scholarly journals Dark Energy Survey Year 3 Results: Measuring the Survey Transfer Function with Balrog

2022 ◽  
Vol 258 (1) ◽  
pp. 15
Author(s):  
S. Everett ◽  
B. Yanny ◽  
N. Kuropatkin ◽  
E. M. Huff ◽  
Y. Zhang ◽  
...  
Keyword(s):  
Dark Energy ◽  
Transfer Function ◽  
Real Data ◽  
Generative Models ◽  
Density Fluctuations ◽  
Proximity Effects ◽  
Small Subset ◽  
Data Set ◽  
Dark Energy Survey ◽  
Energy Survey

Abstract We describe an updated calibration and diagnostic framework, Balrog, used to directly sample the selection and photometric biases of the Dark Energy Survey (DES) Year 3 (Y3) data set. We systematically inject onto the single-epoch images of a random 20% subset of the DES footprint an ensemble of nearly 30 million realistic galaxy models derived from DES Deep Field observations. These augmented images are analyzed in parallel with the original data to automatically inherit measurement systematics that are often too difficult to capture with generative models. The resulting object catalog is a Monte Carlo sampling of the DES transfer function and is used as a powerful diagnostic and calibration tool for a variety of DES Y3 science, particularly for the calibration of the photometric redshifts of distant “source” galaxies and magnification biases of nearer “lens” galaxies. The recovered Balrog injections are shown to closely match the photometric property distributions of the Y3 GOLD catalog, particularly in color, and capture the number density fluctuations from observing conditions of the real data within 1% for a typical galaxy sample. We find that Y3 colors are extremely well calibrated, typically within ∼1–8 mmag, but for a small subset of objects, we detect significant magnitude biases correlated with large overestimates of the injected object size due to proximity effects and blending. We discuss approaches to extend the current methodology to capture more aspects of the transfer function and reach full coverage of the survey footprint for future analyses.

scholarly journals Dark energy survey internal consistency tests of the joint cosmological probes analysis with posterior predictive distributions

2021 ◽  
Vol 503 (2) ◽  
pp. 2688-2705
Author(s):  
C Doux ◽  
E Baxter ◽  
P Lemos ◽  
C Chang ◽  
A Alarcon ◽  
...  
Keyword(s):  
Dark Energy ◽  
Internal Consistency ◽  
Goodness Of Fit ◽  
Small Scale ◽  
P Value ◽  
Posterior Predictive Distribution ◽  
Data Set ◽  
Dark Energy Survey ◽  
Data Vector ◽  
Energy Survey

ABSTRACT Beyond ΛCDM, physics or systematic errors may cause subsets of a cosmological data set to appear inconsistent when analysed assuming ΛCDM. We present an application of internal consistency tests to measurements from the Dark Energy Survey Year 1 (DES Y1) joint probes analysis. Our analysis relies on computing the posterior predictive distribution (PPD) for these data under the assumption of ΛCDM. We find that the DES Y1 data have an acceptable goodness of fit to ΛCDM, with a probability of finding a worse fit by random chance of p = 0.046. Using numerical PPD tests, supplemented by graphical checks, we show that most of the data vector appears completely consistent with expectations, although we observe a small tension between large- and small-scale measurements. A small part (roughly 1.5 per cent) of the data vector shows an unusually large departure from expectations; excluding this part of the data has negligible impact on cosmological constraints, but does significantly improve the p-value to 0.10. The methodology developed here will be applied to test the consistency of DES Year 3 joint probes data sets.

scholarly journals Galaxy formation and evolution with the Dark Energy Survey

2012 ◽  
Vol 8 (S295) ◽  
pp. 137-140
Author(s):  
Diego Capozzi ◽  
Daniel Thomas ◽  
Claudia Maraston ◽  
Luke J. M. Davies
Keyword(s):  
Dark Energy ◽  
Galaxy Evolution ◽  
Galaxy Formation ◽  
Large Scale ◽  
Time Range ◽  
Data Set ◽  
Dark Energy Survey ◽  
Galaxy Formation And Evolution ◽  
Formation And Evolution ◽  
Energy Survey

