Zooniverse

Abstract:

Spiral arms are common features in low-redshift disc galaxies, and are prominent sites of star-formation and dust obscuration. However, spiral structure can take many forms: from galaxies displaying two strong `grand design' arms, to those with many `flocculent' arms. We investigate how these different arm types are related to a galaxy's star-formation and gas properties by making use of visual spiral arm number measurements from Galaxy Zoo 2. We combine UV and mid-IR photometry from GALEX and WISE to measure the rates and relative fractions of obscured and unobscured star formation in a sample of low-redshift SDSS spirals. Total star formation rate has little dependence on spiral arm multiplicity, but two-armed spirals convert their gas to stars more efficiently. We find significant differences in the fraction of obscured star-formation: an additional $\sim 10$ per cent of star-formation in two-armed galaxies is identified via mid-IR dust emission, compared to that in many-armed galaxies. The latter are also significantly offset below the IRX-$\beta$ relation for low-redshift star-forming galaxies. We present several explanations for these differences versus arm number: variations in the spatial distribution, sizes or clearing timescales of star-forming regions (i.e., molecular clouds), or contrasting recent star-formation histories.

Abstract:

With the first direct detection of gravitational waves, the advanced laser interferometer gravitational-wave observatory (LIGO) has initiated a new field of astronomy by providing an alternative means of sensing the universe. The extreme sensitivity required to make such detections is achieved through exquisite isolation of all sensitive components of LIGO from non-gravitational-wave disturbances. Nonetheless, LIGO is still susceptible to a variety of instrumental and environmental sources of noise that contaminate the data. Of particular concern are noise features known as glitches, which are transient and non-Gaussian in their nature, and occur at a high enough rate so that accidental coincidence between the two LIGO detectors is non-negligible. Glitches come in a wide range of time-frequency-amplitude morphologies, with new morphologies appearing as the detector evolves. Since they can obscure or mimic true gravitational-wave signals, a robust characterization of glitches is paramount in the effort to achieve the gravitational-wave detection rates that are predicted by the design sensitivity of LIGO. This proves a daunting task for members of the LIGO Scientific Collaboration alone due to the sheer amount of data. In this paper we describe an innovative project that combines crowdsourcing with machine learning to aid in the challenging task of categorizing all of the glitches recorded by the LIGO detectors. Through the Zooniverse platform, we engage and recruit volunteers from the public to categorize images of time-frequency representations of glitches into pre-identified morphological classes and to discover new classes that appear as the detectors evolve. In addition, machine learning algorithms are used to categorize images after being trained on human-classified examples of the morphological classes. Leveraging the strengths of both classification methods, we create a combined method with the aim of improving the efficiency and accuracy of each individual classifier. The resulting classification and characterization should help LIGO scientists to identify causes of glitches and subsequently eliminate them from the data or the detector entirely, thereby improving the rate and accuracy of gravitational-wave observations. We demonstrate these methods using a small subset of data from LIGO's first observing run.

Abstract:

© 2017. The American Astronomical Society. All rights reserved.Submillimeter galaxies (SMGs) at z ≳ 1 are luminous in the far-infrared, and have star formation rates, SFR, of hundreds to thousands of solar masses per year. However, it is unclear whether they are true analogs of local ULIRGs or whether the mode of their star formation is more similar to that in local disk galaxies.We target these questions by using Herschel-PACS to examine the conditions in the interstellar medium (ISM) in far-infrared luminous SMGs at z ∼ 1-4. We present 70-160 μm photometry and spectroscopy of the [O IV]26 μm, [Fe II]26 μm, [S III]33 μm, [Si II]34 μm, [O III]52 μm, [N III]57 μm, and [O I]63 μm fine-structure lines and the S(0) and S(1) hydrogen rotational lines in 13 lensed SMGs identified by their brightness in early Herschel data. Most of the 13 targets are not individually spectroscopically detected; we instead focus on stacking these spectra with observations of an additional 32 SMGs from the Herschel archive-representing a complete compilation of PACS spectroscopy of SMGs. We detect [O I]63 μm, [Si II]34 μm, and [N III]57 μm at ≥3σ in the stacked spectra, determining that the average strengths of these lines relative to the far-IR continuum are (0.36 ± 0.12) × 10-3, (0.84 ± 0.17) × 10-3, and (0.27 ± 0.10) × 10-3, respectively. Using the [O III]52 μm/[N III]57 μm emission line ratio, we show that SMGs have average gas-phase metallicities ≳Zo. By using PDR modeling and combining the new spectral measurements with integrated far-infrared fluxes and existing [C II]158 μm data, we show that SMGs have average gas densities, n, of ∼101-3 cm-3 and FUV field strengths, G0 ∼ 102.2-4.5 (in Habing units: 1.6 × 10-3 erg cm-2 s-1), consistent with both local ULIRGs and lower luminosity star-forming galaxies.