A UV to Mid-IR Study of AGN Selection

We classify the spectral energy distributions (SEDs) of 431,038 sources in the 9 sq. deg Bootes field of the NOAO Deep Wide-Field Survey (NDWFS). There are up to 17 bands of data available per source, including ultraviolet (GALEX), optical (NDWFS), near-IR (NEWFIRM), and mid-infrared (IRAC/MIPS) data, as well as spectroscopic redshifts for ~20,000 objects, primarily from the AGN and Galaxy Evolution Survey (AGES). We fit galaxy, AGN, stellar, and brown dwarf templates to the observed SEDs, which yield spectral classes for the Galactic sources and photometric redshifts and galaxy/AGN luminosities for the extragalactic sources. The photometric redshift precision of the galaxy and AGN samples are sigma/(1+z)=0.040 and sigma/(1+z)=0.169, respectively, with the worst 5% outliers excluded. Based on the reduced chi-squared of the SED fit for each SED model, we are able to distinguish between Galactic and extragalactic sources for sources brighter than I=23.5. We compare the SED fits for a galaxy-only model and a galaxy+AGN model. Using known X-ray and spectroscopic AGN samples, we confirm that SED fitting can be successfully used as a method to identify large populations of AGN, including spatially resolved AGN with significant contributions from the host galaxy and objects with the emission line ratios of "composite" spectra. We also use our results to compare to the X-ray, mid-IR, optical color and emission line ratio selection techniques. For an F-ratio threshold of F>10 we find 16,266 AGN candidates brighter than I=23.5 and a surface density of ~1900 AGN per deg^2.Comment: Submitted to ApJ, 35 pages, 17 figures, 2 table

On Dark Peaks and Missing Mass: A Weak-Lensing Mass Reconstruction of the Merging Cluster System A520

Merging clusters of galaxies are unique in their power to directly probe and place limits on the self-interaction cross-section of dark matter. Detailed observations of several merging clusters have shown the intracluster gas to be displaced from the centroids of dark matter and galaxy density by ram pressure, while the latter components are spatially coincident, consistent with collisionless dark matter. This has been used to place upper limits on the dark matter particle self-interaction cross-section of order 1 sq cm/g. The cluster A520 has been seen as a possible exception. We revisit A520 presenting new Hubble Space Telescope Advanced Camera for Surveys mosaic images and a Magellan image set. We perform a detailed weak-lensing analysis and show that the weak-lensing mass measurements and morphologies of the core galaxy-filled structures are mostly in good agreement with previous works. There is, however, one significant difference: We do not detect the previously claimed "dark core" that contains excess mass with no significant galaxy overdensity at the location of the X-ray plasma. This peak has been suggested to be indicative of a large self-interaction cross-section for dark matter (at least approx 5alpha larger than the upper limit of 0.7 sq cm/g determined by observations of the Bullet Cluster). We find no such indication and instead find that the mass distribution of A520, after subtraction of the X-ray plasma mass, is in good agreement with the luminosity distribution of the cluster galaxies.We conclude that A520 shows no evidence to contradict the collisionless dark matter scenario

Star Formation and AGN Activity in Galaxy Clusters from z=1−2z=1-2: a Multi-wavelength Analysis Featuring HerschelHerschel/PACS

We present a detailed, multi-wavelength study of star formation (SF) and AGN activity in 11 near-infrared (IR) selected, spectroscopically confirmed, massive ($\gtrsim10^{14}\,\rm{M_{\odot}}$) galaxy clusters at $1<z<1.75$. Using new, deep $Herschel$/PACS imaging, we characterize the optical to far-IR spectral energy distributions (SEDs) for IR-luminous cluster galaxies, finding that they can, on average, be well described by field galaxy templates. Identification and decomposition of AGN through SED fittings allows us to include the contribution to cluster SF from AGN host galaxies. We quantify the star-forming fraction, dust-obscured SF rates (SFRs), and specific-SFRs for cluster galaxies as a function of cluster-centric radius and redshift. In good agreement with previous studies, we find that SF in cluster galaxies at $z\gtrsim1.4$ is largely consistent with field galaxies at similar epochs, indicating an era before significant quenching in the cluster cores ($r<0.5\,$Mpc). This is followed by a transition to lower SF activity as environmental quenching dominates by $z\sim1$. Enhanced SFRs are found in lower mass ($10.1< \log \rm{M_{\star}}/\rm{M_{\odot}}<10.8$) cluster galaxies. We find significant variation in SF from cluster-to-cluster within our uniformly selected sample, indicating that caution should be taken when evaluating individual clusters. We examine AGN in clusters from $z=0.5-2$, finding an excess AGN fraction at $z\gtrsim1$, suggesting environmental triggering of AGN during this epoch. We argue that our results $-$ a transition from field-like to quenched SF, enhanced SF in lower mass galaxies in the cluster cores, and excess AGN $-$ are consistent with a co-evolution between SF and AGN in clusters and an increased merger rate in massive haloes at high redshift.Comment: 26 pages, 14 figures, 6 tables with appendix, accepted for publication in the Astrophysical Journa

