Amortized Bayesian Inference for Supernovae in the Era of the Vera Rubin Observatory Using Normalizing Flows

The Vera Rubin Observatory, set to begin observations in mid-2024, will increase our discovery rate of supernovae to well over one million annually. There has been a significant push to develop new methodologies to identify, classify and ultimately understand the millions of supernovae discovered with the Rubin Observatory. Here, we present the first simulation-based inference method using normalizing flows, trained to rapidly infer the parameters of toy supernovae model in multivariate, Rubin-like datastreams. We find that our method is well-calibrated compared to traditional inference methodologies (specifically MCMC), requiring only one-ten-thousandth of the CPU hours during test time.Comment: 5 pages, accepted in the Neurips Machine Learning and the Physical Sciences conferenc

Superluminous supernovae in LSST:rates, detection metrics, and light-curve modeling

We explore and demonstrate the capabilities of LSST to study Type I superluminous supernovae (SLSNe). We first fit the light curves of 58 known SLSNe at z~0.1-1.6, using an analytical magnetar spin-down model implemented in MOSFiT. We then use the posterior distributions of the magnetar and ejecta parameters to generate thousands of synthetic SLSN light curves, and we inject those into the OpSim to generate realistic ugrizy light curves. We define simple, measurable metrics to quantify the detectability and utility of the light curve, and to measure the efficiency of LSST in returning SLSN light curves satisfying these metrics. We combine the metric efficiencies with the volumetric rate of SLSNe to estimate the overall discovery rate of LSST, and we find that ~10^4 SLSNe per year with >10 data points will be discovered in the WFD survey at z<3.0, while only ~15 SLSNe per year will be discovered in each DDF at z<4.0. To evaluate the information content in the LSST data, we refit representative output light curves with the same model that was used to generate them. We correlate our ability to recover magnetar and ejecta parameters with the simple light curve metrics to evaluate the most important metrics. We find that we can recover physical parameters to within 30% of their true values from ~18% of WFD light curves. Light curves with measurements of both the rise and decline in gri-bands, and those with at least fifty observations in all bands combined, are most information rich, with ~30% of these light curves having recoverable physical parameters to ~30% accuracy. WFD survey strategies which increase cadence in these bands and minimize seasonal gaps will maximize the number of scientifically useful SLSN light curves. Finally, although the DDFs will provide more densely sampled light curves, we expect only ~50 SLSNe with recoverable parameters in each field in the decade-long survey.Comment: 13 pages, 11 figures, submitted to Ap

Photometrically-Classified Superluminous Supernovae from the Pan-STARRS1 Medium Deep Survey: A Case Study for Science with Machine Learning-Based Classification

With the upcoming Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST), it is expected that only $\sim 0.1\%$ 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 our SuperRAENN and Superphot algorithms. We first construct a sub-sample 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 multi-band 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 overall consistent, 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 to an orders-of-magnitude increase in the SLSN sample in the LSST era through photometric selection and study.Comment: 13 pages, 6 figures, submitted to Ap

The Pre-Explosion Mass Distribution of Hydrogen-Poor Superluminous Supernova Progenitors and New Evidence for a Mass-Spin Correlation

Despite indications that superluminous supernovae (SLSNe) originate from massive progenitors, the lack of a uniformly analyzed statistical sample has so far prevented a detailed view of the progenitor mass distribution. Here we present and analyze the pre-explosion mass distribution of hydrogen-poor SLSN progenitors as determined from uniformly modelled light curves of 62 events. We construct the distribution by summing the ejecta mass posteriors of each event, using magnetar light curve models presented in our previous works (and using a nominal neutron star remnant mass). The resulting distribution spans $3.6-40$ M$_{\odot}$, with a sharp decline at lower masses, and is best fit by a broken power law described by ${\rm d}N/{\rm dlog}M \propto M^{-0.41 \pm 0.06}$ at $3.6-8.6$ M$_{\odot}$ and $\propto M^{-1.26 \pm 0.06}$ at $8.6-40$ M$_{\odot}$. We find that observational selection effects cannot account for the shape of the distribution. Relative to Type Ib/c SNe, the SLSN mass distribution extends to much larger masses and has a different power-law shape, likely indicating that the formation of a magnetar allows more massive stars to explode as some of the rotational energy accelerates the ejecta. Comparing the SLSN distribution with predictions from single and binary star evolution models, we find that binary models for a metallicity of $Z\lesssim 1/3$ Z$_{\odot}$ are best able to reproduce its broad shape, in agreement with the preference of SLSNe for low metallicity environments. Finally, we uncover a correlation between the pre-explosion mass and the magnetar initial spin period, where SLSNe with low masses have slower spins, a trend broadly consistent with the effects of angular momentum transport evident in models of rapidly-rotating carbon-oxygen stars.Comment: 18 pages, 11 figures, Submitted to Ap

