We explore and demonstrate the capabilities of the upcoming Large Synoptic Survey Telescope (LSST) to study Type I superluminous supernovae (SLSNe). We fit the light curves of 58 known SLSNe at z ...0.1-1.6, using a magnetar spin-down model. We use the posterior distributions of the magnetar and ejecta parameters to generate synthetic SLSN light curves, and we inject those into the LSST Operations Simulator to generate ugrizy light curves. We define metrics to quantify the detectability and utility of the light curve. We combine the metric efficiencies with the SLSN volumetric rate to estimate the discovery rate of LSST and find that 104 SLSNe per year with >10 data points will be discovered in the Wide-Fast-Deep (WFD) survey at z 3.0, while only 15 SLSNe per year will be discovered in each Deep Drilling Field at z 4.0. To evaluate the information content in the LSST data, we refit representative output light curves. 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 50 observations in all bands combined, are most information rich. WFD survey strategies, which increase cadence in these bands and minimize seasonal gaps, will maximize the number of scientifically useful SLSNe. Finally, although the Deep Drilling Fields will provide more densely sampled light curves, we expect only 50 SLSNe with recoverable parameters in each field in the decade-long survey.
The duration-luminosity phase space (DLPS) of optical transients is used, mostly heuristically, to compare various classes of transient events, to explore the origin of new transients, and to ...influence optical survey observing strategies. For example, several observational searches have been guided by intriguing voids and gaps in this phase space. However, we should ask, do we expect to find transients in these voids given our understanding of the various heating sources operating in astrophysical transients? In this work, we explore a broad range of theoretical models and empirical relations to generate optical light curves and to populate the DLPS. We explore transients powered by adiabatic expansion, radioactive decay, magnetar spin-down, and circumstellar interaction. For each heating source, we provide a concise summary of the basic physical processes, a physically motivated choice of model parameter ranges, an overall summary of the resulting light curves and their occupied range in the DLPS, and how the various model input parameters affect the light curves. We specifically explore the key voids discussed in the literature: the intermediate-luminosity gap between classical novae and supernovae, and short-duration transients ( days). We find that few physical models lead to transients that occupy these voids. Moreover, we find that only relativistic expansion can produce fast and luminous transients, while for all other heating sources events with durations days are dim ( mag). Finally, we explore the detection potential of optical surveys (e.g., Large Synoptic Survey Telescope) in the DLPS and quantify the notion that short-duration and dim transients are exponentially more difficult to discover in untargeted surveys.
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 modeled 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 , with a sharp decline at lower masses, and is best fit by a broken power law described by at 3.6-8.6 M and at 8.6-40 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 1/3 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.
MOSFiT: Modular Open Source Fitter for Transients Guillochon, James; Nicholl, Matt; Villar, V. Ashley ...
The Astrophysical journal. Supplement series,
05/2018, Letnik:
236, Številka:
1
Journal Article
Recenzirano
Odprti dostop
Much of the progress made in time-domain astronomy is accomplished by relating observational multiwavelength time-series data to models derived from our understanding of physical laws. This goal is ...typically accomplished by dividing the task in two: collecting data (observing), and constructing models to represent that data (theorizing). Owing to the natural tendency for specialization, a disconnect can develop between the best available theories and the best available data, potentially delaying advances in our understanding new classes of transients. We introduce MOSFiT: the Modular Open Source Fitter for Transients, a Python-based package that downloads transient data sets from open online catalogs (e.g., the Open Supernova Catalog), generates Monte Carlo ensembles of semi-analytical light-curve fits to those data sets and their associated Bayesian parameter posteriors, and optionally delivers the fitting results back to those same catalogs to make them available to the rest of the community. MOSFiT is designed to help bridge the gap between observations and theory in time-domain astronomy; in addition to making the application of existing models and creation of new models as simple as possible, MOSFiT yields statistically robust predictions for transient characteristics, with a standard output format that includes all the setup information necessary to reproduce a given result. As large-scale surveys such as that conducted with the Large Synoptic Survey Telescope (LSST), discover entirely new classes of transients, tools such as MOSFiT will be critical for enabling rapid comparison of models against data in statistically consistent, reproducible, and scientifically beneficial ways.
