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