120 research outputs found
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 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
SBI++: Flexible, Ultra-fast Likelihood-free Inference Customized for Astronomical Application
Flagship near-future surveys targeting 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 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 1 sec for objects
in the observational training set distribution, and additionally permits
parameter inference outside of the trained noise and data at 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
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
M, with a sharp decline at lower masses, and is best fit by a broken
power law described by at
M and at M.
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 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
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 with no obvious analog in the
existing literature. The peculiar light curve has two roughly equal brightness
peaks ( 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 km s, superposed on a
strong blue continuum in the first year, and with a large ratio of 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
M and up to M, 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
M ejected in the final decade before explosion. We further
find that SN 2016iet is located at an unusually large offset ( kpc) from
its low metallicity dwarf host galaxy ( Z, M), supporting the PPISN/PISN interpretation. In the final
spectrum, we detect narrow H 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
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