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  1. Abstract

    The unknown cause of the correlation between Type Ia supernova (SN Ia) Hubble residuals and their host-galaxy masses (the “mass step”) may bias cosmological parameter measurements. To better understand the mass step, we develop a SALT3 light-curve model for SN cosmology that uses the host-galaxy masses of 296 low-redshift SNe Ia to derive a spectral energy distribution–host-galaxy mass relationship. The resulting model has larger CaiiH and K, Caiinear-infrared triplet, and Siiiequivalent widths for SNe in low-mass host galaxies at 2.2–2.7σsignificance; this indicates higher explosion energies per unit mass in low-mass-hosted SNe. The model has phase-dependent changes in SN Ia colors as a function of host mass, indicating intrinsic differences in mean broadband light curves. Although the model provides a better fit to the SN data overall, it does not substantially reduce data–model residuals for a typical light curve in our sample nor does it significantly reduce Hubble residual dispersion. This is because we find that previous SALT models parameterized most host-galaxy dependencies with their first principal component, although they failed to model some significant spectral variations. Our new model is luminosity and cosmology independent, and applying it to data reduces the mass step by 0.021 ± 0.002 mag (uncertainty accounts for correlated data sets); these results indicate that ∼35% of the mass step can be attributed to luminosity-independent effects. This SALT model version could be trained using alternative host-galaxy properties and at different redshifts, and therefore will be a tool for understanding redshift-dependent correlations between SNe Ia and their host properties as well as their impact on cosmological parameter measurements.

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    For the past decade, SALT2 has been the most common model used to fit Type Ia supernova (SN Ia) light curves for dark energy analyses. Recently, the SALT3 model was released, which upgraded a number of model features but has not yet been used for measurements of dark energy. Here, we evaluate the impact of switching from SALT2 to SALT3 for a SN cosmology analysis. We train SALT2 and SALT3 on an identical training sample of 1083 well-calibrated Type Ia supernovae, ensuring that any differences found come from the underlying model framework. We publicly release the results of this training (the SALT ‘surfaces’). We then run a cosmology analysis on the public Dark Energy Survey 3-Yr Supernova data sample (DES-SN3YR), and on realistic simulations of those data. We provide the first estimate of the SN + CMB systematic uncertainty arising from the choice of SALT model framework (i.e. SALT2 versus SALT3), Δw  = + 0.001 ± 0.005 – a negligible effect at the current level of dark energy analyses. We also find that the updated surfaces are less sensitive to photometric calibration uncertainties than previous SALT2 surfaces, with the average spectral energy density dispersion reduced by a factor of two over optical wavelengths. This offers an opportunity to reduce the contribution of calibration errors to SN cosmology uncertainty budgets.

