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With the advent of high-cadence, all-sky automated surveys, supernovae (SNe) are now discovered closer than ever to their dates of explosion. However, young premaximum light follow-up spectra of Type Ic SNe (SNe Ic), probably arising from the most-stripped massive stars, remain rare despite their importance. In this Letter, we present a set of 49 optical spectra observed with the Las Cumbres Observatory through the Global Supernova Project for 6 SNe Ic, including a total of 17 premaximum spectra, of which 8 are observed more than a week beforeV-band maximum light. This data set increases the total number of publicly available premaximum-light SN Ic spectra by 25%, and we provide publicly available SNID templates that will significantly aid in the fast identification of young SNe Ic in the future. We present a detailed analysis of these spectra, including Feii5169 velocity measurements, Oi7774 line strengths, and continuum shapes. We compare our results to published samples of stripped SNe in the literature and find one SN in our sample that stands out. SN 2019ewu has a unique combination of features for an SN Ic: an extremely blue continuum, high absorption velocities, a P Cygni–shaped feature almost 2 weeks before maximum light that TARDIS radiative transfer modeling attributes to Ciirather than Hα, and weak or nonexistent Oi7774 absorption feature until maximum light.

 

Few published ultraviolet (UV) spectra exist for stripped-envelope supernovae and none to date for broad-lined Type Ic supernovae (SNe Ic-bl). These objects have extremely high ejecta velocities and are the only supernova type directly linked to gamma-ray bursts (GRBs). Here we present two epochs of HST/STIS spectra of the SN Ic-bl 2014ad, the first UV spectra for this class. We supplement this with 26 new epochs of ground-based optical spectra, augmenting a rich spectral time series. The UV spectra do not show strong features and are consistent with broadened versions of other SN Ic spectra observed in the UV. We measure Feii5169 Å velocities and show that SN 2014ad has even higher ejecta velocities than most SNe Ic both with and without observed GRBs. We construct models of the SN 2014ad UV+optical spectra usingtardis, a 1D Monte Carlo radiative-transfer spectral synthesis code. The models fit the data well at multiple epochs in the optical but underestimate the flux in the UV, likely due to simplifying assumptions. We find that high densities at high velocities are needed to reproduce the spectra, with ∼3M_⊙of material atv> 22,000 km s⁻¹, assuming spherical symmetry. Our nebular line fits suggest a steep density profile at low velocities. Together, these results imply a higher total ejecta mass than estimated from previous light-curve analysis and expected from theory. This may be reconciled by a flattening of the density profile at low velocity and extra emission near the center of the ejecta.

 

We present the densely sampled early light curve of the Type II supernova (SN) 2023ixf, first observed within hours of explosion in the nearby Pinwheel Galaxy (Messier 101; 6.7 Mpc). Comparing these data to recently updated models of shock-cooling emission, we find that the progenitor likely had a radius of 410 ± 10R_⊙. Our estimate is model dependent but consistent with a red supergiant. These models provide a good fit to the data starting about 1 day after the explosion, despite the fact that the classification spectrum shows signatures of circumstellar material around SN 2023ixf during that time. Photometry during the first day after the explosion, provided almost entirely by amateur astronomers, does not agree with the shock-cooling models or a simple power-law rise fit to data after 1 day. We consider the possible causes of this discrepancy, including precursor activity from the progenitor star, circumstellar interaction, and emission from the shock before or after it breaks out of the stellar surface. The very low luminosity (−11 mag >M> −14 mag) and short duration of the initial excess lead us to prefer a scenario related to prolonged emission from the SN shock traveling through the progenitor system.

 

We present optical, infrared, ultraviolet, and radio observations of SN 2022xkq, an underluminous fast-declining Type Ia supernova (SN Ia) in NGC 1784 (D≈ 31 Mpc), from <1 to 180 days after explosion. The high-cadence observations of SN 2022xkq, a photometrically transitional and spectroscopically 91bg-like SN Ia, cover the first days and weeks following explosion, which are critical to distinguishing between explosion scenarios. The early light curve of SN 2022xkq has a red early color and exhibits a flux excess that is more prominent in redder bands; this is the first time such a feature has been seen in a transitional/91bg-like SN Ia. We also present 92 optical and 19 near-infrared (NIR) spectra, beginning 0.4 days after explosion in the optical and 2.6 days after explosion in the NIR. SN 2022xkq exhibits a long-lived Ci1.0693μm feature that persists until 5 days post-maximum. We also detect Ciiλ6580 in the pre-maximum optical spectra. These lines are evidence for unburnt carbon that is difficult to reconcile with the double detonation of a sub-Chandrasekhar mass white dwarf. No existing explosion model can fully explain the photometric and spectroscopic data set of SN 2022xkq, but the considerable breadth of the observations is ideal for furthering our understanding of the processes that produce faint SNe Ia.

 

Software is a critical part of modern research, and yet there are insufficient mechanisms in the scholarly ecosystem to acknowledge, cite, and measure the impact of research software. The majority of academic fields rely on a one-dimensional credit model whereby academic articles (and their associated citations) are the dominant factor in the success of a researcher's career. In the petabyte era of astronomical science, citing software and measuring its impact enables academia to retain and reward researchers that make significant software contributions. These highly skilled researchers must be retained to maximize the scientific return from petabyte-scale datasets. Evolving beyond the one-dimensional credit model requires overcoming several key challenges, including the current scholarly ecosystem and scientific culture issues. This white paper will present these challenges and suggest practical solutions for elevating the role of software as a product of the research enterprise.