AbstractThe Dark Energy Survey (DES) will be the new state-of the-art in large-scale galaxy imaging surveys. With 5,000 deg2, it will cover an area of the sky similar to SDSS-II, but will go over two magnitudes deeper, reaching 24th magnitude in all four optical bands (griz). DES will further provide observations in the redder Y-band and will be complemented with VISTA observations in the near-infrared bands JHK. Hence DES will furnish an unprecedented combination of sky and wavelength coverage and depth, unreached by any of the existing galaxy surveys. The very nature of the DES data set – large volume at intermediate photometric depth – allows us to probe galaxy formation and evolution within a cosmic-time range of ~ 10 Gyr and in different environments. In fact there will be many galaxy clusters available for galaxy evolution studies, given that one of the main aims of DES is to use their abundance to constrain the equation of state of dark energy. The X-ray follow up of these clusters, coupled with the use of gravitational lensing, will provide very precise measures of their masses, enabling us to study in detail the influence of the environment on galaxy formation and evolution processes. DES will leverage the study of these processes by allowing us to perform a detailed investigation of the galaxy luminosity and stellar mass functions and of the relationship between dark and baryonic matter as described by the Halo Occupation Distribution.

scholarly journals Linear Systematics Mitigation in Galaxy Clustering in the Dark Energy Survey Year 1 Data

2021 ◽  
Author(s):  
Erika L Wagoner ◽  
Eduardo Rozo ◽  
Xiao Fang ◽  
Martín Crocce ◽  
Jack Elvin-Poole ◽  
...  
Keyword(s):  
Correlation Function ◽  
Dark Energy ◽  
Goodness Of Fit ◽  
Density Field ◽  
Data Set ◽  
Galaxy Clustering ◽  
Dark Energy Survey ◽  
Significant Bias ◽  
The Galaxy ◽  
Energy Survey

Abstract We implement a linear model for mitigating the effect of observing conditions and other sources of contamination in galaxy clustering analyses. Our treatment improves upon the fiducial systematics treatment of the Dark Energy Survey (DES) Year 1 (Y1) cosmology analysis in four crucial ways. Specifically, our treatment 1) does not require decisions as to which observable systematics are significant and which are not, allowing for the possibility of multiple maps adding coherently to give rise to significant bias even if no single map leads to a significant bias by itself; 2) characterizes both the statistical and systematic uncertainty in our mitigation procedure, allowing us to propagate said uncertainties into the reported cosmological constraints; 3) explicitly exploits the full spatial structure of the galaxy density field to differentiate between cosmology-sourced and systematics-sourced fluctuations within the galaxy density field; 4) is fully automated, and can therefore be trivially applied to any data set The updated correlation function for the DES Y1 redMaGiC catalog minimally impacts the cosmological posteriors from that analysis. Encouragingly, our analysis does improve the goodness of fit statistic of the DES Y1 3×2pt data set (Δχ2 = −6.5 with no additional parameters). This improvement is due in nearly equal parts to both the change in the correlation function and the added statistical and systematic uncertainties associated with our method. We expect the difference in mitigation techniques to become more important in future work as the size of cosmological data sets grows.

scholarly journals Dark Energy Survey Year 3 Results: Photometric Data Set for Cosmology

2021 ◽  
Vol 254 (2) ◽  
pp. 24
Author(s):  
I. Sevilla-Noarbe ◽  
K. Bechtol ◽  
M. Carrasco Kind ◽  
A. Carnero Rosell ◽  
M. R. Becker ◽  
...  
Keyword(s):  
Dark Energy ◽  
Photometric Data ◽  
Data Set ◽  
Dark Energy Survey ◽  
Energy Survey

scholarly journals On the relative bias of void tracers in the Dark Energy Survey

2019 ◽  
Vol 487 (2) ◽  
pp. 2836-2852 ◽  
Author(s):  
G Pollina ◽  
N Hamaus ◽  
K Paech ◽  
K Dolag ◽  
J Weller ◽  
...  
Keyword(s):  
Dark Energy ◽  
Large Scale ◽  
Three Dimensional ◽  
Large Scale Structure ◽  
Density Fluctuations ◽  
Scale Structure ◽  
Relative Bias ◽  
Dark Energy Survey ◽  
Luminous Objects ◽  
Energy Survey

