<i>Euclid</i>mission: building of a reference survey

Amiaux, J.; Scaramella, R.; Mellier, Y.; Altiéri, B.; Burigana, C.; Silva, Antônio da; Gomez, P.; Hoar, J.; Laureijs, R. J.; Maiorano, E.; Oliveira, D. Magalhães; Renk, Florian; Criado, Gonzalo Saavedra; Tereno, I.; Auguères, J.-L.; Brinchmann, Jarle; Cropper, Mark; Duvet, L.; Ealet, A.; Franzetti, P.; Garilli, B.; Gondoin, P.; Guzzo, L.; Hoekstra, Henk; Holmes, Rory; Jahnke, K.; Kitching, Thomas D.; Meneghetti, M.; Percival, Will J.; Warren, Steve

doi:10.1117/12.926513

Cited by 51 publications

(31 citation statements)

References 5 publications

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“…Weak gravitational lensing (WL) is a promising cosmological probe for constraining the dark energy equation of state w, and has been considered by a range of past (CFHTLens [2,3], COSMOS [4]), ongoing (DES [5]) and future (LSST [6], Euclid [7], WFIRST [8]) experiments. In an era where cosmology is data driven, accurate numerical simulations of shear fields are becoming important for several reasons, including assessing baryonic effects [9][10][11][12][13][14], the utility of non-Gaussian statistics [15][16][17][18][19][20][21][22][23] and various systematic effects [24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Sample variance in weak lensing: How many simulations are required?

2016

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Constraining cosmology using weak gravitational lensing consists of comparing a measured feature vector of dimension N b with its simulated counterpart. An accurate estimate of the N b × N b feature covariance matrix C is essential to obtain accurate parameter confidence intervals. When C is measured from a set of simulations, an important question is how large this set should be. To answer this question, we construct different ensembles of Nr realizations of the shear field, using a common randomization procedure that recycles the outputs from a smaller number Ns ≤ Nr of independent ray-tracing N -body simulations. We study parameter confidence intervals as a function of (Ns, Nr) in the range 1 ≤ Ns ≤ 200 and 1 ≤ Nr 10 5 . Previous work [1] has shown that Gaussian noise in the feature vectors (from which the covariance is estimated) lead, at quadratic order, to an O(1/Nr) degradation of the parameter confidence intervals. Using a variety of lensing features measured in our simulations, including shear-shear power spectra and peak counts, we show that cubic and quartic covariance fluctuations lead to additional O(1/N 2 r ) error degradation that is not negligible when Nr is only a factor of few larger than N b . We study the large Nr limit, and find that a single, 240Mpc/h sized 512 3 -particle N -body simulation (Ns = 1) can be repeatedly recycled to produce as many as Nr = few × 10 4 shear maps whose power spectra and high-significance peak counts can be treated as statistically independent. As a result, a small number of simulations (Ns = 1 or 2) is sufficient to forecast parameter confidence intervals at percent accuracy.PACS numbers: 95.36.+x, 95.30.Sf, 98.62.Sb

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Section: Introductionmentioning

confidence: 99%

Sample variance in weak lensing: How many simulations are required?

2016

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“…In such cases, galactic blends can be severe enough to warrant discarding the images. Current surveys such as DES (Dark Energy Survey) (Abbott et al 2018), KiDs (Kilo-Degree Survey) (de Jong et al 2013) and HSC (Hyper Suprime-Cam) (Miyazaki et al 2018) as well as future surveys such as LSST (Large Synoptic Survey Telescope) (LSST Science Collaboration et al 2009), Euclid (Amiaux et al 2012) and WFIRST (Wide-Field Infrared Survey Telescope) (Gehrels et al 2015) will generate immense quantities of imaging data within the next decade. Thus, a robust and high-throughput solution to galaxy image deblending is vital for drawing maximal value from nearfuture surveys.…”

Section: Introductionmentioning

confidence: 99%

Deblending galaxy superpositions with branched generative adversarial networks

Reiman

Göhre

2019

Monthly Notices of the Royal Astronomical Society

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Near-future large galaxy surveys will encounter blended galaxy images at a fraction of up to 50% in the densest regions of the universe. Current deblending techniques may segment the foreground galaxy while leaving missing pixel intensities in the background galaxy flux. The problem is compounded by the diffuse nature of galaxies in their outer regions, making segmentation significantly more difficult than in traditional object segmentation applications. We propose a novel branched generative adversarial network (GAN) to deblend overlapping galaxies, where the two branches produce images of the two deblended galaxies. We show that generative models are a powerful engine for deblending given their innate ability to infill missing pixel values occluded by the superposition. We maintain high peak signal-to-noise ratio and structural similarity scores with respect to ground truth images upon deblending. Our model also predicts near-instantaneously, making it a natural choice for the immense quantities of data soon to be created by large surveys such as LSST, Euclid and WFIRST.

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“…LSST will be the deepest wide-field optical survey ever conducted, performing multiple scans of the entire Southern sky over ten years, with an estimated 15 TB of data generated per night. Euclid (Amiaux et al 2012) is a wide-field space telescope that is due to commence operation in 2022. It will conduct a weak lensing survey to probe the nature of Dark Matter and Dark Energy, with an estimated 1PB of data generated per year.…”

Section: Introductionmentioning

confidence: 99%

Deep-CEE I: fishing for galaxy clusters with deep neural nets

Chan

Stott

2019

Monthly Notices of the Royal Astronomical Society

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We introduce Deep-CEE (Deep Learning for Galaxy Cluster Extraction and Evaluation), a proof of concept for a novel deep learning technique, applied directly to wide-field colour imaging to search for galaxy clusters, without the need for photometric catalogues. This technique is complementary to traditional methods and could also be used in combination with them to confirm galaxy cluster candidates. We use a state-of-the-art probabilistic algorithm, adapted to localise and classify galaxy clusters from other astronomical objects in SDSS imaging. As there is an abundance of labelled data for galaxy clusters from previous classifications in publicly available catalogues, we do not need to rely on simulated data. This means we keep our training data as realistic as possible, which is advantageous when training a deep learning algorithm. Ultimately, we will apply our model to surveys such as LSST and Euclid to probe wider and deeper into unexplored regions of the Universe. This will produce large samples of both high redshift and low mass clusters, which can be utilised to constrain both environment-driven galaxy evolution and cosmology.

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Euclidmission: building of a reference survey

Cited by 51 publications

References 5 publications

Sample variance in weak lensing: How many simulations are required?

Sample variance in weak lensing: How many simulations are required?

Deblending galaxy superpositions with branched generative adversarial networks

Deep-CEE I: fishing for galaxy clusters with deep neural nets

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