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We present an extensive Hubble Space Telescope rest-frame UV imaging study of the locations of Type I superluminous supernovae (SLSNe) within their host galaxies. The sample includes 65 SLSNe with detected host galaxies in the redshift rangez≈ 0.05–2. Using precise astrometric matching with SN images, we determine the distributions of the physical and host-normalized offsets relative to the host centers, as well as the fractional flux distribution relative to the underlying UV light distributions. We find that the host-normalized offsets of SLSNe roughly track an exponential disk profile, but exhibit an overabundance of sources with large offsets of 1.5–4 times their hosts' half-light radii. The SLSNe normalized offsets are systematically larger than those of long gamma-ray bursts (LGRBs), and even Type Ib/c and Type II SNe. Furthermore, we find from a Monte Carlo procedure that about${37}_{-8}^{+6}\%$of SLSNe occur in the dimmest regions of their host galaxies, with a median fractional flux value of 0.16, in stark contrast to LGRBs and Type Ib/c and Type II SNe. We do not detect any significant trends in the locations of SLSNe as a function of redshift, or as a function of explosion and magnetar engine parameters inferred from modeling of their optical light curves. The significant difference in SLSN locations compared to LGRBs (and normal core-collapse SNe) suggests that at least some of their progenitors follow a different evolutionary path. We speculate that SLSNe arise from massive runaway stars from disrupted binary systems, with velocities of ∼10²km s⁻¹.

 

Identifying the sites of r-process nucleosynthesis, a primary mechanism of heavy element production, is a key goal of astrophysics. The discovery of the brightest gamma-ray burst (GRB) to date, GRB 221009A, presented an opportunity to spectroscopically test the idea that r-process elements are produced following the collapse of rapidly rotating massive stars. Here we present James Webb Space Telescope observations of GRB 221009A obtained +168 and +170 rest-frame days after the gamma-ray trigger, and demonstrate that they are well described by a SN 1998bw-like supernova (SN) and power-law afterglow, with no evidence for a component from r-process emission. The SN, with a nickel mass of approximately 0.09 M_⊙, is only slightly fainter than the brightness of SN 1998bw at this phase, which indicates that the SN is not an unusual GRB-SN. This demonstrates that the GRB and SN mechanisms are decoupled and that highly energetic GRBs are not likely to produce significant quantities of r-process material, which leaves open the question of whether explosions of massive stars are key sources of r-process elements. Moreover, the host galaxy of GRB 221009A has a very low metallicity of approximately 0.12 Z_⊙and strong H₂emission at the explosion site, which is consistent with recent star formation, hinting that environmental factors are responsible for its extreme energetics.

 

We present a detailed compilation and analysis of the X-ray phase space of low- to intermediate-redshift (0 ≤z≤ 1) transients that consolidates observed light curves (and theory where necessary) for a large variety of classes of transient/variable phenomena in the 0.3–10 keV energy band. We include gamma-ray burst afterglows, supernovae, supernova shock breakouts and shocks interacting with the environment, tidal disruption events and active galactic nuclei, fast blue optical transients, cataclysmic variables, magnetar flares/outbursts and fast radio bursts, cool stellar flares, X-ray binary outbursts, and ultraluminous X-ray sources. Our overarching goal is to offer a comprehensive resource for the examination of these ephemeral events, extending the X-ray duration–luminosity phase space (DLPS) to show luminosity evolution. We use existing observations (both targeted and serendipitous) to characterize the behavior of various transient/variable populations. Contextualizing transient signals in the larger DLPS serves two primary purposes: to identify areas of interest (i.e., regions in the parameter space where one would expect detections, but in which observations have historically been lacking), and to provide initial qualitative guidance in classifying newly discovered transient signals. We find that while the most luminous (largely extragalactic) and least luminous (largely Galactic) part of the phase space is well populated att> 0.1 days, intermediate-luminosity phenomena (L_X= 10³⁴–10⁴²erg s⁻¹) represent a gap in the phase space. We thus identifyL_X= 10³⁴–10⁴²erg s⁻¹andt= 10⁻⁴to 0.1 days as a key discovery phase space in transient X-ray astronomy.

