Rapid: Early classification of explosive transients using deep learning

We present RAPID (Real-time Automated Photometric IDentification), a novel time-series classification tool capable of automatically identifying transients from within a day of the initial alert, to the full lifetime of a light curve. Using a deep recurrent neural network with Gated Recurrent Units (GRUs), we present the first method specifically designed to provide early classifications of astronomical time-series data, typing 12 different transient classes. Our classifier can process light curves with any phase coverage, and it does not rely on deriving computationally expensive features from the data, making RAPID well-suited for processing the millions of alerts that ongoing and upcoming wide-field surveys such as the Zwicky Transient Facility (ZTF), and the Large Synoptic Survey Telescope (LSST) will produce. The classification accuracy improves over the lifetime of the transient as more photometric data becomes available, and across the 12 transient classes, we obtain an average area under the receiver operating characteristic curve of 0.95 and 0.98 at early and late epochs, respectively. We demonstrate RAPID's ability to effectively provide early classifications of transients from the ZTF data stream. We have made RAPID available as an open-source software package (this https URL) for machine learning-based alert-brokers to use for the autonomous and quick classification of several thousand light curves within a few seconds

The Type Ia Supernova Color-Magnitude Relation and Host Galaxy Dust: A Simple Hierarchical Bayesian Model

Conventional Type Ia supernova (SN Ia) cosmology analyses currently use a simplistic linear regression of magnitude versus color and light curve shape, which does not model intrinsic SN Ia variations and host galaxy dust as physically distinct effects, resulting in low color-magnitude slopes. We construct a probabilistic generative model for the dusty distribution of extinguished absolute magnitudes and apparent colors as the convolution of a intrinsic SN Ia color-magnitude distribution and a host galaxy dust reddening-extinction distribution. If the intrinsic color-magnitude ($M_B$ vs. $B-V$) slope $\beta_{int}$ differs from the host galaxy dust law $R_B$, this convolution results in a specific curve of mean extinguished absolute magnitude vs. apparent color. The derivative of this curve smoothly transitions from $\beta_{int}$ in the blue tail to $R_B$ in the red tail of the apparent color distribution. The conventional linear fit approximates this effective curve near the average apparent color, resulting in an apparent slope $\beta_{app}$ between $\beta_{int}$ and $R_B$. We incorporate these effects into a hierarchical Bayesian statistical model for SN Ia light curve measurements, and analyze a dataset of SALT2 optical light curve fits of 248 nearby SN Ia at z < 0.10. The conventional linear fit obtains $\beta_{app} \approx 3$. Our model finds a $\beta_{int} = 2.3 \pm 0.3$ and a distinct dust law of $R_B = 3.8 \pm 0.3$, consistent with the average for Milky Way dust, while correcting a systematic distance bias of $\sim 0.10$ mag in the tails of the apparent color distribution. Finally, we extend our model to examine the SN Ia luminosity-host mass dependence in terms of intrinsic and dust components

Type Ia Supernovae Are Excellent Standard Candles in the Near-infrared

Abstract We analyze a set of 89 type Ia supernovae (SNe Ia) that have both optical and near-infrared (NIR) photometry to derive distances and construct low-redshift (z ≤ 0.04) Hubble diagrams. We construct mean light curve (LC) templates using a hierarchical Bayesian model. We explore both Gaussian process (GP) and template methods for fitting the LCs and estimating distances, while including peculiar-velocity and photometric uncertainties. For the 56 SNe Ia with both optical and NIR observations near maximum light, the GP method yields a NIR-only Hubble-diagram with a root mean square (rms) of mag when referenced to the NIR maxima. For each NIR band, a comparable GP method rms is obtained when referencing to NIR-max or B-max. Using NIR LC templates referenced to B-max yields a larger rms value of mag. Fitting the corresponding optical data using standard LC fitters that use LC shape and color corrections yields larger rms values of 0.179 ± 0.018 mag with SALT2 and mag with SNooPy. Applying our GP method to subsets of SNe Ia NIR LCs at NIR maximum light, even without corrections for LC shape, color, or host-galaxy dust reddening, provides smaller rms in the inferred distances, at the ∼2.3–4.1σ level, than standard optical methods that correct for those effects. Our ongoing RAISIN program on the Hubble Space Telescope will exploit this promising infrared approach to limit systematic errors when measuring the expansion history of the universe in order to constrain dark energy.</jats:p

