865 research outputs found
Impact of Weak Lensing Mass Calibration on eROSITA Galaxy Cluster Cosmological Studies -- a Forecast
We forecast the impact of weak lensing (WL) cluster mass calibration on the
cosmological constraints from the X-ray selected galaxy cluster counts in the
upcoming eROSITA survey. We employ a prototype cosmology pipeline to analyze
mock cluster catalogs. Each cluster is sampled from the mass function in a
fiducial cosmology and given an eROSITA count rate and redshift, where count
rates are modeled using the eROSITA effective area, a typical exposure time,
Poisson noise and the scatter and form of the observed X-ray luminosity-- and
temperature--mass--redshift relations. A subset of clusters have mock shear
profiles to mimic either those from DES and HSC or from the future Euclid and
LSST surveys. Using a count rate selection, we generate a baseline cluster
cosmology catalog that contains 13k clusters over 14,892~deg of
extragalactic sky. Low mass groups are excluded using raised count rate
thresholds at low redshift. Forecast parameter uncertainties for
, and are 0.023 (0.016; 0.014), 0.017 (0.012;
0.010), and 0.085 (0.074; 0.071), respectively, when adopting DES+HSC WL
(Euclid; LSST), while marginalizing over the sum of the neutrino masses. A
degeneracy between the distance--redshift relation and the parameters of the
observable--mass scaling relation limits the impact of the WL calibration on
the constraints, but with BAO measurements from DESI an improved
determination of to 0.043 becomes possible. With Planck CMB priors,
() can be determined to (), and the
summed neutrino mass limited to eV (at 95\%). If
systematics on the group mass scale can be controlled, the eROSITA group and
cluster sample with 43k objects and LSST WL could constrain
and to 0.007 and to 0.050.Comment: 28 pages, 13 figur
A Regression Model for Plasma Reaction Kinetics
Machine learning (ML) is used to provide reactions rates appropriate for models of low temperature plasmas with a focus on A + B C + D binary chemical reactions. The regression model is trained on data extracted from the QBD, KIDA, NFRI and UfDA databases. The regression model used a variety of data on the reactant and product species, some of which also had to be estimated using ML. The final model is a voting regressor comprising three distinct optimized regression models: a support vector regressor, random forest regressor and a gradient-boosted trees regressor model; this model is made freely available via a GitHub repository. As a sample use case, the ML results are used to augment the chemistry of a BCl3/H2 gas mixture
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