From Data to Software to Science with the Rubin Observatory LSST

Breivik, Katelyn; Connolly, Andrew J.; Ford, K. E. Saavik; Jurić, Mario; Mandelbaum, Rachel; Miller, A. A.; Norman, Dara; Olsen, Knut; O’Mullane, W.; Price-Whelan, Adrian M.; Sacco, Timothy; Sokoloski, J. L.; Villar, A.; Acquaviva, Viviana; Ahumada, Tomás; AlSayyad, Yusra; Alves, Claudia; Andreoni, Igor; Anguita, T.; Best, Henry J.; Bianco, Federica; Bonito, R.; Bradshaw, Andrew; Burke, Colin J.; Campos, Andresa Rodrigues de; Cantiello, Matteo; Čaplar, Neven; Chandler, Colin Orion; Chan, James C.M.; Costa, L. N. da; Danieli, Shany; Davenport, James R. A.; Fabbian, Giulio; Fagin, Joshua; Gagliano, Alexander; Gall, C.; Camargo, Nicolás Garavito; Gawiser, Eric; Gezari, Suvi; Gomboc, A.; González‐Morales, Alma X.; Graham, Matthew J.; Gschwend, J.; Guy, L. P.; Holman, Matthew J.; Hsieh, Henry H.; Hundertmark, M.; Ilić, D.; Ishida, Emille E. O.; Jurkić, Tomislav; Kannawadi, Arun; Kosakowski, Alekzander; Kovačević, Andjelka B.; Kubica, Jeremy; Lanusse, François; Lazar, Ilin; Levine, W. Garrett; Li, Xiaolong; Lu, Jiwen; Luna, G. J. M.; Mahabal, A.; Malz, Alex I.; Mao, Yao-Yuan; Medan, Ilija; Moeyens, Joachim; Nikolić, Mladen; Nikutta, Robert; O’Dowd, Matt; Olsen, Charlotte; Pearson, Sarah; Pedraza, Ilhuiyolitzin Villicana; Popinchalk, Mark; Quint, Bruno; Radović, Viktor; Ragosta, Fabio; Riccio, Gabriele; Riley, A. H.; Rożek, A.; Sánchez-Sáez, P.; Sarro, L. M.; Saunders, Clare; Savić, Đorđe; Schmidt, Samuel; Scott, Adam; Shirley, R.; Smotherman, Hayden R.; Stetzler, Steven; Storey-Fisher, Kate; Street, R. A.; Trilling, David E.; Tsapras, Y.; Ustamujic, S.; Velzen, S. van; Vázquez-Mata, J. A.; Venuti, L.; Wyatt, S.; Yu, Weixiang; Zabludoff, Ann I.

doi:10.48550/arxiv.2208.02781

Cited by 6 publications

(6 citation statements)

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“…The major obstacles are the small sample size and the lack of wavelength coverage in domains outside the optical and NIR ones. However, both issues will be soon effectively tackled: with the inauguration of the Vera C. Rubin Observatory (e.g., [167][168][169]), the sample size problem will be immediately solved, thanks to the large amount of data that this survey will provide. On the other hand, with the launch of the James Webb Space Telescope [170], it will be possible to investigate in the infrared domain in an unprecedented fashion, unveiling the secrets of the dust that is such a key component for many of these gap transients.…”

Section: Discussionmentioning

confidence: 99%

Gap Transients Interacting with Circumstellar Medium

2022

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In the last 20 years, modern wide-field surveys discovered a new class of peculiar transients, which lie in the luminosity gap between standard supernovae and classical novae. These transients are often called “intermediate luminosity optical transients” or “gap transients”. They are usually distinguished in subgroups based on their phenomenology, such as supernova impostors, intermediate luminosity red transients, and luminous red novae. In this review, we present a brief overview of their observational features and possible physical scenarios to date, in the attempt to understand their nature.

