Randall R. Rojas

Photometric Properties of Void Galaxies in the Sloan Digital Sky Survey

Using a nearest neighbor analysis, we construct a sample of void galaxies from the Sloan Digital Sky Survey (SDSS) and compare the photometric properties of these galaxies to the population of nonvoid (wall) galaxies. We trace the density field of galaxies using a volume-limited sample with zmax ¼ 0:089. Galaxies from the flux-limited SDSS with zzmax and fewer than three volume-limited neighbors within 7 h � 1 Mpc are classif ied as void gal- axies. This criterion implies a density contrast ��=� < � 0:6 around void galaxies. From 155,000 galaxies, we obtain a subsample of 13,742 galaxies with zzmax, from which we identify 1010 galaxies as void galaxies. To identify an additional 194 faint void galaxies from the SDSS in the nearby universe, r P 72 h � 1 Mpc, we employ volume-limited samples extracted from the Updated Zwicky Catalog and the Southern Sky Redshift Survey with zmax ¼ 0:025 to trace the galaxy distribution. Our void galaxies span a range of absolute magnitude from Mr ¼ � 13: 5t o� 22.5. Using SDSS photometry, we compare the colors, concentration indices, and Sersic indices of the void and wall samples. Void galaxies are significantly bluer than galaxies lying at higher density. The population of void galaxies with Mr P M � þ 1 and brighter is on average bluer and more concentrated (later type) than galaxies outside of voids. The latter behavior is only partly explained by the paucity of luminous red galaxies in voids. These results generally agree with the predictions of semianalytic models for galaxy formation in cold dark matter models, which indicate that void galaxies should be relatively bluer, more disklike, and have higher specific star formation rates. Subject heading gs: cosmology: observations — galaxies: photometry — galaxies: structure — large-scale structure of universe — methods: statistical

The luminosity function of void galaxies in the sloan digital sky survey

We measure the r-band luminosity function (LF) of a sample of 103 void galaxies over a large range of magnitude, -21.5 5 A], have brighter M but faint-end slopes similar to those of void galaxies. In contrast, the LFs of wall galaxies with red g - r color, elliptical-like profiles, or low star formation rates have significantly shallower faint-end slopes and brighter values of M than we find for void galaxies. We conclude that the void galaxy population is dominated by faint, late-type galaxies. The shift in M* between the void and wall galaxy LFs is consistent with the shift of the mass function in voids predicted by extended Press-Schechter theory.

SPECTROSCOPIC PROPERTIES OF VOID GALAXIES IN THE SLOAN DIGITAL SKY SURVEY

We study the spectroscopic properties of a sample of 103 void galaxies identified in the Sloan Digital Sky Survey (SDSS) and compare these with the properties of galaxies in higher density regions (wall galaxies). This sample of void galaxies covers the range of absolute magnitude from Mr = -13.5 to Mr = -22.5 in regions with density contrast δρ/ρ < -0.6. We compare the equivalent widths of Hα, [O II], [N II], Hβ, and [O III] of void and wall galaxies with similar luminosities. We find that void galaxies have larger emission line equivalent widths, indicating that they are forming stars at a higher rate. A comparison of the Balmer break, as measured by the parameter Dn(4000), reveals that void galaxies have younger stellar populations than wall galaxies. Using standard techniques, we estimate Hα and [O II] star formation rates (SFRs) of the void and wall galaxies. Combining these measurements with estimates of the stellar masses, we find specific star formation rates (SFR per unit stellar mass) for void galaxies that are generally higher than for wall galaxies, consistent with the results from the equivalent widths.

A Bayesian Theory of Sequential Causal Learning and Abstract Transfer.

Two key research issues in the field of causal learning are how people acquire causal knowledge when observing data that are presented sequentially, and the level of abstraction at which learning takes place. Does sequential causal learning solely involve the acquisition of specific cause-effect links, or do learners also acquire knowledge about abstract causal constraints? Recent empirical studies have revealed that experience with one set of causal cues can dramatically alter subsequent learning and performance with entirely different cues, suggesting that learning involves abstract transfer, and such transfer effects involve sequential presentation of distinct sets of causal cues. It has been demonstrated that pre-training (or even post-training) can modulate classic causal learning phenomena such as forward and backward blocking. To account for these effects, we propose a Bayesian theory of sequential causal learning. The theory assumes that humans are able to consider and use several alternative causal generative models, each instantiating a different causal integration rule. Model selection is used to decide which integration rule to use in a given learning environment in order to infer causal knowledge from sequential data. Detailed computer simulations demonstrate that humans rely on the abstract characteristics of outcome variables (e.g., binary vs. continuous) to select a causal integration rule, which in turn alters causal learning in a variety of blocking and overshadowing paradigms. When the nature of the outcome variable is ambiguous, humans select the model that yields the best fit with the recent environment, and then apply it to subsequent learning tasks. Based on sequential patterns of cue-outcome co-occurrence, the theory can account for a range of phenomena in sequential causal learning, including various blocking effects, primacy effects in some experimental conditions, and apparently abstract transfer of causal knowledge.