The DES Science Verification weak lensing shear catalogues

Jarvis, Mike; Sheldon, E.; Zuntz, J.; Kacprzak, T.; Bridle, Sarah; Amara, Adam; Armstrong, R.; Becker, M. R.; Bernstein, G. M.; Bonnett, C.; Chang, C.; Das, Ritanjan; Dietrich, J. P.; Drlica-Wagner, A.; Eifler, T. F.; Gangkofner, C.; Gruen, D.; Hirsch, Michael; Huff, Eric; Jain, Bhuvnesh; Kent, Stephen M.; Kirk, D.; MacCrann, N.; Melchior, P.; Plazas, A. A.; Réfrégier, Alexandre; Rowe, Barnaby; Rykoff, E. S.; Samuroff, S.; Sánchez, C.; Suchyta, E.; Troxel, M. A.; Vikram, V.; Abbott, T. M. C.; Abdalla, F. B.; Allam, S.; Annis, J.; Benoit-Lévy, A.; Bertin, E.; Brooks, D.; Buckley-Geer, E.; Burke, D. L.; Capozzi, D.; Rosell, A. Carnero; Kind, M. Carrasco; Carretero, J.; Castander, F. J.; Clampitt, Joseph; Crocce, M.; Cunha, C. E.; D’Andrea, C. B.; Costa, L. N. da; DePoy, D. L.; Desai, S.; Diehl, H. T.; Doel, P.; Neto, A. Fausti; Flaugher, B.; Fosalba, P.; Frieman, Joshua A.; Gaztañaga, E.; Gerdes, D. W.; Gruendl, R. A.; Gutiérrez, G.; Honscheid, K.; James, D. J.; Kuêhn, K.; Kuropatkin, N.; Lahav, O.; Li, T. S.; Lima, M.; March, M.; Martini, Paul; Miquel, R.; Mohr, J. J.; Neilsen, Eric H.; Nord, B.; Ogando, R. L. C.; Reil, K.; Romer, A. K.; Roodman, A.; Šako, M.; Sánchez, E.; Scarpine, V.; Schubnell, M.; Sevilla-Noarbe, I.; Smith, R. C.; Soares-Santos, M.; Sobreira, F.; Swanson, M. E. C.; Tarlè, G.; Thaler, J. J.; Thomas, Daniel; Walker, A. R.; Wechsler, Risa H.

doi:10.1093/mnras/stw990

Cited by 176 publications

(268 citation statements)

References 102 publications

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“…To circumvent this issue, we used ngmix 51 Jarvis et al 2016;Drlica-Wagner et al 2018) to fit the flux and morphology of each source over all individual single-epoch images simultaneously. When fitting each source, we masked nearby neighbors using the uberseg map, which was derived from the SExtractor co-add segmentation maps (Section 5.2 of Jarvis et al 2016). 52 Throughout this paper, quoted magnitudes and errors were derived from fitting a PSF model to each source using ngmix (i.e., PSF MAG _ and PSF MAG ERR _ _ ).…”

Section: Data Setmentioning

confidence: 99%

Stellar Streams Discovered in the Dark Energy Survey

Shipp

Drlica-Wagner

Balbinot

et al. 2018

ApJ

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246

254

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We perform a search for stellar streams around the Milky Way using the first 3 yr of multiband optical imaging data from the Dark Energy Survey (DES). We use DES data covering ∼5000 deg 2 to a depth of g>23.5 with a relative photometric calibration uncertainty of <1%. This data set yields unprecedented sensitivity to the stellar density field in the southern celestial hemisphere, enabling the detection of faint stellar streams to a heliocentric distance of ∼50 kpc. We search for stellar streams using a matched filter in color-magnitude space derived from a synthetic isochrone of an old, metal-poor stellar population. Our detection technique recovers four previously known thin stellar streams: Phoenix, ATLAS, Tucana III, and a possible extension of Molonglo. In addition, we report the discovery of 11 new stellar streams. In general, the new streams detected by DES are fainter, more distant, and lower surface brightness than streams detected by similar techniques in previous photometric surveys. As a by-product of our stellar stream search, we find evidence for extratidal stellar structure associated with four globular clusters: NGC 288, NGC 1261, NGC 1851, and NGC 1904. The ever-growing sample of stellar streams will provide insight into the formation of the Galactic stellar halo, the Milky Way gravitational potential, and the large-and small-scale distribution of dark matter around the Milky Way.

