We present an exquisite 30 minute cadence Kepler (K2) light curve of the Type Ia supernova (SN Ia) 2018oh (ASASSN-18bt), starting weeks before explosion, covering the moment of explosion and the subsequent rise, and continuing past peak brightness. These data are supplemented by multi-color Panoramic Survey Telescope (Pan-STARRS1) and Rapid Response System 1 and Cerro Tololo Inter-American Observatory 4 m Dark Energy Camera (CTIO 4-m DECam) observations obtained within hours of explosion. The K2 light curve has an unusual twocomponent shape, where the flux rises with a steep linear gradient for the first few days, followed by a quadratic rise as seen for typical supernovae (SNe)Ia. This "flux excess" relative to canonical SNIa behavior is confirmed in our i-band light curve, and furthermore, SN 2018oh is especially blue during the early epochs. The flux excess peaks 2.14±0.04 days after explosion, has a FWHM of 3.12±0.04 days, a blackbody temperature of T 17, 500 9,000 11,500 =-+ K, a peak luminosity of 4.3 0.2 10 erg s 37 1 ´-, and a total integrated energy of 1.27 0.01 10 erg 43 ´. We compare SN 2018oh to several models that may provide additional heating at early times, including collision with a companion and a shallow concentration of radioactive nickel. While all of these models generally reproduce the early K2 light curve shape, we slightly favor a companion interaction, at a distance of ∼2 10 cm 12 based on our early color measurements, although the exact distance depends on the uncertain viewing angle. Additional confirmation of a companion interaction in future modeling and observations of SN 2018oh would provide strong support for a single-degenerate progenitor system.

On 2018 February 4.41, the All-Sky Automated Survey for SuperNovae (ASAS-SN) discovered ASASSN-18bt in the K2 Campaign 16 field. With a redshift of z=0.01098 and a peak apparent magnitude of B max =14.31, ASASSN-18bt is the nearest and brightest SNe Ia yet observed by the Kepler spacecraft. Here we present the discovery of ASASSN-18bt, the K2 light curve, and prediscovery data from ASAS-SN and the Asteroid Terrestrial-impact Last Alert System. The K2 early-time light curve has an unprecedented 30-minute cadence and photometric precision for an SNIa light curve, and it unambiguously shows a ∼4 day nearly linear phase followed by a steeper rise. Thus, ASASSN-18bt joins a growing list of SNe Ia whose early light curves are not well described by a single power law. We show that a double-power-law model fits the data reasonably well, hinting that two physical processes must be responsible for the observed rise. However, we find that current models of the interaction with a nondegenerate companion predict an abrupt rise and cannot adequately explain the initial, slower linear phase. Instead, we find that existing published models with shallow 56 Ni are able to span the observed behavior and, with tuning, may be able to reproduce the ASASSN-18bt light curve. Regardless, more theoretical work is needed to satisfactorily model this and other early-time SNeIa light curves. Finally, we use Swift X-ray nondetections to constrain the presence of circumstellar material (CSM) at much larger distances and lower densities than possible with the optical light curve. For a constant-density CSM, these nondetections constrain ρ<4.5×10 5 cm −3 at a radius of 4×10 15 cm from the progenitor star. Assuming a wind-like environment, we place mass loss limits of M M 8 10 yr 6 1 <´-☉ for v w =100 km s −1 , ruling out some symbiotic progenitor systems. This work highlights the power of well-sampled early-time data and the need for immediate multiband, high-cadence follow-up for progress in understanding SNeIa.

Supernova (SN) 2018oh (ASASSN-18bt) is the first spectroscopically confirmed Type Ia supernova (SN Ia) observed in the Kepler field. The Kepler data revealed an excess emission in its early light curve, allowing us to place interesting constraints on its progenitor system. Here we present extensive optical, ultraviolet, and nearinfrared photometry, as well as dense sampling of optical spectra, for this object. SN 2018oh is relatively normal in its photometric evolution, with a rise time of 18.3±0.3 days and Δm 15 (B)=0.96±0.03 mag, but it seems to have bluer B−V colors. We construct the "UVOIR" bolometric light curve having a peak luminosity of 1.49×10 43 erg s −1 , from which we derive a nickel mass as 0.55±0.04 M e by fitting radiation diffusion models powered by centrally located 56 Ni. Note that the moment when nickel-powered luminosity starts to emerge is +3.85 days after the first light in the Kepler data, suggesting other origins of the early-time emission, e.g., mixing of 56 Ni to outer layers of the ejecta or interaction between the ejecta and nearby circumstellar material or a nondegenerate companion star. The spectral evolution of SN 2018oh is similar to that of a normal SN Ia but is characterized by prominent and persistent carbon absorption features. The CII features can be detected from the early phases to about 3 weeks after the maximum light, representing the latest detection of carbon ever recorded in an SN Ia. This indicates that a considerable amount of unburned carbon exists in the ejecta of SN 2018oh and may mix into deeper layers.

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With large data collection projects such as the Dark Energy Survey underway, data from distant supernovae (SNe) are becoming increasingly available. As the quantity of information increases, the ability to quickly and accurately classify SNe has become essential. An area of great interest is the development of a strictly photometric classification mechanism. The first step in the advancement of modern photometric classification is the estimation of individual Supernova (SN) light curves. We propose the use of hierarchical Gaussian processes to model light curves. Individual SN light curves are assigned a Gaussian process prior centered at a type-specific mean curve which is also assigned a Gaussian process prior. Properties inherent in this Bayesian non-parametric form of modeling yield flexible yet smooth curves estimates with a unique quantification of the error surrounding these curve estimates. Specifying the hierarchical structure relates individual SN light curves in such a way that borrowing strength across curves is possible. This allows for the estimation of SN light curves in entirety even when data are sparse. Additionally, it also yields a meaningful representation of SN class differences in the form of mean curves. The differences inherent in these mean curves may eventually allow for classification of SNe.