Search for Displaced Photons from Exotic Decays of the Higgs Boson with the ATLAS Detector

A search for displaced photons from exotic decays of the Higgs boson is presented, specifically targeting the relatively weak constraints on the branching ratio of Higgs boson decays to invisible particles imposed by other searches. 139 fb⁻¹ of - collision data at center-of-mass energy √ = 13 TeV, collected between 2015 and 2018, is analyzed. Exploiting the excellent timing performance and longitudinal segmentation of the ATLAS electromagnetic calorimeter, the photon arrival time and extrapolation to the beam axis, or pointing, can be used to extract the displaced photon signature from Standard Model background processes. The background photon timing and pointing distributions are predicted using carefully constructed, data-derived templates. Signal contributions are analyzed in the context of a Gauge-Mediated Supersymmetry Breaking model in which the Higgs boson decays into two, long-lived next-to-lightest supersymmetric particle neutralinos, which each subsequently decay into a photon and a gravitino. Agreement with background is observed with no significant excesses for any signal models considered. Limits at the 95% confidence level are placed on the branching ratio of the Higgs boson to two neutralinos in the context of the various signal models

Joint analysis of psychiatric disorders increases accuracy of risk prediction for schizophrenia, bipolar disorder, and major depressive disorder

\ua9 2015 The Authors. This is an open access article under the CC BY-NC-ND license. Genetic risk prediction has several potential applications in medical research and clinical practice and could be used, for example, to stratify a heterogeneous population of patients by their predicted genetic risk. However, for polygenic traits, such as psychiatric disorders, the accuracy of risk prediction is low. Here we use a multivariate linear mixed model and apply multi-trait genomic best linear unbiased prediction for genetic risk prediction. This method exploits correlations between disorders and simultaneously evaluates individual risk for each disorder. We show that the multivariate approach significantly increases the prediction accuracy for schizophrenia, bipolar disorder, and major depressive disorder in the discovery as well as in independent validation datasets. By grouping SNPs based on genome annotation and fitting multiple random effects, we show that the prediction accuracy could be further improved. The gain in prediction accuracy of the multivariate approach is equivalent to an increase in sample size of 34% for schizophrenia, 68% for bipolar disorder, and 76% for major depressive disorders using single trait models. Because our approach can be readily applied to any number of GWAS datasets of correlated traits, it is a flexible and powerful tool to maximize prediction accuracy. With current sample size, risk predictors are not useful in a clinical setting but already are a valuable research tool, for example in experimental designs comparing cases with high and low polygenic risk

Observation of γγ → ττ in proton-proton collisions and limits on the anomalous electromagnetic moments of the τ lepton

The production of a pair of τ leptons via photon–photon fusion, γγ → ττ, is observed for the f irst time in proton–proton collisions, with a significance of 5.3 standard deviations. This observation is based on a data set recorded with the CMS detector at the LHC at a center-of-mass energy of 13 TeV and corresponding to an integrated luminosity of 138 fb−1. Events with a pair of τ leptons produced via photon–photon fusion are selected by requiring them to be back-to-back in the azimuthal direction and to have a minimum number of charged hadrons associated with their production vertex. The τ leptons are reconstructed in their leptonic and hadronic decay modes. The measured fiducial cross section of γγ → ττ is σfid obs = 12.4+3.8 −3.1 fb. Constraints are set on the contributions to the anomalous magnetic moment (aτ) and electric dipole moments (dτ) of the τ lepton originating from potential effects of new physics on the γττ vertex: aτ = 0.0009+0.0032 −0.0031 and |dτ| < 2.9×10−17ecm (95% confidence level), consistent with the standard model

Large-scale genome-wide association analysis of bipolar disorder identifies a new susceptibility locus near ODZ4.

International audienceWe conducted a combined genome-wide association study (GWAS) of 7,481 individuals with bipolar disorder (cases) and 9,250 controls as part of the Psychiatric GWAS Consortium. Our replication study tested 34 SNPs in 4,496 independent cases with bipolar disorder and 42,422 independent controls and found that 18 of 34 SNPs had P < 0.05, with 31 of 34 SNPs having signals with the same direction of effect (P = 3.8 × 10(-7)). An analysis of all 11,974 bipolar disorder cases and 51,792 controls confirmed genome-wide significant evidence of association for CACNA1C and identified a new intronic variant in ODZ4. We identified a pathway comprised of subunits of calcium channels enriched in bipolar disorder association intervals. Finally, a combined GWAS analysis of schizophrenia and bipolar disorder yielded strong association evidence for SNPs in CACNA1C and in the region of NEK4-ITIH1-ITIH3-ITIH4. Our replication results imply that increasing sample sizes in bipolar disorder will confirm many additional loci