Abstract
There is a shortage of multiwavelength and spectroscopic follow-up capabilities given the number of transient and variable astrophysical events discovered through wide-field optical surveys ...such as the upcoming Vera C. Rubin Observatory and its associated Legacy Survey of Space and Time. From the haystack of potential science targets, astronomers must allocate scarce resources to study a selection of needles in real time. Here we present a variational recurrent autoencoder neural network to encode simulated Rubin Observatory extragalactic transient events using 1% of the PLAsTiCC data set to train the autoencoder. Our unsupervised method uniquely works with unlabeled, real-time, multivariate, and aperiodic data. We rank 1,129,184 events based on an anomaly score estimated using an isolation forest. We find that our pipeline successfully ranks rarer classes of transients as more anomalous. Using simple cuts in anomaly score and uncertainty, we identify a pure (≈95% pure) sample of rare transients (i.e., transients other than Type Ia, Type II, and Type Ibc supernovae), including superluminous and pair-instability supernovae. Finally, our algorithm is able to identify these transients as anomalous well before peak, enabling real-time follow-up studies in the era of the Rubin Observatory.
One of the key challenges of real-time detection and parameter estimation of gravitational waves from compact binary mergers is the computational cost of conventional matched-filtering and Bayesian ...inference approaches. In particular, the application of these methods to the full signal parameter space available to the gravitational-wave detectors, and/or real-time parameter estimation is computationally prohibitive. On the other hand, rapid detection and inference are critical for prompt follow-up of the electromagnetic and astro-particle counterparts accompanying important transients, such as binary neutron-star and black-hole neutron-star mergers. Training deep neural networks to identify specific signals and learn a computationally efficient representation of the mapping between gravitational-wave signals and their parameters allows both detection and inference to be done quickly and reliably, with high sensitivity and accuracy. In this work we apply a deep-learning approach to rapidly identify and characterize transient gravitational-wave signals from binary neutron-star mergers in real LIGO data. We show for the first time that artificial neural networks can promptly detect and characterize binary neutron star gravitational-wave signals in real LIGO data, and distinguish them from noise and signals from coalescing black-hole binaries. We illustrate this key result by demonstrating that our deep-learning framework classifies correctly all gravitational-wave events from the Gravitational-Wave Transient Catalog, GWTC-1 Abbott et al. (2019) 4. These results emphasize the importance of using realistic gravitational-wave detector data in machine learning approaches, and represent a step towards achieving real-time detection and inference of gravitational waves.
Abstract
We present optical photometry and spectroscopy of SN 2019stc (=ZTF19acbonaa), an unusual Type Ic supernova (SN Ic) at a redshift of
z
= 0.117. SN 2019stc exhibits a broad double-peaked light ...curve, with the first peak having an absolute magnitude of
M
r
= −20.0 mag, and the second peak, about 80 rest-frame days later,
M
r
= −19.2 mag. The total radiated energy is large,
E
rad
≈ 2.5 × 10
50
erg. Despite its large luminosity, approaching those of Type I superluminous supernovae (SLSNe), SN 2019stc exhibits a typical SN Ic spectrum, bridging the gap between SLSNe and SNe Ic. The spectra indicate the presence of Fe-peak elements, but modeling of the first light-curve peak with radioactive heating alone leads to an unusually high nickel mass fraction of
f
Ni
≈ 0.31 (
M
Ni
≈ 3.2
M
⊙
). Instead, if we model the first peak with a combined magnetar spin-down and radioactive heating model we find a better match with
M
ej
≈ 4
M
⊙
, a magnetar spin period of
P
spin
≈ 7.2 ms, and magnetic field of
B
≈ 10
14
G, and
f
Ni
≲ 0.2 (consistent with SNe Ic). The prominent second peak cannot be naturally accommodated with radioactive heating or magnetar spin-down, but instead can be explained as circumstellar interaction with ≈0.7
M
⊙
of hydrogen-free material located ≈400 au from the progenitor. Accounting for the ejecta mass, circumstellar shell mass, and remnant neutron star mass, we infer a CO core mass prior to explosion of ≈6.5
M
⊙
. The host galaxy has a metallicity of ≈0.26
Z
⊙
, low for SNe Ic but consistent with SLSNe. Overall, we find that SN 2019stc is a transition object between normal SNe Ic and SLSNe.
