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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.

 

The modern study of astrophysical transients has been transformed by an exponentially growing volume of data. Within the last decade, the transient discovery rate has increased by a factor of ∼20, with associated survey data, archival data, and metadata also increasing with the number of discoveries. To manage the data at this increased rate, we require new tools. Here we presentYSE-PZ, a transient survey management platform that ingests multiple live streams of transient discovery alerts, identifies the host galaxies of those transients, downloads coincident archival data, and retrieves photometry and spectra from ongoing surveys.YSE-PZalso presents a user with a range of tools to make and support timely and informed transient follow-up decisions. Those subsequent observations enhance transient science and can reveal physics only accessible with rapid follow-up observations. Rather than automating out human interaction,YSE-PZfocuses on accelerating and enhancing human decision making, a role we describe as empowering the human-in-the-loop. Finally,YSE-PZis built to be flexibly used and deployed;YSE-PZcan support multiple, simultaneous, and independent transient collaborations through group-level data permissions, allowing a user to view the data associated with the union of all groups in which they are a member.YSE-PZcan be used as a local instance installed via Docker or deployed as a service hosted in the cloud. We provideYSE-PZas an open-source tool for the community.

 

We present the Keck Infrared Transient Survey, a NASA Key Strategic Mission Support program to obtain near-infrared (NIR) spectra of astrophysical transients of all types, and its first data release, consisting of 105 NIR spectra of 50 transients. Such a data set is essential as we enter a new era of IR astronomy with the James Webb Space Telescope (JWST) and the upcoming Nancy Grace Roman Space Telescope (Roman). NIR spectral templates will be essential to search JWST images for stellar explosions of the first stars and to plan an effective Roman SN Ia cosmology survey, both key science objectives for mission success. Between 2022 February and 2023 July, we systematically obtained 274 NIR spectra of 146 astronomical transients, representing a significant increase in the number of available NIR spectra in the literature. Here, we describe the first release of data from the 2022A semester. We systematically observed three samples: a flux-limited sample that includes all transients <17 mag in a red optical band (usually ZTFror ATLASobands); a volume-limited sample including all transients within redshiftz< 0.01 (D≈ 50 Mpc); and an SN Ia sample targeting objects at phases and light-curve parameters that had scant existing NIR data in the literature. The flux-limited sample is 39% complete (60% excluding SNe Ia), while the volume-limited sample is 54% complete and is 79% complete toz= 0.005. Transient classes observed include common Type Ia and core-collapse supernovae, tidal disruption events, luminous red novae, and the newly categorized hydrogen-free/helium-poor interacting Type Icn supernovae. We describe our observing procedures and data reduction usingPypeIt, which requires minimal human interaction to ensure reproducibility.

 

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. 

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

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ABSTRACT After correcting for their light-curve shape and colour, Type Ia supernovae (SNe Ia) are precise cosmological distance indicators. However, there remains a non-zero intrinsic scatter in the differences between measured distance and that inferred from a cosmological model (i.e. Hubble residuals or HRs), indicating that SN Ia distances can potentially be further improved. We use the open-source relational data base kaepora to generate composite spectra with desired average properties of phase, light-curve shape, and HR. At many phases, the composite spectra from two subsamples with positive and negative average HRs are significantly different. In particular, in all spectra from 9 d before to 15 d after peak brightness, we find that SNe with negative HRs have, on average, higher ejecta velocities (as seen in nearly every optical spectral feature) than SNe with positive HRs. At +4 d relative to B-band maximum, using a sample of 62 SNe Ia, we measure a 0.091 ± 0.035 mag (2.7σ) HR step between SNe with Si ii λ6355 line velocities ($v_{Si\, rm{\small II}}$) higher/lower than −11 000 km s−1 (the median velocity). After light-curve shape and colour correction, SNe with higher velocities tend to have underestimated distance moduli relative to a cosmological model. The intrinsic scatter in our sample reduces from 0.094 to 0.082 mag after making this correction. Using the Si ii λ6355 velocity evolution of 115 SNe Ia, we estimate that a velocity difference >500 km s−1 exists at each epoch between the positive-HR and negative-HR samples with 99.4 per cent confidence. Finally at epochs later than +37 d, we observe that negative-HR composite spectra tend to have weaker spectral features in comparison to positive-HR composite spectra. 

Type Ia supernovae (SNe Ia) are more precise standardizable candles when measured in the near-infrared (NIR) than in the optical. With this motivation, from 2012 to 2017 we embarked on the RAISIN program with the Hubble Space Telescope (HST) to obtain rest-frame NIR light curves for a cosmologically distant sample of 37 SNe Ia (0.2 ≲z≲ 0.6) discovered by Pan-STARRS and the Dark Energy Survey. By comparing higher-zHST data with 42 SNe Ia atz< 0.1 observed in the NIR by the Carnegie Supernova Project, we construct a Hubble diagram from NIR observations (with only time of maximum light and some selection cuts from optical photometry) to pursue a unique avenue to constrain the dark energy equation-of-state parameter,w. We analyze the dependence of the full set of Hubble residuals on the SN Ia host galaxy mass and find Hubble residual steps of size ∼0.06-0.1 mag with 1.5σ−2.5σsignificance depending on the method and step location used. Combining our NIR sample with cosmic microwave background constraints, we find 1 +w= −0.17 ± 0.12 (statistical + systematic errors). The largest systematic errors are the redshift-dependent SN selection biases and the properties of the NIR mass step. We also use these data to measureH₀= 75.9 ± 2.2 km s⁻¹Mpc⁻¹from stars with geometric distance calibration in the hosts of eight SNe Ia observed in the NIR versusH₀= 71.2 ± 3.8 km s⁻¹Mpc⁻¹using an inverse distance ladder approach tied to Planck. Using optical data, we find 1 +w= −0.10 ± 0.09, and with optical and NIR data combined, we find 1 +w= −0.06 ± 0.07; these shifts of up to ∼0.11 inwcould point to inconsistency in the optical versus NIR SN models. There will be many opportunities to improve this NIR measurement and better understand systematic uncertainties through larger low-zsamples, new light-curve models, calibration improvements, and eventually by building high-zsamples from the Roman Space Telescope.