Influence of Pore Size on Carbon Dioxide Diffusion in Two Isoreticular Metal-Organic Frameworks

The rapid diffusion of molecules in porous materials is critical for numerous applications including separations, energy storage, sensing, and catalysis. A common strategy for tuning guest diffusion rates is to vary the material pore size, although detailed studies that isolate the effect of changing this particular variable are lacking. Here, we begin to address this challenge by measuring the diffusion of carbon dioxide in two isoreticular metal-organic frameworks featuring channels with different diameters, Zn2(dobdc) (dobdc4- = 2,5-dioxidobenzene-1,4-dicarboxylate) and Zn2(dobpdc) (dobpdc4- = 4,4′-dioxidobiphenyl-3,3′-dicarboxylate), using pulsed field gradient NMR spectroscopy. An increase in the pore diameter from 15 Å in Zn2(dobdc) to 22 Å in Zn2(dobpdc) is accompanied by an increase in the self-diffusion of CO2 by a factor of 4 to 6, depending on the gas pressure. Analysis of the diffusion anisotropy in Zn2(dobdc) reveals that the self-diffusion coefficient for motion of CO2 along the framework channels is at least 10000 times greater than for motion between the framework channels. Our findings should aid the design of improved porous materials for a range of applications where diffusion plays a critical role in determining performance

Generalized Transformation for Decorated Spin Models

The paper discusses the transformation of decorated Ising models into an effective \textit{undecorated} spin models, using the most general Hamiltonian for interacting Ising models including a long range and high order interactions. The inverse of a Vandermonde matrix with equidistant nodes $[-s,s]$ is used to obtain an analytical expression of the transformation. This kind of transformation is very useful to obtain the partition function of decorated systems. The method presented by Fisher is also extended, in order to obtain the correlation functions of the decorated Ising models transforming into an effective undecorated Ising models. We apply this transformation to a particular mixed spin-(1/2,1) and (1/2,2) square lattice with only nearest site interaction. This model could be transformed into an effective uniform spin-$S$ square lattice with nearest and next-nearest interaction, furthermore the effective Hamiltonian also include combinations of three-body and four-body interactions, particularly we considered spin 1 and 2.Comment: 16 pages, 4 figure

An early warning risk prediction tool (RECAP-V1) for patients diagnosed with COVID-19: the protocol for a statistical analysis plan

Background: Since the start of the Covid-19 pandemic efforts have been made to develop early warning risk scores to help clinicians decide which patient is likely to deteriorate and require hospitalisation. The RECAP (Remote COVID Assessment in Primary Care) study investigates the predictive risk of hospitalisation, deterioration, and death of patients with confirmed COVID-19, based on a set of parameters chosen through a Delphi process done by clinicians. The study aims to use rich data collected remotely through the use of electronic data templates integrated in the electronic health systems of a number of general practices across the UK to construct accurate predictive models that will use pre-existing conditions and monitoring data of a patient’s clinical parameters such as blood oxygen saturation to make reliable predictions as to the patient’s risk of hospital admission, deterioration, and death. Objective: We outline the statistical methods to build the prediction model to be used in the prioritisation of patients in the primary care setting. The statistical analysis plan for the RECAP study includes as primary outcome the development and validation of the RECAP-V1 prediction model. Such prediction model will be adapted as a three-category risk score split into red (high risk), amber (medium risk), and green (low risk) for any patient with suspected covid-19. The model will predict risk of deterioration, hospitalisation, and death. Methods: After the data has been collected, we will assess the degree of missingness and use a combination of traditional data imputation using multiple imputation by chained equations, as well as more novel machine learning approaches to impute the missing data for the final analysis. For predictive model development we will use multiple logistic regressions to construct the model on a training dataset, as well as validating the model on an independent dataset. The model will also be applied for multiple different datasets to assess both its performance in different patient groups, and applicability for different methods of data collection. Results: As of 5th of May 2021 we have recruited 2280 patients for the main dataset for model development, as well as a further 1741 patients for the validation dataset. Final analysis will commence as soon as data for 2880 are collected. Conclusions: We believe that the methodology for the development of the RECAP V1 prediction model as well as the risk score will provide clinicians with a statistically robust tool to help prioritise Covid-19 patients. Clinical Trial: Trial registration number: NCT0443504