Abstract We present preexplosion optical and infrared (IR) imaging at the site of the type II supernova (SN II) 2023ixf in Messier 101 at 6.9 Mpc. We astrometrically registered a ground-based image ...of SN 2023ixf to archival Hubble Space Telescope (HST), Spitzer Space Telescope (Spitzer), and ground-based near-IR images. A single point source is detected at a position consistent with the SN at wavelengths ranging from HST R band to Spitzer 4.5 μ m. Fitting with blackbody and red supergiant (RSG) spectral energy distributions (SEDs), we find that the source is anomalously cool with a significant mid-IR excess. We interpret this SED as reprocessed emission in a 8600 R ⊙ circumstellar shell of dusty material with a mass ∼5 × 10 −5 M ⊙ surrounding a log ( L / L ⊙ ) = 4.74 ± 0.07 and T eff = 3920 − 160 + 200 K RSG. This luminosity is consistent with RSG models of initial mass 11 M ⊙ , depending on assumptions of rotation and overshooting. In addition, the counterpart was significantly variable in preexplosion Spitzer 3.6 and 4.5 μ m imaging, exhibiting ∼70% variability in both bands correlated across 9 yr and 29 epochs of imaging. The variations appear to have a timescale of 2.8 yr, which is consistent with κ -mechanism pulsations observed in RSGs, albeit with a much larger amplitude than RSGs such as α Orionis (Betelgeuse).
We present optical photometry and spectroscopy of SN 2016iet (=Gaia16bvd=PS17brq), an unprecedented Type I supernova (SN I) at z = 0.0676 with no obvious analog in the existing literature. SN 2016iet ...exhibits a peculiar light curve, with two roughly equal brightness peaks ( −19 mag) separated by about 100 days, and a subsequent slow decline by about 5 mag in 650 rest-frame days. The spectra are dominated by strong 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. There is no clear evidence for hydrogen or helium associated with the SN at any phase. The nebular spectra exhibit a ratio of , much larger than for core-collapse SNe and Type I superluminous SNe. We model the light curves with several potential energy sources: radioactive decay, a central engine, and ejecta-circumstellar medium (CSM) interaction. Regardless of the model, the inferred progenitor mass near the end of its life (i.e., the CO core mass) is 55 M and potentially up to 120 M , clearly 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 35 M ejected in the final decade before explosion. We further find that SN 2016iet is located at an unusually large projected offset (16.5 kpc, 4.3 effective radii) from its low-metallicity dwarf host galaxy (Z 0.1 Z , L 0.02 L*, M 108.5 M ), supporting the interpretation of a PPISN/PISN explosion. In our final spectrum at a phase of about 770 rest-frame days we detect weak and narrow H emission at the location of the SN, corresponding to a star formation rate of 3 × 10−4 M yr−1, which is 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.
Over the past decade wide-field optical time-domain surveys have increased the discovery rate of transients to the point that 10% are being spectroscopically classified. Despite this, these surveys ...have enabled the discovery of new and rare types of transients, most notably the class of hydrogen-poor superluminous supernovae (SLSN-I), with about 150 events confirmed to date. Here we present a machine-learning classification algorithm targeted at rapid identification of a pure sample of SLSN-I to enable spectroscopic and multiwavelength follow-up. This algorithm is part of the Finding Luminous and Exotic Extragalactic Transients (FLEET) observational strategy. It utilizes both light-curve and contextual information, but without the need for a redshift, to assign each newly discovered transient a probability of being a SLSN-I. This classifier can achieve a maximum purity of about 85% (with 20% completeness) when observing a selection of SLSN-I candidates. Additionally, we present two alternative classifiers that use either redshifts or complete light curves and can achieve an even higher purity and completeness. At the current discovery rate, the FLEET algorithm can provide about 20 SLSN-I candidates per year for spectroscopic follow-up with 85% purity; with the Legacy Survey of Space and Time we anticipate this will rise to more than events per year.
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