Dark energy survey operations: years 4 and 5

Diehl, H. T.; Neilsen, Eric H.; Gruendl, R. A.; Abbott, T. M. C.; Allam, S.; Álvarez, O.; Annis, J.; Balbinot, E.; Bhargava, S.; Bechtol, K.; Bernstein, G. M.; Bhatawdekar, Rachana; Bocquet, S.; Brout, D.; Capasso, R.; Cawthon, R.; Chang, C.; Cook, Erika; Conselice, C. J.; Cruz, Jaime Moreno de la; D’Andrea, C. B.; Costa, L. Da; Das, Ritanjan; DePoy, D. L.; Drlica-Wagner, A.; Elliott, A.; Everett, S.; Frieman, Joshua A.; Neto, A. Fausti; Ferté, A.; Friswell, Indiarose; Furnell, K.; Gelman, Landon; Gerdes, D. W.; Gill, M. S.; Goldstein, Daniel A.; Gruen, David; Gulledge, D. J.; Hamilton, S.; Hollowood, D. L.; Honscheid, K.; James, D. J.; Johnson, Michael D.; Johnson, M. W. G.; Kent, Stephen M.; Kessler, R.; Khullar, Gourav; Kovacs, E.; Kremin, Anthony; Krön, Richard G.; Kuropatkin, N.; Lasker, J.; Lathrop, A.; Li, T. S.; Manera, Marc; March, M.; Marshall, J. L.; Medford, Michael S.; Menanteau, F.; Mohammed, Irshad; Monroy, M Rodriguez; Moraes, Bruno; Morganson, E.; Muir, J.; Murphy, Michael T.; Nord, B.; Pace, Andrew B.; Palmese, A.; Park, Y.; Paz-Chinchón, F.; Pereira, M. E. S.; Petravick, D.; Plazas, A. A.; Poh, J.; Prochaska, Travis; Reil, K.; Romer, A. K.; Roodman, A.; Šako, M.; Sauseda, M.; Scolnic, D.; Secco, L. F.; Sevilla-Noarbe, I.; Shipp, Nora; Smith, J. A.; Soares-Santos, M.; Soergel, Bjoern; Stebbins, Albert; Story, K. T.; Stringer, K. M.; Tarsitano, F.; Thomas, B. P.; Tucker, D. L.; Vivas, A. K.; Walker, A. R.; Wang, M. Y.; Weaverdyck, Curtis; Weaverdyck, N.; Wester, W.; Wethers, Clare; Wilkinson, R. D.; Wu, Hao-Yi; Yanny, B.; Zenteno, A.; Zhang, Y.

doi:10.1117/12.2312113

Cited by 17 publications

(8 citation statements)

References 15 publications

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“…The DES-SN program (DES-SN; Bernstein et al 2012), which uses ≈ 10 per cent of the total survey time, surveys 10 DECam pointings, imaging 27 deg 2 in g, r, i and z filters with an approximate 7-day cadence. For a detailed discussion of the DES-SN observing strategy see Kessler et al (2015); Diehl et al (2018).…”

Section: The Dark Energy Surveymentioning

confidence: 99%

Superluminous supernovae from the Dark Energy Survey

Angus

Smith

Sullivan

et al. 2019

Monthly Notices of the Royal Astronomical Society

104

110

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We present a sample of 21 hydrogen-free superluminous supernovae (SLSNe-I), and one hydrogen-rich SLSN (SLSN-II) detected during the five-year Dark Energy Survey (DES). These SNe, located in the redshift range 0.220 < z < 1.998, represent the largest homogeneously-selected sample of SLSN events at high redshift. We present the observed g, r, i, z light curves for these SNe, which we interpolate using Gaussian Processes. The resulting light curves are analysed to determine the luminosity function of SLSN-I, and their evolutionary timescales. The DES SLSN-I sample significantly broadens the distribution of SLSN-I light curve properties when combined with existing samples from the literature. We fit a magnetar model to our SLSNe, and find that this model alone is unable to replicate the behaviour of many of the bolometric light curves. We search the DES SLSN-I light curves for the presence of initial peaks prior to the main light-curve peak. Using a shock breakout model, our Monte Carlo search finds that 3 of our 14 events with pre-max data display such initial peaks. However, 10 events show no evidence for such peaks, in some cases down to an absolute magnitude of < −16, suggesting that such features are not ubiquitous to all SLSN-I events. We also identify a red pre-peak feature within the light curve of one SLSN, which is comparable to that observed within SN2018bsz.