Abstract Luminous tracers of large-scale structure are not entirely representative of the distribution of mass in our Universe. As they arise from the highest peaks in the matter density field, the spatial distribution of luminous objects is biased towards those peaks. On large scales, where density fluctuations are mild, this bias simply amounts to a constant offset in the clustering amplitude of the tracer, known as linear bias. In this work we focus on the relative bias between galaxies and galaxy clusters that are located inside and in the vicinity of cosmic voids, extended regions of relatively low density in the large-scale structure of the Universe. With the help of mock data we verify that the relation between galaxy and cluster overdensity around voids remains linear. Hence, the void-centric density profiles of different tracers can be linked by a single multiplicative constant. This amounts to the same value as the relative linear bias between tracers for the largest voids in the sample. For voids of small sizes, which typically arise in higher density regions, this constant has a higher value, possibly showing an environmental dependence similar to that observed for the linear bias itself. We confirm our findings by analysing data obtained during the first year of observations by the Dark Energy Survey. As a side product, we present the first catalogue of three-dimensional voids extracted from a photometric survey with a controlled photo-z uncertainty. Our results will be relevant in forthcoming analyses that attempt to use voids as cosmological probes.

scholarly journals Dark Energy Survey Year 1 Results: The Photometric Data Set for Cosmology

2018 ◽  
Vol 235 (2) ◽  
pp. 33 ◽  
Author(s):  
A. Drlica-Wagner ◽  
I. Sevilla-Noarbe ◽  
E. S. Rykoff ◽  
R. A. Gruendl ◽  
B. Yanny ◽  
...  
Keyword(s):  
Dark Energy ◽  
Photometric Data ◽  
Data Set ◽  
Dark Energy Survey ◽  
Energy Survey

scholarly journals MODELING THE TRANSFER FUNCTION FOR THE DARK ENERGY SURVEY

2015 ◽  
Vol 801 (2) ◽  
pp. 73 ◽  
Author(s):  
C. Chang ◽  
M. T. Busha ◽  
R. H. Wechsler ◽  
A. Refregier ◽  
A. Amara ◽  
...  
Keyword(s):  
Dark Energy ◽  
Transfer Function ◽  
Dark Energy Survey ◽  
Energy Survey

scholarly journals Pushing automated morphological classifications to their limits with the Dark Energy Survey

2021 ◽  
Author(s):  
J Vega-Ferrero ◽  
H Domínguez Sánchez ◽  
M Bernardi ◽  
M Huertas-Company ◽  
R Morgan ◽  
...  
Keyword(s):  
Dark Energy ◽  
Classification Scheme ◽  
Learning Algorithm ◽  
Small Subset ◽  
Early Type ◽  
Lenticular Galaxies ◽  
Dark Energy Survey ◽  
Deep Learning Algorithm ◽  
Data Release ◽  
Energy Survey

Abstract We present morphological classifications of ∼27 million galaxies from the Dark Energy Survey (DES) Data Release 1 (DR1) using a supervised deep learning algorithm. The classification scheme separates: (a) early-type galaxies (ETGs) from late-types (LTGs), and (b) face-on galaxies from edge-on. Our Convolutional Neural Networks (CNNs) are trained on a small subset of DES objects with previously known classifications. These typically have mr ≲ 17.7mag; we model fainter objects to mr < 21.5 mag by simulating what the brighter objects with well determined classifications would look like if they were at higher redshifts. The CNNs reach 97% accuracy to mr < 21.5 on their training sets, suggesting that they are able to recover features more accurately than the human eye. We then used the trained CNNs to classify the vast majority of the other DES images. The final catalog comprises five independent CNN predictions for each classification scheme, helping to determine if the CNN predictions are robust or not. We obtain secure classifications for ∼ 87% and 73% of the catalog for the ETG vs. LTG and edge-on vs. face-on models, respectively. Combining the two classifications (a) and (b) helps to increase the purity of the ETG sample and to identify edge-on lenticular galaxies (as ETGs with high ellipticity). Where a comparison is possible, our classifications correlate very well with Sérsic index (n), ellipticity (ε) and spectral type, even for the fainter galaxies. This is the largest multi-band catalog of automated galaxy morphologies to date.

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