The star S0-2, which orbits the supermassive black hole (SMBH) in our Galaxy with a period of 16 years, provides the strongest constraint on both the mass of the SMBH and the distance to the Galactic center. S0-2 will soon provide the first measurement of relativistic effects near a SMBH. We report the first limits on the binarity of S0-2 from radial velocity (RV) monitoring, which has implications for both understanding its origin and robustness as a probe of the central gravitational field. With 87 RV measurements, which include 12 new observations that we present, we have the requisite data set to look for RV variations from S0-2's orbital model. Using a LombScargle analysis and orbit-fitting for potential binaries, we detect no RV variation beyond S0-2's orbital motion and do not find any significant periodic signal. The lack of a binary companion does not currently distinguish different formation scenarios for S0-2. The upper limit on the mass of a companion star (Mcomp) still allowed by our results has a median upper limit of Mcomp sin i. ≤ 1.6M for periods between 1 and 150 days, the longest period to avoid tidal break-up of the binary. We also investigate the impact of the remaining allowed binary system on the measurement of the relativistic redshift at S0-2's closest approach in 2018. While binary star systems are important to consider for this experiment, we find that plausible binaries for S0-2 will not alter a 5δ detection of the relativistic redshift.

We present an ≈11.5 yr adaptive optics (AO) study of stellar variability and search for eclipsing binaries in the central ∼0.4 pc (∼10″) of the Milky Way nuclear star cluster. We measure the photometry of 563 stars using the Keck II NIRC2 imager (K′-band, λ 0 = 2.124 μm). We achieve a photometric uncertainty floor of Δm K′ ∼ 0.03 (≈3%), comparable to the highest precision achieved in other AO studies. Approximately half of our sample (50% ± 2%) shows variability: 52% ±5% of known early-type young stars and 43% ±4% of known late-type giants are variable. These variability fractions are higher than those of other young, massive star populations or late-type giants in globular clusters, and can be largely explained by two factors. First, our experiment time baseline is sensitive to long-term intrinsic stellar variability. Second, the proper motion of stars behind spatial inhomogeneities in the foreground extinction screen can lead to variability. We recover the two known Galactic center eclipsing binary systems: IRS 16SW and S4-258 (E60). We constrain the Galactic center eclipsing binary fraction of known early-type stars to be at least 2.4% ±1.7%. We find no evidence of an eclipsing binary among the young S-stars nor among the young stellar disk members. These results are consistent with the local OB eclipsing binary fraction. We identify a new periodic variable, S2-36, with a 39.43 days period. Further observations are necessary to determine the nature of this source.

Images obtained with single-conjugate adaptive optics (AO) show spatial variation of the point spread function (PSF) due to both atmospheric anisoplanatism and instrumental aberrations. The poor knowledge of the PSF across the field of view strongly impacts the ability to take full advantage of AO capabilities. The AIROPA project aims to model these PSF variations for the NIRC2 imager at the Keck Observatory. Here, we present the characterization of the instrumental phase aberrations over the entire NIRC2 field of view and we present a metric for quantifying the quality of the calibration, the fraction of variance unexplained (FVU). We used phase diversity measurements obtained on an artificial light source to characterize the variation of the aberrations across the field of view and their evolution with time. We find that there is a daily variation of the wavefront error (RMS of the residuals is 94 nm) common to the whole detector, but the differential aberrations across the field of view are very stable (RMS of the residuals between different epochs is 59 nm). This means that instrumental calibrations need to be monitored often only at the center of the detector, and the much more time-consuming variations across the field of view can be characterized less frequently (most likely when hardware upgrades happen). Furthermore, we tested AIROPA’s instrumental model through real data of the fiber images on the detector. We find that modeling the PSF variations across the field of view improves the FVU metric by 60% and reduces the detection of fake sources by 70%.

The general theory of relativity predicts that a star passing close to a supermassive black hole should exhibit a relativistic redshift. In this study, we used observations of the Galactic Center star S0-2 to test this prediction. We combined existing spectroscopic and astrometric measurements from 1995-2017, which cover S0-2's 16-year orbit, with measurements from March to September 2018, which cover three events during S0-2's closest approach to the black hole. We detected a combination of special relativistic and gravitational redshift, quantified using the redshift parameter ϒ. Our result, ϒ = 0.88 ± 0.17, is consistent with general relativity (ϒ = 1) and excludes a Newtonian model (ϒ = 0) with a statistical significance of 5σ.

Genome-wide association studies (GWAS) of psychiatric disorders have identified multiple genetic associations with such disorders, but better methods are needed to derive the underlying biological mechanisms that these signals indicate. We sought to identify biological pathways in GWAS data from over 60,000 participants from the Psychiatric Genomics Consortium. We developed an analysis framework to rank pathways that requires only summary statistics. We combined this score across disorders to find common pathways across three adult psychiatric disorders: schizophrenia, major depression and bipolar disorder. Histone methylation processes showed the strongest association, and we also found statistically significant evidence for associations with multiple immune and neuronal signaling pathways and with the postsynaptic density. Our study indicates that risk variants for psychiatric disorders aggregate in particular biological pathways and that these pathways are frequently shared between disorders. Our results confirm known mechanisms and suggest several novel insights into the etiology of psychiatric disorders.

Most psychiatric disorders are moderately to highly heritable. The degree to which genetic variation is unique to individual disorders or shared across disorders is unclear. To examine shared genetic etiology, we use genome-wide genotype data from the Psychiatric Genomics Consortium (PGC) for cases and controls in schizophrenia, bipolar disorder, major depressive disorder, autism spectrum disorders (ASD) and attention-deficit/hyperactivity disorder (ADHD). We apply univariate and bivariate methods for the estimation of genetic variation within and covariation between disorders. SNPs explained 17-29% of the variance in liability. The genetic correlation calculated using common SNPs was high between schizophrenia and bipolar disorder (0.68 ± 0.04 s.e.), moderate between schizophrenia and major depressive disorder (0.43 ± 0.06 s.e.), bipolar disorder and major depressive disorder (0.47 ± 0.06 s.e.), and ADHD and major depressive disorder (0.32 ± 0.07 s.e.), low between schizophrenia and ASD (0.16 ± 0.06 s.e.) and non-significant for other pairs of disorders as well as between psychiatric disorders and the negative control of Crohns disease. This empirical evidence of shared genetic etiology for psychiatric disorders can inform nosology and encourages the investigation of common pathophysiologies for related disorders.

Disorders of the brain can exhibit considerable epidemiological comorbidity and often share symptoms, provoking debate about their etiologic overlap. We quantified the genetic sharing of 25 brain disorders from genome-wide association studies of 265,218 patients and 784,643 control participants and assessed their relationship to 17 phenotypes from 1,191,588 individuals. Psychiatric disorders share common variant risk, whereas neurological disorders appear more distinct from one another and from the psychiatric disorders. We also identified significant sharing between disorders and a number of brain phenotypes, including cognitive measures. Further, we conducted simulations to explore how statistical power, diagnostic misclassification, and phenotypic heterogeneity affect genetic correlations. These results highlight the importance of common genetic variation as a risk factor for brain disorders and the value of heritability-based methods in understanding their etiology.