 

Abstract We present 1.3 mm (230 GHz) observations of the recent and nearby Type II supernova, SN 2023ixf, obtained with the Submillimeter Array (SMA) at 2.6–18.6 days after explosion. The observations were obtained as part the SMA Large Program, POETS (Pursuit of Extragalactic Transients with the SMA). We do not detect any emission at the location of SN 2023ixf, with the deepest limits of L ν (230 GHz) ≲ 8.6 × 10 25 erg s −1 Hz −1 at 2.7 and 7.7 days, and L ν (230 GHz) ≲ 3.4 × 10 25 erg s −1 Hz −1 at 18.6 days. These limits are about a factor of 2 times dimmer than the millimeter emission from SN 2011dh (IIb), about 1 order of magnitude dimmer compared to SN 1993J (IIb) and SN 2018ivc (IIL), and about 30 times dimmer than the most luminous nonrelativistic SNe in the millimeter band (Type IIb/Ib/Ic). Using these limits in the context of analytical models that include synchrotron self-absorption and free–free absorption, we place constraints on the proximate circumstellar medium around the progenitor star, to a scale of ∼2 × 10 15 cm, excluding the range M ̇ ∼ few × 10 − 6 − 10 − 2 M ⊙ yr −1 (for a wind velocity, v w = 115 km s −1 , and ejecta velocity, v ej ∼ (1 − 2) × 10 4 km s −1 ). These results are consistent with an inference of the mass-loss rate based on optical spectroscopy (∼2 × 10 −2 M ⊙ yr −1 for v w = 115 km s −1 ), but are in tension with the inference from hard X-rays (∼7 × 10 −4 M ⊙ yr −1 for v w = 115 km s −1 ). This tension may be alleviated by a nonhomogeneous and confined CSM, consistent with results from high-resolution optical spectroscopy. 

In 2019 November, we began operating Finding Luminous and Exotic Extragalactic Transients (FLEET), a machine-learning algorithm designed to photometrically identify Type I superluminous supernovae (SLSNe) in transient alert streams. Through this observational campaign, we spectroscopically classified 21 of the 50 SLSNe identified worldwide between 2019 November and 2022 January. Based on our original algorithm, we anticipated that FLEET would achieve a purity of about 50% for transients with a probability of being an SLSN,P(SLSN-I) > 0.5; the true on-sky purity we obtained is closer to 80%. Similarly, we anticipated FLEET could reach a completeness of about 30%, and we indeed measure an upper limit on the completeness of ≲33%. Here we present FLEET 2.0, an updated version of FLEET trained on 4780 transients (almost three times more than FLEET 1.0). FLEET 2.0 has a similar predicted purity to FLEET 1.0 but outperforms FLEET 1.0 in terms of completeness, which is now closer to ≈40% for transients withP(SLSN-I) > 0.5. Additionally, we explore the possible systematics that might arise from the use of FLEET for target selection. We find that the population of SLSNe recovered by FLEET is mostly indistinguishable from the overall SLSN population in terms of physical and most observational parameters. We provide FLEET as an open source package on GitHub: https://github.com/gmzsebastian/FLEET.

 

We present an expansion of FLEET, a machine-learning algorithm optimized to select transients that are most likely tidal disruption events (TDEs). FLEET is based on a random forest algorithm trained on both the light curves and host galaxy information of 4779 spectroscopically classified transients. We find that for transients with a probability of being a TDE,P(TDE) > 0.5, we can successfully recover TDEs with ≈40% completeness and ≈30% purity when using their first 20 days of photometry or a similar completeness and ≈50% purity when including 40 days of photometry, an improvement of almost 2 orders of magnitude compared to random selection. Alternatively, we can recover TDEs with a maximum purity of ≈80% and a completeness of ≈30% when considering only transients withP(TDE) > 0.8. We explore the use of FLEET for future time-domain surveys such as the Legacy Survey of Space and Time on the Vera C. Rubin Observatory (Rubin) and the Nancy Grace Roman Space Telescope (Roman). We estimate that ∼10⁴well-observed TDEs could be discovered every year by Rubin and ∼200 TDEs by Roman. Finally, we run FLEET on the TDEs from our Rubin survey simulation and find that we can recover ∼30% of them at redshiftz< 0.5 withP(TDE) > 0.5, or ∼3000 TDEs yr^–1that FLEET could uncover from the Rubin stream. We have demonstrated that we will be able to run FLEET on Rubin photometry as soon as this survey begins. FLEET is provided as an open source package on GitHub: https://github.com/gmzsebastian/FLEET.

