An end-to-end simulation framework for the Large Synoptic Survey Telescope

Connolly, Andrew J.; Angeli, George Z.; Chandrasekharan, Srinivasan; Claver, Charles F.; Cook, K. H.; Ivezić, Željko; Jones, R. Lynne; Krughoff, K. Simon; Peng, E.; Peterson, J. R.; Petry, C. E.; Rasmussen, Andrew; Ridgway, Stephen T.; Saha, Abhijit; Sembroski, G. H.; Vanderplas, Jake; Yoachim, Peter

doi:10.1117/12.2054953

Cited by 42 publications

(22 citation statements)

References 9 publications

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“…We add a second contaminant to δg associated with star contamination. For the curved-sky realizations this contaminant is proportional to the fluctuation around the mean of a star density map generated using the LSST CatSim catalog (Connolly et al 2014). We modify this contaminant slightly by adding a white-noise component that dominates the small-scale spectrum beyond 400.…”

Section: Galaxy Clustering and Weak Lensingmentioning

confidence: 99%

A unified pseudo-Cℓ framework

Alonso

Sánchez

Slosar

2019

Monthly Notices of the Royal Astronomical Society

342

370

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The pseudo-C is an algorithm for estimating the angular power and cross-power spectra that is very fast and in realistic cases also nearly optimal. The algorithm can be extended to deal with contaminant deprojection and E/B purification, and can therefore be applied in a wide variety of scenarios of interest for current and future cosmological observations. This paper presents NaMaster, a public, validated, accurate and easy-to-use software package that, for the first time, provides a unified framework to compute angular cross-power spectra of any pair of spin-0 or spin-2 fields, contaminated by an arbitrary number of linear systematics and requiring B-or E-mode purification, both on the sphere or in the flat-sky approximation. We describe the mathematical background of the estimator, including all the features above, and its software implementation in NaMaster. We construct a validation suite that aims to resemble the types of observations that next-generation large-scale structure and ground-based CMB experiments will face, and use it to show that the code is able to recover the input power spectra in the most complex scenarios with no detectable bias. NaMaster can be found at https://github.com/LSSTDESC/NaMaster, and is provided with comprehensive documentation and a number of code examples.

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Section: Galaxy Clustering and Weak Lensingmentioning

confidence: 99%

A unified pseudo-Cℓ framework

Alonso

Sánchez

Slosar

2019

Monthly Notices of the Royal Astronomical Society

342

370

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“…Redshifts for the lens and source were selected from uniform distributions with upper limits of z lens = 2 and z source = 6, based on trial data sets and work by Collett (2015). Spectral energy distributions (SEDs) of appropriate ages were selected uniformly from LSST simulated object SEDs (Connolly et al 2014, available from the LSST GitHub repository) and redshifted accordingly. These SEDs use the Bruzual and Charlot models (Bruzual & Charlot 2003) with a Chabrier (2003) IMF, and either an exponential decline or an instantaneous burst of star formation.…”

Section: Lensed Image Simulationsmentioning

confidence: 99%

“…The data sets used for training and testing the CNN contained tens of thousands of single or multiband images (see Section 3 for further details). Multiband images were generated using three filters of the LSST with RGB=(i, r, g), using the LSST's CCD filter response function (Connolly et al 2014, available from the LSST GitHub repository), and likewise the Euclid VIS transmission curve. We adopted the native pixel scale of the LSST (0.2 arcsec pixel −1 ; Ivezić et al 2008;Abell et al 2009) and Euclid (0.1 arcsec pixel −1 ; Racca et al 2016), and based on the distribution of known Einstein radii the postage stamp images were fixed at 57×57 pixels.…”

Section: Lensed Image Simulationsmentioning

confidence: 99%

The use of convolutional neural networks for modelling large optically-selected strong galaxy-lens samples

Pearson

Dye

2019

Monthly Notices of the Royal Astronomical Society

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We explore the effectiveness of deep learning convolutional neural networks (CNNs) for estimating strong gravitational lens mass model parameters. We have investigated a number of practicalities faced when modelling real image data, such as how network performance depends on the inclusion of lens galaxy light, the addition of colour information and varying signal-to-noise. Our CNN was trained and tested with strong galaxy-galaxy lens images simulated to match the imaging characteristics of the Large Synoptic Survey Telescope (LSST) and Euclid. For images including lens galaxy light, the CNN can recover the lens model parameters with an acceptable accuracy, although a 34 per cent average improvement in accuracy is obtained when lens light is removed. However, the inclusion of colour information can largely compensate for the drop in accuracy resulting from the presence of lens light. While our findings show similar accuracies for single epoch Euclid VIS and LSST r-band data sets, we find a 24 per cent increase in accuracy by adding g-and i-band images to the LSST r-band without lens light and a 20 per cent increase with lens light. The best network performance is obtained when it is trained and tested on images where lens light exactly follows the mass, but when orientation and ellipticity of the light is allowed to differ from those of the mass, the network performs most consistently when trained with a moderate amount of scatter in the difference between the mass and light profiles.

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“…As templates we used SEDs from the LSST simulations (Connolly et al 2014) SED library 2 which are Bruzual & Charlot (2003) (BC03) model SEDs. The full library of 959 spectra samples 4 different star formation histories and span a range of ages from 1.585 Myr to 12.5 Gyr (using the Padova 1994 isochrones).…”

Section: Creation Of Training Setmentioning

confidence: 99%

“…The colors we plan on using for this technique are most likely those of an optical survey such as the Large Synoptic Survey Telescope (LSST). Using the latest version of the LSST bandpasses from the LSST simulations software stack (Connolly et al 2014) 3 we calculated the colors for our training and test SEDs. We followed the procedure outlined above only using the SEDs at wavelengths from 299 -1200 nm which covers the range of the LSST filters for the PCA stage.…”

Section: Optical Wavelengthsmentioning

confidence: 99%

Estimating Spectra from Photometry

Kalmbach

Connolly

2017

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Measuring the physical properties of galaxies such as redshift frequently requires the use of Spectral Energy Distributions (SEDs). SED template sets are, however, often small in number and cover limited portions of photometric color space. Here we present a new method to estimate SEDs as a function of color from a small training set of template SEDs. We first cover the mathematical background behind the technique before demonstrating our ability to reconstruct spectra based upon colors and then compare to other common interpolation and extrapolation methods. When the photometric filters and spectra overlap we show reduction of error in the estimated spectra of over 65% compared to the more commonly used techniques. We also show an expansion of the method to wavelengths beyond the range of the photometric filters. Finally, we demonstrate the usefulness of our technique by generating 50 additional SED templates from an original set of 10 and applying the new set to photometric redshift estimation. We are able to reduce the photometric redshifts standard deviation by at least 22.0% and the outlier rejected bias by over 86.2% compared to original set for z ≤ 3.

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An end-to-end simulation framework for the Large Synoptic Survey Telescope

Cited by 42 publications

References 9 publications

A unified pseudo-Cℓ framework

A unified pseudo-Cℓ framework

The use of convolutional neural networks for modelling large optically-selected strong galaxy-lens samples

Estimating Spectra from Photometry

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