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Section: Discussionmentioning

confidence: 99%

Gap Transients Interacting with Circumstellar Medium

2022

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“…This is driven by the gap between flux sensitivity and angular resolution, which becomes important as one pushes to fainter depths. For current and near-future surveys, an increasingly large fraction of sources that would be reliably measured in isolation will be observed as partial or full blends with adjacent sources, complicating both the identification and measurement of bright and faint objects (Breivik et al 2022). For some data sets, a fast mapping rate is prioritized over angular resolution, and these surveys in particular will approach sensitivities where source blending is relevant, both in the spatial and spectral domains.…”

Section: Pcatmentioning

confidence: 99%

PCAT-DE: Reconstructing Pointlike and Diffuse Signals in Astronomical Images Using Spatial and Spectral Information

Feder

Butler

Daylan

et al. 2023

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Observational data from astronomical imaging surveys contain information about a variety of source populations and environments, and their complexity will increase substantially as telescopes become more sensitive. Even for existing observations, measuring the correlations between pointlike and diffuse emission can be crucial to correctly inferring the properties of any individual component. For this task, information is typically lost, because of conservative data cuts, aggressive filtering, or incomplete treatment of contaminated data. We present the code PCAT-DE, an extension of probabilistic cataloging, designed to simultaneously model pointlike and diffuse signals. This work incorporates both explicit spatial templates and a set of nonparametric Fourier component templates into a forward model of astronomical images, reducing the number of processing steps applied to the observed data. Using synthetic Herschel-SPIRE multiband observations, we demonstrate that point-source and diffuse emission can be reliably separated and measured. We present two applications of this model. For the first, we perform point-source detection/photometry in the presence of galactic cirrus and demonstrate that cosmic infrared background galaxy counts can be recovered in cases of significant contamination. In the second, we show that the spatially extended thermal Sunyaev–Zel’dovich effect signal can be reliably measured even when it is subdominant to the pointlike emission from individual galaxies.

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“…For large-scale sky surveys aiming at understanding the constituents of the Universe, having realistic mock galaxy catalogs is essential, as they are required for estimating covariance matrices for constraining cosmological parameters, performing end-to-end validation of the analysis pipeline, as well as estimating the accuracy of photometric redshifts and masses of galaxies, just to name a few (e.g., Breivik et al 2022). Cosmological surveys are particularly demanding, as tens of thousands of mocks over significant volumes are needed to reduce systematic uncertainties (e.g., Villaescusa-Navarro et al 2020;Rossi et al 2021).…”

Section: Introductionmentioning

confidence: 99%

Leaving No Branches Behind: Predicting Baryonic Properties of Galaxies from Merger Trees

Chuang,

Jespersen,

Lin

et al. 2024

ApJ

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Galaxies play a key role in our endeavor to understand how structure formation proceeds in the Universe. For any precision study of cosmology or galaxy formation, there is a strong demand for huge sets of realistic mock galaxy catalogs, spanning cosmologically significant volumes. For such a daunting task, methods that can produce a direct mapping between dark matter halos from dark matter-only simulations and galaxies are strongly preferred, as producing mocks from full-fledged hydrodynamical simulations or semi-analytical models is too expensive. Here, we present a graph-neural-network-based model that is able to accurately predict key properties of galaxies such as stellar mass, g − r color, star formation rate, gas mass, stellar metallicity, and gas metallicity, purely from dark matter properties extracted from halos along the full assembly history of the galaxies. Tests based on the TNG300 simulation of the IllustrisTNG project show that our model can recover the baryonic properties of galaxies to high accuracy, over a wide redshift range (z = 0–5), for all galaxies with stellar masses more massive than 109 M ⊙ and their progenitors, with strong improvements over the state-of-the-art methods. We further show that our method makes substantial strides toward providing an understanding of the implications of the IllustrisTNG galaxy formation model.

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From Data to Software to Science with the Rubin Observatory LSST

Cited by 6 publications

References 0 publications

Gap Transients Interacting with Circumstellar Medium

Gap Transients Interacting with Circumstellar Medium

PCAT-DE: Reconstructing Pointlike and Diffuse Signals in Astronomical Images Using Spatial and Spectral Information

Leaving No Branches Behind: Predicting Baryonic Properties of Galaxies from Merger Trees

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