 

We present the discovery of the Type II supernova SN 2023ixf in M101 and follow-up photometric and spectroscopic observations, respectively, in the first month and week of its evolution. Our discovery was made within a day of estimated first light, and the following light curve is characterized by a rapid rise (≈5 days) to a luminous peak (M_V≈ − 18.2 mag) and plateau (M_V≈ − 17.6 mag) extending to 30 days with a fast decline rate of ≈0.03 mag day⁻¹. During the rising phase,U−Vcolor shows blueward evolution, followed by redward evolution in the plateau phase. Prominent flash features of hydrogen, helium, carbon, and nitrogen dominate the spectra up to ≈5 days after first light, with a transition to a higher ionization state in the first ≈2 days. Both theU−Vcolor and flash ionization states suggest a rise in the temperature, indicative of a delayed shock breakout inside dense circumstellar material (CSM). From the timescales of CSM interaction, we estimate its compact radial extent of ∼(3–7) × 10¹⁴cm. We then construct numerical light-curve models based on both continuous and eruptive mass-loss scenarios shortly before explosion. For the continuous mass-loss scenario, we infer a range of mass-loss history with 0.1–1.0M_⊙yr⁻¹in the final 2−1 yr before explosion, with a potentially decreasing mass loss of 0.01–0.1M_⊙yr⁻¹in ∼0.7–0.4 yr toward the explosion. For the eruptive mass-loss scenario, we favor eruptions releasing 0.3–1M_⊙of the envelope at about a year before explosion, which result in CSM with mass and extent similar to the continuous scenario. We discuss the implications of the available multiwavelength constraints obtained thus far on the progenitor candidate and SN 2023ixf to our variable CSM models.

 

Stripped-envelope core-collapse supernovae can be divided into two broad classes: the common Type Ib/c supernovae (SNe Ib/c), powered by the radioactive decay of⁵⁶Ni, and the rare superluminous supernovae (SLSNe), most likely powered by the spin-down of a magnetar central engine. Up to now, the intermediate regime between these two populations has remained mostly unexplored. Here, we present a comprehensive study of 40luminous supernovae(LSNe), SNe with peak magnitudes ofM_r= −19 to −20 mag, bound by SLSNe on the bright end and by SNe Ib/c on the dim end. Spectroscopically, LSNe appear to form a continuum between Type Ic SNe and SLSNe. Given their intermediate nature, we model the light curves of all LSNe using a combined magnetar plus radioactive decay model and find that they are indeed intermediate, not only in terms of their peak luminosity and spectra, but also in their rise times, power sources, and physical parameters. We subclassify LSNe into distinct groups that are either as fast evolving as SNe Ib/c or as slow evolving as SLSNe, and appear to be either radioactively or magnetar powered, respectively. Our findings indicate that LSNe are powered by either an overabundant production of⁵⁶Ni or by weak magnetar engines, and may serve as the missing link between the two populations.

 

With the upcoming Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST), it is expected that only ∼0.1% of all transients will be classified spectroscopically. To conduct studies of rare transients, such as Type I superluminous supernovae (SLSNe), we must instead rely on photometric classification. In this vein, here we carry out a pilot study of SLSNe from the Pan-STARRS1 Medium Deep Survey (PS1-MDS), classified photometrically with ourSuperRAENNandSuperphotalgorithms. We first construct a subsample of the photometric sample using a list of simple selection metrics designed to minimize contamination and ensure sufficient data quality for modeling. We then fit the multiband light curves with a magnetar spin-down model using the Modular Open-Source Fitter for Transients (MOSFiT). Comparing the magnetar engine and ejecta parameter distributions of the photometric sample to those of the PS1-MDS spectroscopic sample and a larger literature spectroscopic sample, we find that these samples are consistent overall, but that the photometric sample extends to slower spins and lower ejecta masses, which correspond to lower-luminosity events, as expected for photometric selection. While our PS1-MDS photometric sample is still smaller than the overall SLSN spectroscopic sample, our methodology paves the way for an orders-of-magnitude increase in the SLSN sample in the LSST era through photometric selection and study.