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Marine Kuna

Publications and source records attributed to Marine Kuna.

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Impact of LSST systematics on stellar-stream density fluctuations for dark matter

The Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) is expected to significantly advance the study of Milky Way stellar streams. In particular, the deep, precise photometry from LSST should greatly increase the statistical sensitivity to density fluctuations in stellar streams, which can be used to probe the small-scale distribution of dark matter. However, current forecasts generally neglect the impact of observational systematics that will be imprinted on stream density measurements. In this study, we develop a realistic forward-modeling framework to inject stellar streams into LSST-like observations including photometric uncertainties, survey depth variations, background contamination, and imperfect star-galaxy classification. We develop a likelihood-ratio analysis to assess the detectability of gaps in stellar streams in the presence of these observational systematics. In the presence of realistic survey systematics, we find that after four years of operations, LSST will be sensitive to density reductions of $\sim50\%$ for gaps with widths of $5$ deg in streams with surface brightness of $\sim33$ mag arcsec$^{-2}$. Relative to the ideal case, this corresponds to a degradation in gap depth sensitivity by a factor of $\sim5$ due to the combined impact of background contamination and observational systematics. Assuming a simplified analytical mapping between gap depth and dark matter subhalo properties, these estimates correspond to a minimum detectable subhalo mass of $\sim1\times10^7$ M$_\odot$. Observational effects shift this accessible mass scale upward by a factor of $\sim16$, with background contamination contributing a factor of $\sim5$ and survey systematics a further factor of $\sim3$, dominated by star-galaxy classification.

astro-ph.GA

Impact of blending on weak lensing measurements with the Legacy Survey of Space and Time

Upcoming deep optical surveys, such as the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST), will scan the sky to unprecedented depths, detecting billions of galaxies. However, this amount of detections will lead to the apparent superposition of galaxies in the images, a phenomenon known as blending, that can affect the accurate measurement of individual galaxy properties. In particular, galaxy shapes play a crucial role in estimating the masses of large-scale structures, such as galaxy clusters, through weak gravitational lensing. This proceeding introduces a new catalog matching algorithm, friendly, designed for detecting and characterizing blends in simulated LSST data for the Dark Energy Science Collaboration (DESC) Data Challenge 2. The aim of this algorithm is to combine several matching procedures, as well as a probabilistic method to quantify blended systems. By removing the resulted 27% of galaxies affected by blending from the dataset, we demonstrate that the amplitude of the excess surface mass density weak lensing profile - potentially biased low due to blending - may be partially corrected.

astro-ph.CO

Impact of blending on weak lensing measurements with the Vera C. Rubin Observatory

Upcoming deep optical surveys such as the Vera C. Rubin Observatory Legacy Survey of Space and Time will scan the sky to unprecedented depths and detect billions of galaxies. This amount of detections will however cause the apparent superposition of galaxies on the images, called blending, and generate a new systematic error due to the confusion of sources. As consequences, the measurements of individual galaxies properties such as their redshifts or shapes will be impacted, and some galaxies will not be detected. However, galaxy shapes are key quantities, used to estimate masses of large scale structures, such as galaxy clusters, through weak gravitational lensing. This work presents a new catalog matching algorithm, called friendly, for the detection and characterization of blends in simulated LSST data for the DESC Data Challenge 2. By identifying a specific type of blends, we show that removing them from the data may partially correct the amplitude of the $ΔΣ$ weak lensing profile that could be biased low by around 20% due to blending. This would result in impacting clusters weak lensing mass estimate and cosmology.

astro-ph.CO