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  3. Abstract We present extensive optical photometry of the afterglow of GRB 221009A. Our data cover 0.9–59.9 days from the time of Swift and Fermi gamma-ray burst (GRB) detections. Photometry in rizy -band filters was collected primarily with Pan-STARRS and supplemented by multiple 1–4 m imaging facilities. We analyzed the Swift X-ray data of the afterglow and found a single decline rate power law f ( t ) ∝ t −1.556±0.002 best describes the light curve. In addition to the high foreground Milky Way dust extinction along this line of sight, the data favor additional extinction to consistently model the optical to X-ray flux with optically thin synchrotron emission. We fit the X-ray-derived power law to the optical light curve and find good agreement with the measured data up to 5−6 days. Thereafter we find a flux excess in the riy bands that peaks in the observer frame at ∼20 days. This excess shares similar light-curve profiles to the Type Ic broad-lined supernovae SN 2016jca and SN 2017iuk once corrected for the GRB redshift of z = 0.151 and arbitrarily scaled. This may be representative of an SN emerging from the declining afterglow. We measure rest-frame absolute peak AB magnitudes of M g = −19.8 ± 0.6 and M r = − 19.4 ± 0.3 and M z = −20.1 ± 0.3. If this is an SN component, then Bayesian modeling of the excess flux would imply explosion parameters of M ej = 7.1 − 1.7 + 2.4 M ⊙ , M Ni = 1.0 − 0.4 + 0.6 M ⊙ , and v ej = 33,900 − 5700 + 5900 km s −1 , for the ejecta mass, nickel mass, and ejecta velocity respectively, inferring an explosion energy of E kin ≃ 2.6–9.0 × 10 52 erg. 
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    Free, publicly-accessible full text available March 1, 2024
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  6. Massive black holes (BHs) at the centres of massive galaxies are ubiquitous. The population of BHs within dwarf galaxies, on the other hand, is evasive. Dwarf galaxies are thought to harbour BHs with proportionally small masses, including intermediate mass BHs, with masses 102 more » « less
  7. Abstract We present multiwavelength observations of the Type II SN 2020pni. Classified at ∼1.3 days after explosion, the object showed narrow (FWHM intensity <250 km s −1 ) recombination lines of ionized helium, nitrogen, and carbon, as typically seen in flash-spectroscopy events. Using the non-LTE radiative transfer code CMFGEN to model our first high-resolution spectrum, we infer a progenitor mass-loss rate of M ̇ = ( 3.5 – 5.3 ) × 10 − 3 M ⊙ yr −1 (assuming a wind velocity of v w = 200 km s −1 ), estimated at a radius of R in = 2.5 × 10 14 cm. In addition, we find that the progenitor of SN 2020pni was enriched in helium and nitrogen (relative abundances in mass fractions of 0.30–0.40 and 8.2 × 10 −3 , respectively). Radio upper limits are also consistent with dense circumstellar material (CSM) and a mass-loss rate of M ̇ > 5 × 10 − 4 M ☉ yr − 1 . During the initial 4 days after first light, we also observe an increase in velocity of the hydrogen lines (from ∼250 to ∼1000 km s −1 ), suggesting complex CSM. The presence of dense and confined CSM, as well as its inhomogeneous structure, indicates a phase of enhanced mass loss of the progenitor of SN 2020pni during the last year before explosion. Finally, we compare SN 2020pni to a sample of other shock-photoionization events. We find no evidence of correlations among the physical parameters of the explosions and the characteristics of the CSM surrounding the progenitors of these events. This favors the idea that the mass loss experienced by massive stars during their final years could be governed by stochastic phenomena and that, at the same time, the physical mechanisms responsible for this mass loss must be common to a variety of different progenitors. 
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  8. Abstract We present the Young Supernova Experiment Data Release 1 (YSE DR1), comprised of processed multicolor PanSTARRS1 griz and Zwicky Transient Facility (ZTF) gr photometry of 1975 transients with host–galaxy associations, redshifts, spectroscopic and/or photometric classifications, and additional data products from 2019 November 24 to 2021 December 20. YSE DR1 spans discoveries and observations from young and fast-rising supernovae (SNe) to transients that persist for over a year, with a redshift distribution reaching z ≈ 0.5. We present relative SN rates from YSE’s magnitude- and volume-limited surveys, which are consistent with previously published values within estimated uncertainties for untargeted surveys. We combine YSE and ZTF data, and create multisurvey SN simulations to train the ParSNIP and SuperRAENN photometric classification algorithms; when validating our ParSNIP classifier on 472 spectroscopically classified YSE DR1 SNe, we achieve 82% accuracy across three SN classes (SNe Ia, II, Ib/Ic) and 90% accuracy across two SN classes (SNe Ia, core-collapse SNe). Our classifier performs particularly well on SNe Ia, with high (>90%) individual completeness and purity, which will help build an anchor photometric SNe Ia sample for cosmology. We then use our photometric classifier to characterize our photometric sample of 1483 SNe, labeling 1048 (∼71%) SNe Ia, 339 (∼23%) SNe II, and 96 (∼6%) SNe Ib/Ic. YSE DR1 provides a training ground for building discovery, anomaly detection, and classification algorithms, performing cosmological analyses, understanding the nature of red and rare transients, exploring tidal disruption events and nuclear variability, and preparing for the forthcoming Vera C. Rubin Observatory Legacy Survey of Space and Time. 
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    Free, publicly-accessible full text available May 1, 2024
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