| 1. |
- Taylor, Peter L., et al.
(författare)
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Cosmic shear : Inference from forward models
- 2019
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Ingår i: Physical Review D. - 2470-0010 .- 2470-0029. ; 100:2
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Tidskriftsartikel (refereegranskat)abstract
- Density-estimation likelihood-free inference (DELFI) has recently been proposed as an efficient method for simulation-based cosmological parameter inference. Compared to the standard likelihood-based Markov chain Monte Carlo (MCMC) approach, DELFI has several advantages: it is highly parallelizable, there is no need to assume a possibly incorrect functional form for the likelihood, and complicated effects (e.g., the mask and detector systematics) are easier to handle with forward models. In light of this, we present two DELFI pipelines to perform weak lensing parameter inference with log-normal realizations of the tomographic shear field-using the C-l summary statistic. The first pipeline accounts for the non-Gaussianities of the shear field, intrinsic alignments, and photometric-redshift error. We validate that it is accurate enough for Stage III experiments and estimate that O(1000) simulations are needed to perform inference on Stage IV data. By comparing the second DELFI pipeline, which makes no assumption about the functional form of the likelihood, with the standard MCMC approach, which assumes a Gaussian likelihood, we test the impact of the Gaussian likelihood approximation in the MCMC analysis. We find it has a negligible impact on Stage IV parameter constraints. Our pipeline is a step towards seamlessly propagating all data-processing, instrumental, theoretical, and astrophysical systematics through to the final parameter constraints.
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| 2. |
- Lochner, Michelle, et al.
(författare)
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Optimizing the LSST Observing Strategy for Dark Energy Science : DESC Recommendations for the Wide-Fast-Deep Survey
- 2018
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Cosmology is one of the four science pillars of LSST, which promises to be transformative for our understanding of dark energy and dark matter. The LSST Dark Energy Science Collaboration (DESC) has been tasked with deriving constraints on cosmological parameters from LSST data. Each of the cosmological probes for LSST is heavily impacted by the choice of observing strategy. This white paper is written by the LSST DESC Observing Strategy Task Force (OSTF), which represents the entire collaboration, and aims to make recommendations on observing strategy that will benefit all cosmological analyses with LSST. It is accompanied by the DESC DDF (Deep Drilling Fields) white paper (Scolnic et al.). We use a variety of metrics to understand the effects of the observing strategy on measurements of weak lensing, large-scale structure, clusters, photometric redshifts, supernovae, strong lensing and kilonovae. In order to reduce systematic uncertainties, we conclude that the current baseline observing strategy needs to be significantly modified to result in the best possible cosmological constraints. We provide some key recommendations: moving the WFD (Wide-Fast-Deep) footprint to avoid regions of high extinction, taking visit pairs in different filters, changing the 2x15s snaps to a single exposure to improve efficiency, focusing on strategies that reduce long gaps (>15 days) between observations, and prioritizing spatial uniformity at several intervals during the 10-year survey.
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| 3. |
- Alves, Catarina S., et al.
(författare)
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Considerations for Optimizing the Photometric Classification of Supernovae from the Rubin Observatory
- 2022
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Ingår i: Astrophysical Journal Supplement Series. - : American Astronomical Society. - 0067-0049 .- 1538-4365. ; 258:2
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Tidskriftsartikel (refereegranskat)abstract
- The Vera C. Rubin Observatory will increase the number of observed supernovae (SNe) by an order of magnitude; however, it is impossible to spectroscopically confirm the class for all SNe discovered. Thus, photometric classification is crucial, but its accuracy depends on the not-yet-finalized observing strategy of Rubin Observatory's Legacy Survey of Space and Time (LSST). We quantitatively analyze the impact of the LSST observing strategy on SNe classification using simulated multiband light curves from the Photometric LSST Astronomical Time-Series Classification Challenge (PLAsTiCC). First, we augment the simulated training set to be representative of the photometric redshift distribution per SNe class, the cadence of observations, and the flux uncertainty distribution of the test set. Then we build a classifier using the photometric transient classification library snmachine, based on wavelet features obtained from Gaussian process fits, yielding a similar performance to the winning PLAsTiCC entry. We study the classification performance for SNe with different properties within a single simulated observing strategy. We find that season length is important, with light curves of 150 days yielding the highest performance. Cadence also has an important impact on SNe classification; events with median inter-night gap <3.5 days yield higher classification performance. Interestingly, we find that large gaps (>10 days) in light-curve observations do not impact performance if sufficient observations are available on either side, due to the effectiveness of the Gaussian process interpolation. This analysis is the first exploration of the impact of observing strategy on photometric SN classification with LSST.
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| 4. |
- Alves, Catarina S., et al.
(författare)
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Impact of Rubin Observatory Cadence Choices on Supernovae Photometric Classification
- 2023
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Ingår i: Astrophysical Journal Supplement Series. - : American Astronomical Society. - 0067-0049 .- 1538-4365. ; 265:2
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Tidskriftsartikel (refereegranskat)abstract
- The Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) will discover an unprecedented number of supernovae (SNe), making spectroscopic classification for all the events infeasible. LSST will thus rely on photometric classification, whose accuracy depends on the not-yet-finalized LSST observing strategy. In this work, we analyze the impact of cadence choices on classification performance using simulated multiband light curves. First, we simulate SNe with an LSST baseline cadence, a nonrolling cadence, and a presto-color cadence, which observes each sky location three times per night instead of twice. Each simulated data set includes a spectroscopically confirmed training set, which we augment to be representative of the test set as part of the classification pipeline. Then we use the photometric transient classification library snmachine to build classifiers. We find that the active region of the rolling cadence used in the baseline observing strategy yields a 25% improvement in classification performance relative to the background region. This improvement in performance in the actively rolling region is also associated with an increase of up to a factor of 2.7 in the number of cosmologically useful Type Ia SNe relative to the background region. However, adding a third visit per night as implemented in presto-color degrades classification performance due to more irregularly sampled light curves. Overall, our results establish desiderata on the observing cadence related to classification of full SNe light curves, which in turn impacts photometric SNe cosmology with LSST.
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