Our understanding of the properties and demographics of exoplanets critically relies on our ability to determine the fundamental properties of their host stars. The advent of Gaia and large spectroscopic surveys has now made it possible, in principle, to infer the properties of individual stars, including most exoplanet hosts, to very high precision. However, we show that, in practice, such analyses are limited by uncertainties in both the fundamental scale and our models of stellar evolution, even for stars similar to the Sun. For example, we show that current uncertainties on measured interferometric angular diameters and bolometric fluxes set a systematic uncertainty floor of ≈2.4% in temperature, ≈2.0% in luminosity, and ≈4.2% in radius. Comparisons between widely available model grids suggest uncertainties of order ≈5% in mass and ≈20% in age for main-sequence and subgiant stars. While the radius uncertainties are roughly constant over this range of stars, the model-dependent uncertainties are a complex function of luminosity, temperature, and metallicity. We provide open-source software for approximating these uncertainties for individual targets and discuss strategies for reducing these uncertainties in the future.

We use models of stellar angular momentum evolution to determine ages for ∼ 500 stars in the APOGEE-Kepler Cool Dwarfs sample. We focus on lower main-sequence stars, where other age-dating tools become ineffective. Our age distributions are compared to those derived from asteroseismic and giant samples and solar analogs. We are able to recover gyrochronological ages for old, lower-mainsequence stars, a remarkable improvement over prior work in hotter stars. Under our model assumptions, our ages have a median relative uncertainty of 14%, comparable to the age precision inferred for more massive stars using traditional methods. We investigate trends of galactic α-enhancement with age, finding evidence of a detection threshold between the age of the oldest α-poor stars and that of the bulk α-rich population. We argue that gyrochronology is an effective tool reaching ages of 10-12 Gyr in K-and early M-dwarfs. Finally, we present the first effort to quantify the impact of detailed abundance patterns on rotational evolution. We estimate a ∼ 15% bias in age for cool, α-enhanced (+ 0.4 dex) stars when standard solar-abundance-pattern rotational models are used for age inference, rather than models that appropriately account for α-enrichment.

We used a convolutional neural network to infer stellar rotation periods from a set of synthetic light curves simulated with realistic spot-evolution patterns. We convolved these simulated light curves with real TESS light curves containing minimal intrinsic astrophysical variability to allow the network to learn TESS systematics and estimate rotation periods despite them. In addition to periods, we predict uncertainties via heteroskedastic regression to estimate the credibility of the period predictions. In the most credible half of the test data, we recover 10% accurate periods for 46% of the targets, and 20% accurate periods for 69% of the targets. Using our trained network, we successfully recover periods of real stars with literature rotation measurements, even past the 13.7 day limit generally encountered by TESS rotation searches using conventional period-finding techniques. Our method also demonstrates resistance to half-period aliases. We present the neural network and simulated training data, and introduce the software butterpy used to synthesize the light curves using realistic starspot evolution.

We report the discovery of TOI-561, a multiplanet system in the galactic thick disk that contains a rocky, ultrashort-period planet. This bright (V = 10.2) star hosts three small transiting planets identified in photometry from the NASA TESS mission: TOI-561 b (TOI-561.02,

We present a sample of 446 galaxy pairs constructed using the cosmological simulation IllustrisTNG-100 at z = 0, with M FoF, dm = 10 11 − 10 13.5 M . We produce ideal mock SDSS g-band images of all pairs to test the reliability of visual classification schema employed to produce samples of interacting galaxies. We visually classify each image as interacting or not based on the presence of a close neighbour, the presence of stellar debris fields, disturbed discs, and/or tidal features. By inspecting the trajectories of the pairs, we determine that these indicators correctly identify interacting galaxies ∼45% of the time. We subsequently split the sample into the visually identified interacting pairs (VIP; 38 pairs) and those which are interacting but are not visually identified (nonVIP; 47 pairs). We find that VIP have undergone a close passage nearly twice as recently as the nonVIP, and typically have higher stellar masses.Further, the VIP sit in dark matter haloes that are approximately 2.5 times as massive, in environments nearly 2 times as dense, and are almost a factor of 10 more affected by the tidal forces of their surroundings than the nonVIP. These factors conspire to increase the observability of tidal features and disturbed morphologies, making the VIP more likely to be identified. Thus, merger rate calculations which rely on stellar morphologies are likely to be significantly biased toward massive galaxy pairs which have recently undergone a close passage.