A Dependence of the Tidal Disruption Event Rate on Global Stellar Surface Mass Density and Stellar Velocity Dispersion

© 2018. The American Astronomical Society. All rights reserved. The rate of tidal disruption events (TDEs), R TDE , is predicted to depend on stellar conditions near the super-massive black hole (SMBH), which are on difficult-to-measure sub-parsec scales. We test whether R TDE depends on kpcscale global galaxy properties, which are observable. We concentrate on stellar surface mass density, ∑ M∗ , and velocity dispersion, σ v , which correlate with the stellar density and velocity dispersion of the stars around the SMBH. We consider 35 TDE candidates, with and without known X-ray emission. The hosts range from starforming to quiescent to quiescent with strong Balmer absorption lines. The last (often with post-starburst spectra) are overrepresented in our sample by a factor of 35 +21 -17 or 18 +8 -7 , depending on the strength of the Hδ absorption line. For a subsample of hosts with homogeneous measurements, ∑ M∗ = 10 9 -10 10 M ⊙ /kpc 2 , higher on average than for a volume-weighted control sample of Sloan Digital Sky Survey galaxies with similar redshifts and stellar masses. This is because (1) most of the TDE hosts here are quiescent galaxies, which tend to have higher ∑ M∗ than the star-forming galaxies that dominate the control, and (2) the star-forming hosts have higher average ∑ M∗ than the star-forming control. There is also a weak suggestion that TDE hosts have lower σ v than for the quiescent control. Assuming that R TDE ∝ ∑ M∗ α × σ v β , and applying a statistical model to the TDE hosts and control sample, we estimate α = 0.9 ; 0.2 and β = -1.0 0.6. This is broadly consistent with RTDE being tied to the dynamical relaxation of stars surrounding the SMBH

MOSFiT: Modular open source fitter for transients

Much of the progress made in time-domain astronomy is accomplished by relating observational multi-wavelength 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 datasets from open online catalogs (e.g., the Open Supernova Catalog), generates Monte Carlo ensembles of semi-analytical light curve fits to those datasets 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 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

Testing the consistency of dust laws in SN Ia host galaxies: a BayeSN examination of Foundation DR1

Abstract We apply BayeSN, our new hierarchical Bayesian model for the SEDs of Type Ia supernovae (SNe Ia), to analyse the griz light curves of 157 nearby SNe Ia (0.015 &amp;lt; z &amp;lt; 0.08) from the public Foundation DR1 dataset. We train a new version of BayeSN, continuous from 0.35–0.95 μm, which we use to model the properties of SNe Ia in the rest-frame z-band, study the properties of dust in their host galaxies, and construct a Hubble diagram of SN Ia distances determined from full griz light curves. Our griz Hubble diagram has a low total RMS of 0.13 mag using BayeSN, compared to 0.16 mag using SALT2. Additionally, we test the consistency of the dust law RV between low- and high-mass host galaxies by using our model to fit the full time- and wavelength-dependent SEDs of SNe Ia up to moderate reddening (peak apparent B − V ≲ 0.3). Splitting the population at the median host mass, we find RV = 2.84 ± 0.31 in low-mass hosts, and RV = 2.58 ± 0.23 in high-mass hosts, both consistent with the global value of RV = 2.61 ± 0.21 that we estimate for the full sample. For all choices of mass split we consider, RV is consistent across the step within ≲ 1.2σ. Modelling population distributions of dust laws in low- and high-mass hosts, we find that both subsamples are highly consistent with the full sample’s population mean μ(RV) = 2.70 ± 0.25 with a 95 per cent upper bound on the population σ(RV) &amp;lt; 0.61. The RV population means are consistent within ≲ 1.2σ. We find that simultaneous fitting of host-mass-dependent dust properties within our hierarchical model does not account for the conventional mass step.</jats:p

