2,824 research outputs found
How to measure metallicity from five-band photometry with supervised machine learning algorithms
We demonstrate that it is possible to measure metallicity from the SDSS
five-band photometry to better than 0.1 dex using supervised machine learning
algorithms. Using spectroscopic estimates of metallicity as ground truth, we
build, optimize and train several estimators to predict metallicity. We use the
observed photometry, as well as derived quantities such as stellar mass and
photometric redshift, as features, and we build two sample data sets at median
redshifts of 0.103 and 0.218 and median r-band magnitude of 17.5 and 18.3
respectively. We find that ensemble methods, such as Random Forests of Trees
and Extremely Randomized Trees, and Support Vector Machines all perform
comparably well and can measure metallicity with a Root Mean Square Error
(RMSE) of 0.081 and 0.090 for the two data sets when all objects are included.
The fraction of outliers (objects for which |Z_true - Z_pred| > 0.2 dex) is 2.2
and 3.9%, respectively and the RMSE decreases to 0.068 and 0.069 if those
objects are excluded. Because of the ability of these algorithms to capture
complex relationships between data and target, our technique performs better
than previously proposed methods that sought to fit metallicity using an
analytic fitting formula, and has 3x more constraining power than SED
fitting-based methods. Additionally, this method is extremely forgiving of
contamination in the training set, and can be used with very satisfactory
results for training sample sizes of just a few hundred objects. We distribute
all the routines to reproduce our results and apply them to other data sets.Comment: Minor revisions, matching version published in MNRA
The CMB as a dark energy probe
We give a brief review of the known effects of a dynamical vacuum
cosmological component, the dark energy, on the anisotropies of the cosmic
microwave background (CMB). We distinguish between a "classic" class of
observables, used so far to constrain the average of the dark energy abundance
in the redshift interval in which it is relevant for acceleration, and a
"modern" class, aiming at the measurement of its differential redshift
behavior. We show that the gravitationally lensed CMB belongs to the second
class, as it can give a measure of the dark energy abundance at the time of
equality with matter, occurring at about redshift 0.5. Indeed, the dark energy
abundance at that epoch influences directly the lensing strength, which is
injected at about the same time, if the source is the CMB. We illustrate this
effect focusing on the curl (BB) component of CMB polarization, which is
dominated by lensing on arcminute angular scales. An increasing dark energy
abundance at the time of equality with matter, parameterized by a rising first
order redshift derivative of its equation of state today, makes the BB power
dropping with respect to a pure LambdaCDM cosmology, keeping the other
cosmological parameters and primordial amplitude fixed. We briefly comment on
the forthcoming probes which might measure the lensing power on CMB.Comment: 12 pages, 9 figures, proceedings of the invited talk at the CMB and
Physics of the Early Universe Conference, Ischia, Italy, April 20-22, 200
SED fitting with MCMC: methodology and application to large galaxy surveys
We present GalMC (Acquaviva et al 2011), our publicly available Markov Chain
Monte Carlo algorithm for SED fitting, show the results obtained for a stacked
sample of Lyman Alpha Emitting galaxies at z ~ 3, and discuss the dependence of
the inferred SED parameters on the assumptions made in modeling the stellar
populations. We also introduce SpeedyMC, a version of GalMC based on
interpolation of pre-computed template libraries. While the flexibility and
number of SED fitting parameters is reduced with respect to GalMC, the average
running time decreases by a factor of 20,000, enabling SED fitting of each
galaxy in about one second on a 2.2GHz MacBook Pro laptop, and making SpeedyMC
the ideal instrument to analyze data from large photometric galaxy surveys.Comment: Proceedings of the IAU Symposium 284, "The Spectral Energy
Distribution of galaxies"; typos fixed; refs adde
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