Gravity Spy: Integrating Advanced LIGO Detector Characterization, Machine Learning, and Citizen Science

(abridged for arXiv) 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 alternate 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. 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 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.Comment: 27 pages, 8 figures, 1 tabl

ReelFramer: Co-creating News Reels on Social Media with Generative AI

Short videos on social media are a prime way many young people find and consume content. News outlets would like to reach audiences through news reels, but currently struggle to translate traditional journalistic formats into the short, entertaining videos that match the style of the platform. There are many ways to frame a reel-style narrative around a news story, and selecting one is a challenge. Different news stories call for different framings, and require a different trade-off between entertainment and information. We present a system called ReelFramer that uses text and image generation to help journalists explore multiple narrative framings for a story, then generate scripts, character boards and storyboards they can edit and iterate on. A user study of five graduate students in journalism-related fields found the system greatly eased the burden of transforming a written story into a reel, and that exploring framings to find the right one was a rewarding process

Classifying the unknown: discovering novel gravitational-wave detector glitches using similarity learning

The observation of gravitational waves from compact binary coalescences by LIGO and Virgo has begun a new era in astronomy. A critical challenge in making detections is determining whether loud transient features in the data are caused by gravitational waves or by instrumental or environmental sources. The citizen-science project \emph{Gravity Spy} has been demonstrated as an efficient infrastructure for classifying known types of noise transients (glitches) through a combination of data analysis performed by both citizen volunteers and machine learning. We present the next iteration of this project, using similarity indices to empower citizen scientists to create large data sets of unknown transients, which can then be used to facilitate supervised machine-learning characterization. This new evolution aims to alleviate a persistent challenge that plagues both citizen-science and instrumental detector work: the ability to build large samples of relatively rare events. Using two families of transient noise that appeared unexpectedly during LIGO's second observing run (O2), we demonstrate the impact that the similarity indices could have had on finding these new glitch types in the Gravity Spy program

Heteronuclear d-d and d-f Ru(II)/M complexes [M = Gd(III), Yb(III), Nd(III), Zn(II) or Mn(II)] of ligands combining phenanthroline and aminocarboxylate binding sites: combined relaxivity, cell imaging and photophysical studies

A ligand skeleton combining a 1,10-phenanthroline (phen) binding site and one or two heptadentate N3O4 aminocarboxylate binding sites, connected via alkyne spacers to the phen C3 or C3/C8 positions, has been used to prepare a range of heteronuclear Ru·M and Ru·M2 complexes which have been evaluated for their cell imaging, relaxivity, and photophysical properties. In all cases the phen unit is bound to a {Ru(bipy)2}2+ unit to give a phosphorescent {Ru(bipy)2(phen)}2+ luminophore, and the pendant aminocarboxylate sites are occupied by a secondary metal ion M which is either a lanthanide [Gd(III), Nd(III), Yb(III)] or another d-block ion [Zn(II), Mn(II)]. When M = Gd(III) or Mn(II) these ions provide the complexes with a high relaxivity for water; in the case of Ru·Gd and Ru·Gd2 the combination of high water relaxivity and 3MLCT phosphorescence from the Ru(II) unit provides the possibility of two different types of imaging modality in a single molecular probe. In the case of Ru·Mn and Ru·Mn2 the Ru(II)-based phosphorescence is substantially reduced compared to the control complexes Ru·Zn and Ru·Zn2 due to the quenching effect of the Mn(II) centres. Ultrafast transient absorption spectroscopy studies on Ru·Mn (and Ru·Zn as a non-quenched control) reveal the occurrence of fast (<1 ns) PET in Ru·Mn, from the Mn(II) ion to the Ru(II)-based 3MLCT state, i.e. MnII–(phen˙−)–RuIII → MnIII–(phen˙−)–RuII; the resulting MnIII–(phen˙−) state decays with τ ≈ 5 ns and is non-luminescent. This occurs in conformers when an ET pathway is facilitated by a planar, conjugated bridging ligand conformation connecting the two units across the alkyne bridge but does not occur in conformers where the two units are electronically decoupled by a twisted conformation of the bridging ligand. Computational studies (DFT) on Ru·Mn confirmed both the occurrence of the PET quenching pathway and its dependence on molecular conformation. In the complexes Ru·Ln and Ru·Ln2 (Ln = Nd, Yb), sensitised near-infrared luminescence from Nd(III) or Yb(III) is observed following photoinduced energy-transfer from the Ru(II) core, with Ru → Nd energy-transfer being faster than Ru → Yb energy-transfer due to the higher density of energy-accepting states on Nd(III)

The dimensions of software engineering success

Software engineering research and practice are hampered by the lack of a well-understood, top-level dependent variable. Recent initiatives on General Theory of Software Engineering suggest a multifaceted variable – Software Engineering Success. However, its exact dimensions are unknown. This paper investigates the dimensions (not causes) of software engineering success. An interdisciplinary sample of 191 design professionals (68 in the software industry) were interviewed concerning their perceptions of success. Non-software designers (e.g. architects) were included to increase the breadth of ideas and facilitate comparative analysis. Transcripts were subjected to supervised, semi-automated semantic content analysis, including a software developer vs. other professionals comparison. Findings suggest that participants view their work as time-constrained projects with explicit clients and other stakeholders. Success depends on stakeholder impacts – financial, social, physical and emotional – and is understood through feedback. Concern with meeting explicit requirements is peculiar to software engineering and design is not equated with aesthetics in many other fields. Software engineering success is a complex multifaceted variable, which cannot sufficiently be explained by traditional dimensions including user satisfaction, profitability or meeting requirements, budgets and schedules. A proto-theory of success is proposed, which models success as the net impact on a particular stakeholder at a particular time. Stakeholder impacts are driven by project efficiency, artifact quality and market performance. Success is not additive, e.g., ‘low’ success for clients does not average with ‘high’ success for developers to make ‘moderate’ success overall; rather, a project may be simultaneously successful and unsuccessful from different perspectives

Time evolution and asymmetry of a laser produced blast wave

Studies of a blast wave produced from carbon rods and plastic spheres in an argon background gas have been conducted using the Vulcan laser at the Rutherford Appleton Laboratory. A laser of 1500 J was focused onto these targets, and rear-side observations of an emission front were recorded using a fast-framing camera. The emission front is asymmetrical in shape and tends to a more symmetrical shape as it progresses due to the production of a second shock wave later in time, which pushes out the front of the blast wave. Plastic spheres produce faster blast waves, and the breakthrough of the second shock is visible before the shock stalls. The results are presented to demonstrate this trend, and similar evolution dynamics of experimental and simulation data from the FLASH radiation-hydrodynamics code are observed