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Section: Data Setmentioning

confidence: 99%

Stellar Streams Discovered in the Dark Energy Survey

Shipp

Drlica-Wagner

Balbinot

et al. 2018

ApJ

Self Cite

246

254

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“…The DES Data Management system is supported by the National Science Foundation under Grant Number (1138766) (Jarvis et al 2016). …”

Section: Ac K N Ow L E D G E M E N T Smentioning

confidence: 99%

The Catalina Surveys Southern periodic variable star catalogue

Drake

Djorgovski

Catelan

et al. 2017

Monthly Notices of the Royal Astronomical Society

166

133

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Here, we present the results from our analysis of 6 yr of optical photometry taken by the Siding Spring Survey (SSS). This completes a search for periodic variable stars within the 30 000 deg 2 of the sky covered by the Catalina Surveys. The current analysis covers 81 million sources with declinations between −20 • and −75 • with median magnitudes in the range 11 < V < 19.5. We find approximately 34 000 new periodic variable stars in addition to the ∼9000 RR Lyrae that we previously discovered in SSS data. This brings the total number of periodic variables identified in Catalina data to ∼110 000. The new SSS periodic variable stars mainly consist of eclipsing binaries, RR Lyrae, LPVs, RS CVn stars, δ Scutis, and Anomalous Cepheids. By cross-matching these variable stars with those from prior surveys, we find that ∼90 per cent of the sources are new discoveries and recover ∼95 per cent of the known periodic variables in the survey region. For the known sources, we find excellent agreement between our catalogue and prior values of luminosity, period, and amplitude. However, we find many variable stars that had previously been misclassified. Examining the distribution of RR Lyrae, we find a population associated with the Large Magellanic Cloud (LMC) that extends more than 20 • from its centre confirming recent evidence for the existence of a very extended stellar halo in the LMC. By combining SSS photometry with Dark Energy Survey data, we identify additional LMC halo RR Lyrae, thus confirming the significance of the population.

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“…We expect systematic uncertainties to be increasingly important as the redshift of the clusters increases due to photometric redshift and shape measurement biases [e.g., 34,68]. For this reason, we compare constraints from clusters with redshifts 0 < z < 1 and 0 < z < 2.…”

Section: Optical Weak Lensingmentioning

confidence: 99%

“…We consider the dark energy equation of state parametrized as w(a) = w + (1 − a)w a (34) and forecast constraints either on {w} (with w a = 0) or on the combinations {w, w a }, {Σm ν , w} and {Σm ν , w, w a }. Measurements of the CMB lensing power spectrum are sensitive to the integrated matter power spectrum and lack redshift resolution.…”

Section: E the Dark Energy Equation Of State W(a)mentioning

confidence: 99%

Fundamental physics from future weak-lensing calibrated Sunyaev-Zel’dovich galaxy cluster counts

2017

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Future high-resolution measurements of the cosmic microwave background (CMB) will produce catalogs of tens of thousands of galaxy clusters through the thermal Sunyaev-Zel'dovich (tSZ) effect. We forecast how well different configurations of a CMB Stage-4 experiment can constrain cosmological parameters, in particular the amplitude of structure as a function of redshift σ8(z), the sum of neutrino masses Σmν , and the dark energy equation of state w(z). A key element of this effort is calibrating the tSZ scaling relation by measuring the lensing signal around clusters. We examine how the mass calibration from future optical surveys like the Large Synoptic Survey (LSST) compares with a purely internal calibration using lensing of the CMB itself. We find that, due to its high-redshift leverage, internal calibration gives constraints on cosmological parameters comparable to the optical calibration, and can be used as a cross-check of systematics in the optical measurement. We also show that in contrast to the constraints using the CMB lensing power spectrum, lensing-calibrated tSZ cluster counts can detect a minimal Σmν at the 3-5σ level even when the dark energy equation of state is freed up.

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The DES Science Verification weak lensing shear catalogues

Cited by 176 publications

References 102 publications

Stellar Streams Discovered in the Dark Energy Survey

Stellar Streams Discovered in the Dark Energy Survey

The Catalina Surveys Southern periodic variable star catalogue

Fundamental physics from future weak-lensing calibrated Sunyaev-Zel’dovich galaxy cluster counts

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