An empirical test of the theoretical population corrections to the Red Clump absolute magnitude

The mean absolute magnitude of the local red clump (RC) is a very well determined quantity due to the availability of accurate HIPPARCOS parallaxes for several hundred RC stars, potentially allowing it to be used as an accurate extra-galactic distance indicator. Theoretical models predict that the RC mean magnitude has non-linear dependencies on both age and metallicity. This suggests that a population correction, based on the star formation rate (SFR) and age-metallicity relation (AMR) of a particular system, should be applied to the local RC magnitude before it can be compared to the RC in that system in order to make a meaningful distance determination. Using a sample of 8 Galactic open clusters and the GC 47 Tuc, we determine the cluster distances, and hence the RC absolute magnitude in V, I and K, by applying our empirical main sequence fitting method, which utilizes a large sample of local field dwarfs with accurate HIPPARCOS parallaxes. The age and metallicity range of these 9 clusters enable us to make a quantitative assessment of the age and metallicity dependencies of the population corrections predicted by the theoretical models of Girardi & Salaris (2001). We find excellent agreement between the empirical data and the models in all 3 pass-bands, with no statistically significant trends or offsets, thus fully confirming the applicability of the models to single-age, single-metallicity stellar populations. Since, from the models, the population correction is a complicated function of both metallicity and age, if this method is used to derive distances to composite populations, it is essential to have an accurate assessment of the SFR and AMR of the system in question, if errors of several tenths of a magnitude are to be avoided.Comment: 9 pages, 5 figures. MNRAS accepte

Search for dark matter produced in association with bottom or top quarks in √s = 13 TeV pp collisions with the ATLAS detector

A search for weakly interacting massive particle dark matter produced in association with bottom or top quarks is presented. Final states containing third-generation quarks and miss- ing transverse momentum are considered. The analysis uses 36.1 fb−1 of proton–proton collision data recorded by the ATLAS experiment at √s = 13 TeV in 2015 and 2016. No significant excess of events above the estimated backgrounds is observed. The results are in- terpreted in the framework of simplified models of spin-0 dark-matter mediators. For colour- neutral spin-0 mediators produced in association with top quarks and decaying into a pair of dark-matter particles, mediator masses below 50 GeV are excluded assuming a dark-matter candidate mass of 1 GeV and unitary couplings. For scalar and pseudoscalar mediators produced in association with bottom quarks, the search sets limits on the production cross- section of 300 times the predicted rate for mediators with masses between 10 and 50 GeV and assuming a dark-matter mass of 1 GeV and unitary coupling. Constraints on colour- charged scalar simplified models are also presented. Assuming a dark-matter particle mass of 35 GeV, mediator particles with mass below 1.1 TeV are excluded for couplings yielding a dark-matter relic density consistent with measurements

Common, low-frequency, rare, and ultra-rare coding variants contribute to COVID-19 severity

The combined impact of common and rare exonic variants in COVID-19 host genetics is currently insufficiently understood. Here, common and rare variants from whole-exome sequencing data of about 4000 SARS-CoV-2-positive individuals were used to define an interpretable machine-learning model for predicting COVID-19 severity. First, variants were converted into separate sets of Boolean features, depending on the absence or the presence of variants in each gene. An ensemble of LASSO logistic regression models was used to identify the most informative Boolean features with respect to the genetic bases of severity. The Boolean features selected by these logistic models were combined into an Integrated PolyGenic Score that offers a synthetic and interpretable index for describing the contribution of host genetics in COVID-19 severity, as demonstrated through testing in several independent cohorts. Selected features belong to ultra-rare, rare, low-frequency, and common variants, including those in linkage disequilibrium with known GWAS loci. Noteworthily, around one quarter of the selected genes are sex-specific. Pathway analysis of the selected genes associated with COVID-19 severity reflected the multi-organ nature of the disease. The proposed model might provide useful information for developing diagnostics and therapeutics, while also being able to guide bedside disease management. © 2021, The Author(s)