have not been identified as outliers in other works. We also find a number of fringing errors and false emission lines, displaying the iForest’s potential in detecting these errors. We find anomalous E+A galaxies - that although selected in a ‘normal’ manner using low [OII] and strong H-delta absorption - are still star-forming, with strong H-alpha emission. We propose two solutions to how these E+A galaxies are still star-forming but also question if these galaxies can be truly classified as E+A galaxies. We suggest that small-scale interactions of gas poor objects cause small star bursts, but the radiative pressure when high mass star form, expels the accreting material quicker than it can be accreted. We also suggest that the Jeans limit in our anomalous E+A galaxies is so low that it is simply not possible for O and B class stars to form, but it does not entirely prevent star formation