Analysis of a Custom Support Vector Machine for Photometric Redshift Estimation and the Inclusion of Galaxy Shape Information

Aims: We present a custom support vector machine classification package for photometric redshift estimation, including comparisons with other methods. We also explore the efficacy of including galaxy shape information in redshift estimation. Support vector machines, a type of machine learning, utilize optimization theory and supervised learning algorithms to construct predictive models based on the information content of data in a way that can treat different input features symmetrically. Methods: The custom support vector machine package we have developed is designated SPIDERz and made available to the community. As test data for evaluating performance and comparison with other methods, we apply SPIDERz to four distinct data sets: 1) the publicly available portion of the PHAT-1 catalog based on the GOODS-N field with spectroscopic redshifts in the range $z < 3.6$, 2) 14365 galaxies from the COSMOS bright survey with photometric band magnitudes, morphology, and spectroscopic redshifts inside $z < 1.4$, 3) 3048 galaxies from the overlap of COSMOS photometry and morphology with 3D-HST spectroscopy extending to $z < 3.9$, and 4) 2612 galaxies with five-band photometric magnitudes and morphology from the All-wavelength Extended Groth Strip International Survey and $z < 1.57$. Results: We find that SPIDER-z achieves results competitive with other empirical packages on the PHAT-1 data, and performs quite well in estimating redshifts with the COSMOS and AEGIS data, including in the cases of a large redshift range ($0 < z < 3.9$). We also determine from analyses with both the COSMOS and AEGIS data that the inclusion of morphological information does not have a statistically significant benefit for photometric redshift estimation with the techniques employed here.Comment: Submitted to A&A, 11 pages, 10 figures, 1 table, updated to version in revisio

What Can the Distribution of Intergalactic Metals Tell us About the History of Cosmological Enrichment?

I study the relationship between the spatial distribution of intergalactic metals and the masses and ejection energies of the sources that produced them. Over a wide range of models, metal enrichment is dominated by the smallest efficient sources, as the enriched volume scales roughly as E^{3/5} ~ M^{3/5} while the number density of sources goes as 1/M. In all cases, the earliest sources have the biggest impact, because fixed comoving distances correspond to smaller physical distances at higher redshifts. This means that most of the enriched volume is found around rare peaks, and intergalactic metals are naturally highly clustered. Furthermore, this clustering is so strong as to lead to a large overlap between individual bubbles. Thus the typical radius of enriched z ~ 3 regions should be interpreted as a constraint on groupings of sources rather than the ejection radius of a typical source. Similarly, the clustering of enriched regions should be taken as a measurement of source bias rather than mass.Comment: 10 pages, 2 figures, ApJL in pres

Nanometer patterning of water by tetraanionic ferrocyanide stabilized in aqueous nanodrops.

Formation of the small, highly charged tetraanion ferrocyanide, Fe(CN)64-, stabilized in aqueous nanodrops is reported. Ion-water interactions inside these nanodrops are probed using blackbody infrared radiative dissociation, infrared photodissociation (IRPD) spectroscopy, and molecular modeling in order to determine how water molecules stabilize this highly charged anion and the extent to which the tetraanion patterns the hydrogen-bonding network of water at long distance. Fe(CN)64-(H2O)38 is the smallest cluster formed directly by nanoelectrospray ionization. Ejection of an electron from this ion to form Fe(CN)63-(H2O)38 occurs with low-energy activation, but loss of a water molecule is favored at higher energy indicating that water molecule loss is entropically favored over loss of an electron. The second solvation shell is almost complete at this cluster size indicating that nearly two solvent shells are required to stabilize this highly charged anion. The extent of solvation necessary to stabilize these clusters with respect to electron loss is substantially lower through ion pairing with either H+ or K+ (n = 17 and 18, respectively). IRPD spectra of Fe(CN)64-(H2O) n show the emergence of a free O-H water molecule stretch between n = 142 and 162 indicating that this ion patterns the structure of water molecules within these nanodrops to a distance of at least ∼1.05 nm from the ion. These results provide new insights into how water stabilizes highly charged ions and demonstrate that highly charged anions can have a significant effect on the hydrogen-bonding network of water molecules well beyond the second and even third solvation shells