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Section: The Dark Energy Surveymentioning

confidence: 99%

Superluminous supernovae from the Dark Energy Survey

Angus

Smith

Sullivan

et al. 2019

Monthly Notices of the Royal Astronomical Society

104

110

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“…The DES observational strategy is fully described in Diehl et al (2016Diehl et al ( , 2018 but we summarize the relevant details here. The "wide" portion of the survey aims to completely tile the 5000 deg 2 footprint with 10 × 90s exposures in each of the g, r, i, and z bands, and 6 × 45 + 2 × 90 s in Y band, such that the completed wide survey will comprise ≈ 80, 000 DECam exposures.…”

Section: Observationsmentioning

confidence: 99%

Trans-Neptunian Objects Found in the First Four Years of the Dark Energy Survey

Bernardinelli

Bernstein

Šako

et al. 2020

ApJS

Self Cite

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We present a catalog of 316 trans-Neptunian bodies detected by the Dark Energy Survey (DES ). These objects include 245 discoveries by DES (139 not previously published) detected in ≈ 60, 000 exposures from the first four seasons of the survey ("Y4" data). The survey covers a contiguous 5000 deg 2 of the southern sky in the grizY optical/NIR filter set, with a typical TNO in this part of the sky being targeted by 25 − 30 Y4 exposures. We describe the processes for detection of transient sources and the linkage into TNO orbits, which are made challenging by the absence of the fewhour repeat observations employed by TNO-optimized surveys. We also describe the procedures for determining detection efficiencies vs. magnitude and estimating rates of false-positive linkages. This work presents all TNOs which were detected on ≥ 6 unique nights in the Y4 data and pass a "subthreshold confirmation" test wherein we demand the the object be detectable in a stack of the individual images in which the orbit indicates an object should be present, but was not detected. This eliminates false positives and yields TNO detections complete to r 23.3 mag with virtually no dependence on orbital properties for bound TNOs at distance 30 AU < d < 2500 AU. The final DES TNO catalog is expected to yield > 0.3 mag more depth, and arcs of > 4 years for nearly all detections.

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“…The number of epochs is~4-6 per coadd object per band, with a limiting magnitude in r of 24.9 and a pixel scale of 0.263 /pixel. The mean seeing is 1.06 in g (Diehl et al 2018). We generate postage stamps in g, r, i and bands of dimensions 100x100 pixels for each of the million sources in our target catalogue.…”

Section: Scoring and Sorting Candidate Sourcesmentioning

confidence: 99%

Finding high-redshift strong lenses in DES using convolutional neural networks

Jacobs

Collett

Glazebrook

et al. 2019

Monthly Notices of the Royal Astronomical Society

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We search Dark Energy Survey (DES) Year 3 imaging data for galaxy-galaxy strong gravitational lenses using convolutional neural networks. We generate 250,000 simulated lenses at redshifts > 0.8 from which we create a data set for training the neural networks with realistic seeing, sky and shot noise. Using the simulations as a guide, we build a catalogue of 1.1 million DES sources with 1.8 < g − i < 5, 0.6 < g − r < 3, r_mag > 19, g_mag > 20 and i_mag > 18.2. We train two ensembles of neural networks on training sets consisting of simulated lenses, simulated non-lenses, and real sources. We use the neural networks to score images of each of the sources in our catalogue with a value from 0 to 1, and select those with scores greater than a chosen threshold for visual inspection, resulting in a candidate set of 7,301 galaxies. During visual inspection we rate 84 as "probably" or "definitely" lenses. Four of these are previously known lenses or lens candidates. We inspect a further 9,428 candidates with a different score threshold, and identify four new candidates. We present 84 new strong lens candidates, selected after a few hours of visual inspection by astronomers. This catalogue contains a comparable number of high-redshift lenses to that predicted by simulations. Based on simulations we estimate our sample to contain most discoverable lenses in this imaging and at this redshift range.

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Dark energy survey operations: years 4 and 5

Cited by 17 publications

References 15 publications

Superluminous supernovae from the Dark Energy Survey

Superluminous supernovae from the Dark Energy Survey

Trans-Neptunian Objects Found in the First Four Years of the Dark Energy Survey

Finding high-redshift strong lenses in DES using convolutional neural networks

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