The coalescence of a binary neutron star pair is expected to produce gravitational waves (GW) and electromagnetic radiation, both of which may be detectable with currently available instruments. We ...describe a search for a predicted r-process optical transient from these mergers, dubbed the "kilonova" (KN), using griz broadband data from the Dark Energy Survey Supernova Program (DES-SN). Some models predict KNe to be redder, shorter-lived, and dimmer than supernovae (SNe), but the event rate of KNe is poorly constrained. We simulate KN and SN light curves with the Monte-Carlo simulation code SNANA to optimize selection requirements, determine search efficiency, and predict SN backgrounds. Our analysis of the first two seasons of DES-SN data results in 0 events, and is consistent with our prediction of 1.1 0.2 background events based on simulations of SNe. From our prediction, there is a 33% chance of finding 0 events in the data. Assuming no underlying galaxy flux, our search sets 90% upper limits on the KN volumetric rate of 1.0 Gpc−3 yr−1 for the dimmest KN model we consider (peak i-band absolute magnitude mag) and 2.4 Gpc−3 yr−1 for the brightest ( mag). Accounting for anomalous subtraction artifacts on bright galaxies, these limits are ∼3 times higher. This analysis is the first untriggered optical KN search and informs selection requirements and strategies for future KN searches. Our upper limits on the KN rate are consistent with those measured by GW and gamma-ray burst searches.
ABSTRACT
Strongly lensed quadruply imaged quasars (quads) are extraordinary objects. They are very rare in the sky and yet they provide unique information about a wide range of topics, including the ...expansion history and the composition of the Universe, the distribution of stars and dark matter in galaxies, the host galaxies of quasars, and the stellar initial mass function. Finding them in astronomical images is a classic ‘needle in a haystack’ problem, as they are outnumbered by other (contaminant) sources by many orders of magnitude. To solve this problem, we develop state-of-the-art deep learning methods and train them on realistic simulated quads based on real images of galaxies taken from the Dark Energy Survey, with realistic source and deflector models, including the chromatic effects of microlensing. The performance of the best methods on a mixture of simulated and real objects is excellent, yielding area under the receiver operating curve in the range of 0.86–0.89. Recall is close to 100 per cent down to total magnitude i ∼ 21 indicating high completeness, while precision declines from 85 per cent to 70 per cent in the range i ∼ 17–21. The methods are extremely fast: training on 2 million samples takes 20 h on a GPU machine, and 108 multiband cut-outs can be evaluated per GPU-hour. The speed and performance of the method pave the way to apply it to large samples of astronomical sources, bypassing the need for photometric pre-selection that is likely to be a major cause of incompleteness in current samples of known quads.
ABSTRACT We report the results of a Dark Energy Camera optical follow-up of the gravitational-wave (GW) event GW151226, discovered by the Advanced Laser Interferometer Gravitational-wave Observatory ...detectors. Our observations cover 28.8 deg2 of the localization region in the i and z bands (containing 3% of the BAYESTAR localization probability), starting 10 hr after the event was announced and spanning four epochs at 2-24 days after the GW detection. We achieve 5 point-source limiting magnitudes of i 21.7 and z 21.5 , with a scatter of 0.4 mag, in our difference images. Given the two-day delay, we search this area for a rapidly declining optical counterpart with 3 significance steady decline between the first and final observations. We recover four sources that pass our selection criteria, of which three are cataloged active galactic nuclei. The fourth source is offset by 5.8 arcsec from the center of a galaxy at a distance of 187 Mpc, exhibits a rapid decline by 0.5 mag over 4 days, and has a red color of i − z 0.3 mag. These properties could satisfy a set of cuts designed to identify kilonovae. However, this source was detected several times, starting 94 days prior to GW151226, in the Pan-STARRS Survey for Transients (dubbed as PS15cdi) and is therefore unrelated to the GW event. Given its long-term behavior, PS15cdi is likely a Type IIP supernova that transitioned out of its plateau phase during our observations, mimicking a kilonova-like behavior. We comment on the implications of this detection for contamination in future optical follow-up observations.
One of the central goals of multi-wavelength galaxy cluster cosmology is to unite all cluster observables to form a consistent understanding of cluster mass. Here, we study the impact of systematic ...effects from optical cluster catalogs on stacked Sunyaev-Zel'dovich (SZ) signals. We show that the optically predicted Y-decrement can vary by as much as 50% based on the current 2sigma systematic uncertainties in the observed mass-richness relationship. Miscentering and impurities will suppress the SZ signal compared to expectations for a clean and perfectly centered optical sample, but to a lesser degree. We show that the levels of these variations and suppression are dependent on the amount of systematics in the optical cluster catalogs. We also study X-ray luminosity-dependent sub-sampling of the optical catalog and find that it creates Malmquist bias, increasing the observed F-decrement of the stacked signal. We show that the current Planck measurements of the Y-decrement around Sloan Digital Sky Survey optical clusters and their X-ray counterparts are consistent with expectations after accounting for the 1sigma a optical systematic uncertainties using the Johnston mass-richness relation.
