Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userDark Matter Science in the Era of LSST
86
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
Astrophysical observations currently provide the only robust, empirical measurements of dark matter. In the coming decade, astrophysical observations will guide other experimental efforts, while simultaneously probing unique regions of dark matter parameter space. This white paper summarizes astrophysical observations that can constrain the fundamental physics of dark matter in the era of LSST. We describe how astrophysical observations will inform our understanding of the fundamental properties of dark matter, such as particle mass, self-interaction strength, non-gravitational interactions with the Standard Model, and compact object abundances. Additionally, we highlight theoretical work and experimental/observational facilities that will complement LSST to strengthen our understanding of the fundamental characteristics of dark matter.
rate research
Read More
The Large Synoptic Survey Telescope (LSST) Dark Energy Science Collaboration (DESC) will use five cosmological probes: galaxy clusters, large scale structure, supernovae, strong lensing, and weak lensing. This Science Requirements Document (SRD) quantifies the expected dark energy constraining power of these probes individually and together, with conservative assumptions about analysis methodology and follow-up observational resources based on our current understanding and the expected evolution within the field in the coming years. We then define requirements on analysis pipelines that will enable us to achieve our goal of carrying out a dark energy analysis consistent with the Dark Energy Task Force definition of a Stage IV dark energy experiment. This is achieved through a forecasting process that incorporates the flowdown to detailed requirements on multiple sources of systematic uncertainty. Futur
This paper introduces cosmoDC2, a large synthetic galaxy catalog designed to support precision dark energy science with the Large Synoptic Survey Telescope (LSST). CosmoDC2 is the starting point for the second data challenge (DC2) carried out by the LSST Dark Energy Science Collaboration (LSST DESC). The catalog is based on a trillion-particle, 4.225 Gpc^3 box cosmological N-body simulation, the `Outer Rim run. It covers 440 deg^2 of sky area to a redshift of z=3 and is complete to a magnitude depth of 28 in the r-band. Each galaxy is characterized by a multitude of properties including stellar mass, morphology, spectral energy distributions, broadband filter magnitudes, host halo information and weak lensing shear. The size and complexity of cosmoDC2 requires an efficient catalog generation methodology; our approach is based on a new hybrid technique that combines data-driven empirical approaches with semi-analytic galaxy modeling. A wide range of observation-based validation tests has been implemented to ensure that cosmoDC2 enables the science goals of the planned LSST DESC DC2 analyses. This paper also represents the official release of the cosmoDC2 data set, including an efficient reader that facilitates interaction with the data.
The deluge of data from time-domain surveys is rendering traditional human-guided data collection and inference techniques impractical. We propose a novel approach for conducting data collection for science inference in the era of massive large-scale surveys that uses value-based metrics to autonomously strategize and co-ordinate follow-up in real-time. We demonstrate the underlying principles in the Recommender Engine For Intelligent Transient Tracking (REFITT) that ingests live alerts from surveys and value-added inputs from data brokers to predict the future behavior of transients and design optimal data augmentation strategies given a set of scientific objectives. The prototype presented in this paper is tested to work given simulated Rubin Observatory Legacy Survey of Space and Time (LSST) core-collapse supernova (CC SN) light-curves from the PLAsTiCC dataset. CC SNe were selected for the initial development phase as they are known to be difficult to classify, with the expectation that any learning techniques for them should be at least as effective for other transients. We demonstrate the behavior of REFITT on a random LSST night given ~32000 live CC SNe of interest. The system makes good predictions for the photometric behavior of the events and uses them to plan follow-up using a simple data-driven metric. We argue that machine-directed follow-up maximizes the scientific potential of surveys and follow-up resources by reducing downtime and bias in data collection.
We show that hidden hot dark matter, hidden-sector dark matter with interactions that decouple when it is relativistic, is a viable dark matter candidate provided it has never been in thermal equilibrium with the particles of the standard model. This hidden hot dark matter may reheat to a lower temperature and number density than the visible Universe and thus account, simply with its thermal abundance, for all the dark matter in the Universe while evading the typical constraints on hot dark matter arising from structure formation. We find masses ranging from ~3 keV to ~10 TeV. While never in equilibrium with the standard model, this class of models may have unique observational signatures in the matter power spectrum or via extra-weak interactions with standard model particles.
Among the most stringent constraints on the dark matter annihilation cross section are those derived from observations of dwarf galaxies by the Fermi Gamma-Ray Space Telescope. As current (e.g., Dark Energy Survey, DES) and future (Large Synoptic Survey Telescope, LSST) optical imaging surveys discover more of the Milky Ways ultra-faint satellite galaxies, they may increase Fermis sensitivity to dark matter annihilations. In this study, we use a semi-analytic model of the Milky Ways satellite population to predict the characteristics of the dwarfs likely to be discovered by DES and LSST, and project how these discoveries will impact Fermis sensitivity to dark matter. While we find that modest improvements are likely, the dwarf galaxies discovered by DES and LSST are unlikely to increase Fermis sensitivity by more than a factor of ~2-4. However, this outlook may be conservative, given that our model underpredicts the number of ultra-faint galaxies with large potential annihilation signals actually discovered in the Sloan Digital Sky Survey. Our simulation-based approach focusing on the Milky Way satellite population demographics complements existing empirically-based estimates.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