Searches for new physics in CMS in events with jets, leptons and photons in the final state

Recent results are presented from the CMS Collaboration on four searches for beyond the Standard Model (BSM) physics in prompt, R-parity conserving final states that include jets, leptons, and/or photons. These searches explore previously uncovered regions of BSM parameter space involving unique signatures, a wide range of motivating models, and a powerful set of novel analysis techniques. Concepts such as low $p_\mathrm{T}^\mathrm{miss}$ signatures, stealth supersymmetry (SUSY), gauge-mediated SUSY breaking (GMSB), dark photon and dark Higgs models, and data scouting techniques are all covered in the featured analyses. In all cases, no significant excesses above SM expectations are observed, and limits are placed on various model interpretations, improving greatly upon previous limits in many portions of the parameter space or, in some cases, establishing the first-ever sets of such limits

ATLAS Liquid Argon Calorimeters Operation and Data Quality During the 2018 Run Including Demonstrator Data Taking

Poster for LHCC poster session (Feb. 2019

ATLAS LAr Calorimeter Performance in LHC Run-2

Liquid argon (LAr) sampling calorimeters are employed by ATLAS for all electromagnetic calorimetry in the pseudo-rapidity region |η| < 3.2, and for hadronic and forward calorimetry in the region from |η| = 1.5 to |η| = 4.9. In the first LHC run a total luminosity of 27 fb−1 has been collected at center-of-mass energies of 7-8 TeV. After detector consolidation during a long shutdown, Run-2 started in 2015 and about 150fb-1 of data at a center-of-mass energy of 13 TeV have been recorded. In order to realize the level-1 acceptance rate of 100 kHz in Run-2 data taking, the number of read-out samples recorded and used for the energy and the time measurement has been modified from five to four while keeping the expected performance. The well calibrated and highly granular LAr Calorimeter reached its design values both in energy measurement as well as in direction resolution. This contribution will give an overview of the detector operation, hardware improvements, changes in the monitoring and data quality procedures, to cope with increased pileup, as well as the achieved performance, including the calibration and stability of the electromagnetic scale, noise level, response uniformity and time resolution

ATLAS LAr Calorimeter Performance in LHC Run-2

Liquid argon (LAr) sampling calorimeters are employed by ATLAS for all electromagnetic calorimetry in the pseudo-rapidity region $|\eta|$ < 3.2, and for hadronic and forward calorimetry in the region from $|\eta|$ = 1.5 to $|\eta|$ = 4.9. In the first LHC run a total luminosity of 27~fb$^{-1}$ has been collected at center-of-mass energies of 7-8 TeV. After detector consolidation during a long shutdown, Run~2 started in 2015 and about 150~fb$^{-1}$ of data at a center-of-mass energy of 13 TeV have been recorded. In order to realize the level-1 acceptance rate of 100 kHz in Run~2 data taking, the number of read-out samples recorded and used for the energy and the time measurement has been modified from five to four while keeping the expected performance. The well calibrated and highly granular LAr Calorimeter reached its design values both in energy measurement as well as in direction resolution. This contribution will give an overview of the detector operation, hardware improvements, changes in the monitoring and data quality procedures, to cope with increased pileup, as well as the achieved performance, including the calibration and stability of the electromagnetic scale, noise level, response uniformity and time resolution

Giant epidermal inclusion cyst masquerading as a soft tissue sarcoma

Epidermal inclusion cysts (EIC) are common lesions formed by the invagination and cystic expansion of the epidermis or hair follicle. We present an unusual case of an epidermal inclusion cyst masquerading as a soft tissue sarcoma based on initial clinical presentation and radiographic findings. Mischaracterization of this type of lesion may lead to unnecessary diagnostic modalities or invasive, overly-aggressive surgery

finsyncR - An R Package to synchronize fish and invertebrate datasets from federal sources in the United States, R package version 1.0.0, February 22, 2024

finsyncR (fish and invertebrate synchronizer in R) is a data management package that integrates and processes national-level aquatic biomonitoring datasets in the U.S., with a focus on fish and macroinvertebrates sampled in rivers and streams. The package streamlines the process of retrieving and harmonizing these data, improving access to cleaned data and making the application these data straightforward for researchers. The sources of data for this package are the United States Environmental Protection Agency’s (USEPA) National Aquatic Resource Surveys (NARS), namely the National River and Streams Assessment (NRSA), and United States Geological Survey’s (USGS) BioData."finsyncR_1.0.0.tar" contains the built R package that can be downloaded, then installed as a "source" package via R."finsyncR-main.zip" contains the zipped version of the finsyncR Github page (github.com/USEPA/finsyncR) as of February 22, 2024.</p