Abstract
We report the combined results of eight searches for strong gravitational lens systems in the full 5000 square degrees of Dark Energy Survey (DES) observations. The observations accumulated ...by the end of the third observing season fully covered the DES footprint in five filters (
grizY
), with an
i
-band limiting magnitude (at 10
σ
) of 23.44. In four searches, a list of potential candidates was identified using a color and magnitude selection from the object catalogs created from the first three observing seasons. Three other searches were conducted at the locations of previously identified galaxy clusters. Cutout images of potential candidates were then visually scanned using an object viewer. An additional set of candidates came from a data-quality check of a subset of the color–coadd tiles created from the full DES six-season data set. A short list of the most promising strong-lens candidates was then numerically ranked according to whether or not we judged them to be bona fide strong gravitational lens systems. These searches discovered a diverse set of 247 strong-lens candidate systems, of which 81 are identified for the first time. We provide the coordinates, magnitudes, and photometric properties of the lens and source objects, and an estimate of the Einstein radius for 81 new systems and 166 previously reported systems. This catalog will be of use for selecting interesting systems for detailed follow up, studies of galaxy cluster and group mass profiles, as well as a training/validation set for automated strong-lens searches.
Abstract
Large-scale astronomical surveys have the potential to capture data on large numbers of strongly gravitationally lensed supernovae (LSNe). To facilitate timely analysis and spectroscopic ...follow-up before the supernova fades, an LSN needs to be identified soon after it begins. To quickly identify LSNe in optical survey data sets, we designed ZipperNet, a multibranch deep neural network that combines convolutional layers (traditionally used for images) with long short-term memory layers (traditionally used for time series). We tested ZipperNet on the task of classifying objects from four categories—no lens, galaxy-galaxy lens, lensed Type-Ia supernova, lensed core-collapse supernova—within high-fidelity simulations of three cosmic survey data sets: the Dark Energy Survey, Rubin Observatory’s Legacy Survey of Space and Time (LSST), and a Dark Energy Spectroscopic Instrument (DESI) imaging survey. Among our results, we find that for the LSST-like data set, ZipperNet classifies LSNe with a receiver operating characteristic area under the curve of 0.97, predicts the spectroscopic type of the lensed supernovae with 79% accuracy, and demonstrates similarly high performance for LSNe 1–2 epochs after first detection. We anticipate that a model like ZipperNet, which simultaneously incorporates spatial and temporal information, can play a significant role in the rapid identification of lensed transient systems in cosmic survey experiments.
We report the observation and confirmation of the first group- and cluster-scale strong gravitational lensing systems found in Dark Energy Survey data. Through visual inspection of data from the ...Science Verification season, we identified 53 candidate systems. We then obtained spectroscopic follow-up of 21 candidates using the Gemini Multi-object Spectrograph at the Gemini South telescope and the Inamori-Magellan Areal Camera and Spectrograph at the Magellan/Baade telescope. With this follow-up, we confirmed six candidates as gravitational lenses: three of the systems are newly discovered, and the remaining three were previously known. Of the 21 observed candidates, the remaining 15 either were not detected in spectroscopic observations, were observed and did not exhibit continuum emission (or spectral features), or were ruled out as lensing systems. The confirmed sample consists of one group-scale and five galaxy-cluster-scale lenses. The lensed sources range in redshift z~ 0.80-3.2 and in i-band surface brightness i sub(SB)~ 23-25 mag arcsec super(-2)(2'' aperture). For each of the six systems, we estimate the Einstein radius theta sub(E) and the enclosed mass M sub(enc), which have ranges theta sub(E)~ 5''-9'' and M sub(enc)~ 8 x 10 super(12) to 6 x 10 super(13)M sub(middot in circle), respectively.
Generalized Fisher matrices Heavens, A F; Seikel, M; Nord, B D ...
Monthly notices of the Royal Astronomical Society,
12/2014, Letnik:
445, Številka:
2
Journal Article
Recenzirano
Odprti dostop
The Fisher Information Matrix formalism (Fisher 1935) is extended to cases where the data are divided into two parts (X, Y), where the expectation value of Y depends on X according to some ...theoretical model, and X and Y both have errors with arbitrary covariance. In the simplest case, (X, Y) represent data pairs of abscissa and ordinate, in which case the analysis deals with the case of data pairs with errors in both coordinates, but X can be any measured quantities on which Y depends. The analysis applies for arbitrary covariance, provided all errors are Gaussian, and provided the errors in X are small, both in comparison with the scale over which the expected signal Y changes, and with the width of the prior distribution. This generalizes the Fisher Matrix approach, which normally only considers errors in the 'ordinate' Y. In this work, we include errors in X by marginalizing over latent variables, effectively employing a Bayesian hierarchical model, and deriving the Fisher Matrix for this more general case. The methods here also extend to likelihood surfaces which are not Gaussian in the parameter space, and so techniques such as DALI (Derivative Approximation for Likelihoods) can be generalized straightforwardly to include arbitrary Gaussian data error covariances. For simple mock data and theoretical models, we compare to Markov Chain Monte Carlo experiments, illustrating the method with cosmological supernova data. We also include the new method in the FISHER4CAST software.
Abstract
Gravitationally lensed supernovae (LSNe) are important probes of cosmic expansion, but they remain rare and difficult to find. Current cosmic surveys likely contain 5–10 LSNe in total while ...next-generation experiments are expected to contain several hundred to a few thousand of these systems. We search for these systems in observed Dark Energy Survey (DES) five year SN fields—10 3 sq. deg. regions of sky imaged in the
griz
bands approximately every six nights over five years. To perform the search, we utilize the DeepZipper approach: a multi-branch deep learning architecture trained on image-level simulations of LSNe that simultaneously learns spatial and temporal relationships from time series of images. We find that our method obtains an LSN recall of 61.13% and a false-positive rate of 0.02% on the DES SN field data. DeepZipper selected 2245 candidates from a magnitude-limited (
m
i
< 22.5) catalog of 3,459,186 systems. We employ human visual inspection to review systems selected by the network and find three candidate LSNe in the DES SN fields.
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