SBI++: Flexible, Ultra-fast Likelihood-free Inference Customized for Astronomical Application

Flagship near-future surveys targeting $10^8-10^9$ galaxies across cosmic time will soon reveal the processes of galaxy assembly in unprecedented resolution. This creates an immediate computational challenge on effective analyses of the full data-set. With simulation-based inference (SBI), it is possible to attain complex posterior distributions with the accuracy of traditional methods but with a $>10^4$ increase in speed. However, it comes with a major limitation. Standard SBI requires the simulated data to have identical characteristics to the observed data, which is often violated in astronomical surveys due to inhomogeneous coverage and/or fluctuating sky and telescope conditions. In this work, we present a complete SBI-based methodology, ``SBI$^{++}$,'' for treating out-of-distribution measurement errors and missing data. We show that out-of-distribution errors can be approximated by using standard SBI evaluations and that missing data can be marginalized over using SBI evaluations over nearby data realizations in the training set. In addition to the validation set, we apply SBI$^{++}$ to galaxies identified in extragalactic images acquired by the James Webb Space Telescope, and show that SBI$^{++}$ can infer photometric redshifts at least as accurately as traditional sampling methods and crucially, better than the original SBI algorithm using training data with a wide range of observational errors. SBI$^{++}$ retains the fast inference speed of $\sim$1 sec for objects in the observational training set distribution, and additionally permits parameter inference outside of the trained noise and data at $\sim$1 min per object. This expanded regime has broad implications for future applications to astronomical surveys.Comment: 12 pages, 5 figures. Code and a Jupyter tutorial are made publicly available at https://github.com/wangbingjie/sbi_p

SN 2016iet: The Pulsational or Pair Instability Explosion of a Low Metallicity Massive CO Core Embedded in a Dense Hydrogen-Poor Circumstellar Medium

We present optical photometry and spectroscopy of SN 2016iet, an unprecedented Type I supernova (SN) at $z=0.0676$ with no obvious analog in the existing literature. The peculiar light curve has two roughly equal brightness peaks ($\approx -19$ mag) separated by 100 days, and a subsequent slow decline by 5 mag in 650 rest-frame days. The spectra are dominated by emission lines of calcium and oxygen, with a width of only $3400$ km s$^{-1}$, superposed on a strong blue continuum in the first year, and with a large ratio of $L_{\rm [Ca\,II]}/L_{\rm [O\,I]}\approx 4$ at late times. There is no clear evidence for hydrogen or helium associated with the SN at any phase. We model the light curves with several potential energy sources: radioactive decay, central engine, and circumstellar medium (CSM) interaction. Regardless of the model, the inferred progenitor mass near the end of its life (i.e., CO core mass) is $\gtrsim 55$ M$_\odot$ and up to $120$ M$_\odot$, placing the event in the regime of pulsational pair instability supernovae (PPISNe) or pair instability supernovae (PISNe). The models of CSM interaction provide the most consistent explanation for the light curves and spectra, and require a CSM mass of $\approx 35$ M$_\odot$ ejected in the final decade before explosion. We further find that SN 2016iet is located at an unusually large offset ($16.5$ kpc) from its low metallicity dwarf host galaxy ($Z\approx 0.1$ Z$_\odot$, $M\approx 10^{8.5}$ M$_\odot$), supporting the PPISN/PISN interpretation. In the final spectrum, we detect narrow H$\alpha$ emission at the SN location, likely due to a dim underlying galaxy host or an H II region. Despite the overall consistency of the SN and its unusual environment with PPISNe and PISNe, we find that the inferred properties of SN\,2016iet challenge existing models of such events.Comment: 26 Pages, 17 Figures, Submitted to Ap