Engineering topics

Kaviraj, S.

Publications and source records attributed to Kaviraj, S..

3 records

DOE OSTI2019

Galaxy morphological classification in deep-wide surveys via unsupervised machine learning

ABSTRACT Galaxy morphology is a fundamental quantity, which is essential not only for the full spectrum of galaxy-evolution studies, but also for a plethora of science in observational cosmology (e.g. as a prior for photometric-redshift measurements and as contextual data for transient light-curve classifications). While a rich literature exists on morphological-classification techniques, the unprecedented data volumes, coupled, in some cases, with the short cadences of forthcoming ‘Big-Data’ surveys (e.g. from the LSST), present novel challenges for this field. Large data volumes make such data sets intractable for visual inspection (even via massively distributed platforms like Galaxy Zoo), while short cadences make it difficult to employ techniques like supervised machine learning, since it may be impractical to repeatedly produce training sets on short time-scales. Unsupervised machine learning, which does not require training sets, is ideally suited to the morphological analysis of new and forthcoming surveys. Here, we employ an algorithm that performs clustering of graph representations, in order to group image patches with similar visual properties and objects constructed from those patches, like galaxies. We implement the algorithm on the Hyper-Suprime-Cam Subaru-Strategic-Program Ultra-Deep survey, to autonomously reduce the galaxy population to a small number (160) of ‘morphological clusters’, populated by galaxies with similar morphologies, which are then benchmarked using visual inspection. The morphological classifications (which we release publicly) exhibit a high level of purity, and reproduce known trends in key galaxy properties as a function of morphological type at z < 1 (e.g. stellar-mass functions, rest-frame colours, and the position of galaxies on the star-formation main sequence). Our study demonstrates the power of unsupervised machine learning in performing accurate morphological analysis, which will become indispensable in this new era of deep-wide surveys.

Martin, G.↗

NASA NTRS2012

The Insignificance of Major Mergers in Driving Star Formation at z approximately equal to 2

We study the significance of major mergers in driving star formation in the early Universe, by quantifying the contribution of this process to the total star formation budget in 80 massive (M(*) > 10(exp 10) Solar M) galaxies at z approx = 2. Employing visually-classified morphologies from rest-frame V-band HST imaging, we find that 55(exp +/-14)% of the star formation budget is hosted by non-interacting late-types, with 27(exp +/-18% in major mergers and 18(exp +/- 6)% in spheroids. Given that a system undergoing a major merger continues to experience star formation driven by other processes at this epoch (e.g. cold accretion, minor mergers), approx 27% is a likely upper limit for the major-merger contribution to star formation activity at this epoch. The ratio of the average specific star formation rate in major mergers to that in the non-interacting late-types is approx 2.2:1, suggesting that the typical enhancement of star formation due to major merging is modest and that just under half the star formation in systems experiencing major mergers is unrelated to the merger itself. Taking this into account, we estimate that the actual major-merger contribution to the star formation budget may be as low as approx 15%. While our study does not preclude a major-merger-dominated. era in the very early Universe, if the major-merger contribution to star formation does not evolve significantly into larger look-back times, then this process has a relatively insignificant role in driving stellar mass assembly over cosmic time.

Kaviraj, S.↗

NASA NTRS2012

The Size Evolution of Passive Galaxies: Observations From the Wide-Field Camera 3 Early Release Science Program

We present the size evolution of passively evolving galaxies at z approximately 2 identified in Wide-Field Camera 3 imaging from the Early Release Science program. Our sample was constructed using an analog to the passive BzK galaxy selection criterion, which isolates galaxies with little or no ongoing star formation at z greater than approximately 1.5. We identify 30 galaxies in approximately 40 arcmin(sup 2) to H less than 25 mag. By fitting the 10-band Hubble Space Telescope photometry from 0.22 micrometers less than approximately lambda (sub obs) 1.6 micrometers with stellar population synthesis models, we simultaneously determine photometric redshift, stellar mass, and a bevy of other population parameters. Based on the six galaxies with published spectroscopic redshifts, we estimate a typical redshift uncertainty of approximately 0.033(1+z).We determine effective radii from Sersic profile fits to the H-band image using an empirical point-spread function. By supplementing our data with published samples, we propose a mass-dependent size evolution model for passively evolving galaxies, where the most massive galaxies (M(sub ∗) approximately 10(sup 11) solar mass) undergo the strongest evolution from z approximately 2 to the present. Parameterizing the size evolution as (1 + z)(sup - alpha), we find a tentative scaling of alpha approximately equals (−0.6 plus or minus 0.7) + (0.9 plus or minus 0.4) log(M(sub ∗)/10(sup 9 solar mass), where the relatively large uncertainties reflect the poor sampling in stellar mass due to the low numbers of highredshift systems. We discuss the implications of this result for the redshift evolution of the M(sub ∗)-R(sub e) relation for red galaxies.

fundamental parameters↗