Extending Supernova Spectral Templates for Next-generation Space Telescope Observations

Pierel, Justin; Rodney, Steven A.; Avelino, Arturo; Bianco, Federica; Filippenko, A. V.; Foley, R. J.; Friedman, Andrew S.; Hicken, M.; Hounsell, R.; Jha, Saurabh W.; Keßler, R.; Kirshner, R.; Mandel, Kaisey S.; Narayan, Gautham; Scolnic, Dan; Strolger, L. G.

doi:10.1088/1538-3873/aadb7a

Cited by 49 publications

(35 citation statements)

References 71 publications

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“…For SNIa, the rest frame SED is computed with the SALT2 model (Guy et al 2010;Betoule et al 2014). Since the original SED model covers only the g and r bands in the rest-frame, we use a wavelength-extended model (Pierel et al 2018) that covers all of the LSST bands. For imSim to run properly, we include an additional modification that prevents negative UV spectral fluxes.…”

Section: The Dc2 Datasetmentioning

confidence: 99%

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SNIa-Cosmology Analysis Results from Simulated LSST Images: from Difference Imaging to Constraints on Dark Energy

Sánchez,

Kessler,

Scolnic

et al. 2021

Preprint

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The Vera Rubin Observatory Legacy Survey of Space and Time (LSST) is expected to process ∼10 6 transient detections per night. To use these transients for precision measurements of cosmological parameters and rates studies, it is critical to understand the detection efficiency, magnitude limits, artifact contamination levels, and biases in the selection and photometry. Here we rigorously test the LSST Difference Image Analysis (DIA) pipeline using simulated images from the Rubin Observatory LSST Dark Energy Science Collaboration (DESC) Data Challenge (DC2) simulation for the Wide-Fast-Deep (WFD) survey area. DC2 is the first large-scale (300 deg 2 ) image simulation of a transient survey that includes realistic cadence, variable observing conditions, and CCD image artifacts. We analyze ∼15 deg 2 of DC2 over a 5-year time-span in which artificial point-sources from Type Ia Supernovae (SNIa) light curves have been overlaid onto the images. We measure the detection efficiency as a function of Signal-to-Noise Ratio (SNR) and find a 50% efficiency at SNR = 5.8 averaged over all bands. The corresponding magnitude limits for each filter are: u = 23.66, g = 24.69, r = 24.06, i = 23.45, z = 22.54, y = 21.62 mag. The artifact contamination levels is ∼ 90% of all detections, corresponding to ∼ 1000 artifacts/deg 2 n g band, and falling to 300/deg 2 n y band. The recovered photometry has biases < 1% for magnitudes 19.5 < m < 23. We show that our DIA performance on simulated images is similar to that of the Dark Energy Survey difference-imaging pipeline applied to real images. We also characterize DC2 image properties to produce catalog-level simulations needed for distance bias corrections. We find good agreement between DC2 data and simulations for distributions of SNR, redshift, and fitted light-curve properties. Applying a realistic SNIa-cosmology analysis for redshifts z < 1, we recover the input cosmology parameters to within statistical uncertainties. Finally, we discuss further applications of this dataset and analysis, and we suggest pipeline improvements before LSST operations begins.

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Section: The Dc2 Datasetmentioning

confidence: 99%

“…-Light curve fitting on data: we use the SALT2-Extended (Pierel et al 2018) light curve model, the same model used to generate DC2 SNIa, and fit for t 0 , x 0 , x 1 and c parameters and their covariances. We impose the following selection requirements (cuts) based on previous cosmology analyses:…”

Section: Cosmology Analysismentioning

confidence: 99%

SNIa-Cosmology Analysis Results from Simulated LSST Images: from Difference Imaging to Constraints on Dark Energy

Sánchez,

Kessler,

Scolnic

et al. 2021

Preprint

Self Cite

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“…For this work, SCONE was trained and tested on a set of LSST deep drilling field (DDF) simulations. The dataset was created with SNANA (Kessler et al 2009) using the PLAsTiCC transient class models for supernovae types Ia, II, Ibc, Ia-91bg, Iax, and SLSN (The PLAsTiCC team et al 2018;Kessler et al 2019;Guy et al 2010;Kessler et al 2013;Pierel et al 2018a;Jha 2017;Kessler et al 2010c;Pierel et al 2018b; 2021) are reliably able to distinguish extragalactic and galactic events. Thus, we can assume the feasibility of creating a pure sample of SN lightcurves such as the one used in this work.…”