Bayesian astrostatistics: a backward look to the future

This perspective chapter briefly surveys: (1) past growth in the use of Bayesian methods in astrophysics; (2) current misconceptions about both frequentist and Bayesian statistical inference that hinder wider adoption of Bayesian methods by astronomers; and (3) multilevel (hierarchical) Bayesian modeling as a major future direction for research in Bayesian astrostatistics, exemplified in part by presentations at the first ISI invited session on astrostatistics, commemorated in this volume. It closes with an intentionally provocative recommendation for astronomical survey data reporting, motivated by the multilevel Bayesian perspective on modeling cosmic populations: that astronomers cease producing catalogs of estimated fluxes and other source properties from surveys. Instead, summaries of likelihood functions (or marginal likelihood functions) for source properties should be reported (not posterior probability density functions), including nontrivial summaries (not simply upper limits) for candidate objects that do not pass traditional detection thresholds.Comment: 27 pp, 4 figures. A lightly revised version of a chapter in "Astrostatistical Challenges for the New Astronomy" (Joseph M. Hilbe, ed., Springer, New York, forthcoming in 2012), the inaugural volume for the Springer Series in Astrostatistics. Version 2 has minor clarifications and an additional referenc

A survey of individual preference for colorectal cancer screening technique

BACKGROUND: Due to the low participation in colorectal cancer screening, public preference for colorectal cancer screening modality was determined. METHODS: A cross-sectional survey was performed of healthy ambulatory adults in a pediatrics primary care office and neighboring church. Overall preference was ranked for each of four colorectal cancer screening modalities: Faecal Occult Blood, Fiberoptic Sigmoidoscopy, Barium Enema and Colonoscopy. Four additional domains of preference also were ranked: suspected discomfort, embarrassment, inconvenience and danger of each exam. RESULTS: 80 surveys were analyzed, 57 of which were received from participants who had experienced none of the screening tests. Fecal Occult Blood Testing is significantly preferred over each other screening modality in overall preference and every domain of preference, among all subjects and those who had experienced none of the tests. CONCLUSIONS: Efforts to increase public participation in colorectal cancer screening may be more effective if undertaken in the context of public perceptions of screening choices

Models and simulations for the photometric lsst astronomical time series classification challenge (Plasticc)

We describe the simulated data sample for the "Photometric LSST Astronomical Time Series Classification Challenge" (PLAsTiCC), a publicly available challenge to classify transient and variable events that will be observed by the Large Synoptic Survey Telescope (LSST), a new facility expected to start in the early 2020s. The challenge was hosted by Kaggle, ran from 2018 September 28 to 2018 December 17, and included 1,094 teams competing for prizes. Here we provide details of the 18 transient and variable source models, which were not revealed until after the challenge, and release the model libraries at this https URL. We describe the LSST Operations Simulator used to predict realistic observing conditions, and we describe the publicly available SNANA simulation code used to transform the models into observed fluxes and uncertainties in the LSST passbands (ugrizy). Although PLAsTiCC has finished, the publicly available models and simulation tools are being used within the astronomy community to further improve classification, and to study contamination in photometrically identified samples of type Ia supernova used to measure properties of dark energy. Our simulation framework will continue serving as a platform to improve the PLAsTiCC models, and to develop new models

Fecal Tests: From Blood to Molecular Markers

Detection of molecular markers for colorectal neoplasia in feces has the potential to improve performance of simple noninvasive screening tests for colorectal cancer. Most research has explored the value of DNA-based, RNA-based, and protein-based markers. In all cases there has been a trend to move from a single marker to a panel of markers to improve sensitivity. Unfortunately, no type of molecular marker has proved specific for neoplasia. DNA tests have been improved by combining mutation detection with assessment of DNA integrity plus epigenetic markers of neoplasia. RNA-based approaches are just beginning to explore the full power of transcriptomics. So far, no protein-based fecal test has proved better than fecal immunochemical tests for hemoglobin. Finally, no marker or panel of markers has yet been developed to the point where it has been evaluated in large unbiased population studies to assess performance across all stages of neoplasia and in all practical environments