Section: Simulationsmentioning

confidence: 99%

Photometric Classification of Early-Time Supernova Lightcurves with SCONE

Qu,

Sako

2021

Preprint

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In this work, we present classification results on early supernova lightcurves from SCONE, a photometric classifier that uses convolutional neural networks to categorize supernovae (SNe) by type using lightcurve data. SCONE is able to identify SN types from lightcurves at any stage, from the night of initial alert to the end of their lifetimes. Simulated LSST SNe lightcurves were truncated at 0, 5, 15, 25, and 50 days after the trigger date and used to train Gaussian processes in wavelength and time space to produce wavelength-time heatmaps. SCONE uses these heatmaps to perform 6-way classification between SN types Ia, II, Ibc, Ia-91bg, Iax, and SLSN-I. SCONE is able to perform classification with or without redshift, but we show that incorporating redshift information improves performance at each epoch. SCONE achieved 75% overall accuracy at the date of trigger (60% without redshift), and 89% accuracy 50 days after trigger (82% without redshift). SCONE was also tested on bright subsets of SNe (r < 20 mag) and produced 91% accuracy at the date of trigger (83% without redshift) and 95% 5 days after trigger (94.7% without redshift). SCONE is the first application of convolutional neural networks to the early-time photometric transient classification problem. All of the data processing and model code developed for this paper can be found in the SCONE software package located at github.com/helenqu/scone (Qu 2021).

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“…However, the shape of the light curve resembles a core-collapse SN. We performed fits using SALT2-extended (Guy et al 2007;Pierel et al 2018) and Nugent templates 17 (Gilliland et al 1999 from Di Carlo et al 2002) yielded the best fit at z∼1.2. SN IIP templates also provided a good fit, while SN IIL templates a poor fit.…”

Section: High-amplitude Variables or Transients In The Hcv Catalogmentioning

confidence: 99%

The Hubble Catalog of Variables (HCV)

Bonanos

Yang

Sokolovsky

et al. 2019

A&A

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Aims. Over its lifetime and despite not being a survey telescope, the Hubble Space Telescope (HST) has obtained multi-epoch observations by multiple, diverse observing programs, providing the opportunity for a comprehensive variability search aiming to uncover new variables. We have therefore undertaken the task of creating a catalog of variable sources based on archival HST photometry. In particular, we have used version 3 of the Hubble Source Catalog (HSC), which relies on publicly available images obtained with the WFPC2, ACS, and WFC3 instruments on board the HST. Methods. We adopted magnitude-dependent thresholding in median absolute deviation (a robust measure of light curve scatter) combined with sophisticated preprocessing techniques and visual quality control to identify and validate variable sources observed by Hubble with the same instrument and filter combination five or more times. Results. The Hubble Catalog of Variables (HCV) includes 84,428 candidate variable sources (out of 3.7 million HSC sources that were searched for variability) with V ≤ 27 mag; for 11,115 of them the variability is detected in more than one filter. The data points in the light curves of the variables in the HCV catalog range from five to 120 points (typically having less than ten points); the time baseline ranges from under a day to over 15 years; while ∼8% of all variables have amplitudes in excess of 1 mag. Visual inspection performed on a subset of the candidate variables suggests that at least 80 % of the candidate variables that passed our automated quality control are true variable sources rather than spurious detections resulting from blending, residual cosmic rays, and calibration errors.Conclusions. The HCV is the first, homogeneous catalog of variable sources created from the highly diverse, archival HST data and currently is the deepest catalog of variables available. The catalog includes variable stars in our Galaxy and nearby galaxies, as well as transients and variable active galactic nuclei. We expect that the catalog will be a valuable resource for the community. Possible uses include searches for new variable objects of a particular type for population analysis, detection of unique objects worthy of follow-up studies, identification of sources observed at other wavelengths, and photometric characterization of candidate progenitors of supernovae and other transients in nearby galaxies. The catalog is available to the community from the ESA Hubble Science Archive (eHST) at the European Space Astronomy Centre (ESAC) and the Mikulski Archive for Space Telescopes (MAST) at Space Telescope Science Institute (STScI).

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Extending Supernova Spectral Templates for Next-generation Space Telescope Observations

Cited by 49 publications

References 71 publications

SNIa-Cosmology Analysis Results from Simulated LSST Images: from Difference Imaging to Constraints on Dark Energy

SNIa-Cosmology Analysis Results from Simulated LSST Images: from Difference Imaging to Constraints on Dark Energy

Photometric Classification of Early-Time Supernova Lightcurves with SCONE

The Hubble Catalog of Variables (HCV)

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