Anti-Trust and Economic Theory: Some Observations from the US Experience

Recent developments in US anti-trust can be characterised as reflecting the uneasy interaction of two quite separate phenomena: first, the increased emphasis on economic analysis as the overriding organising principle of anti-trust policy and on economic efficiency as the primary (perhaps only) relevant goal for anti-trust; second, the long-standing reluctance of the federal judiciary to involve itself in any substantive economic analysis, and the preference, instead, for simple rules of thumb or ‘pigeon holes’ to sort out lawful from unlawful conduct. The result has been that while economics has played a major role, it has not influenced American anti-trust as thoroughly or as uniformly as might have been imagined; rather the extent and the nature of its influence have depended on the degree to which the relevant economics could be reduced to the kind of simple rules or pigeon holes that the judiciary favours. The present paper will illustrate that theme, first by reporting on the two developments separately and then by illustrating their joint influence with reference to two important areas of American anti-trust: predatory conduct and so-called vertical restraints. Finally, a contrast will be made between judicial development in those two areas and recent American merger policy which, it is argued, is carried out largely independently of the judiciary, and hence the opportunities for economics to influence the process are less inhibited by the judicial reluctance to undertake extensive economic analysis

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

LIGO detector characterization in the second and third observing runs

The characterization of the Advanced LIGO detectors in the second and third observing runs has increased the sensitivity of the instruments, allowing for a higher number of detectable gravitational-wave signals, and provided confirmation of all observed gravitational-wave events. In this work, we present the methods used to characterize the LIGO detectors and curate the publicly available datasets, including the LIGO strain data and data quality products. We describe the essential role of these datasets in LIGO–Virgo Collaboration analyses of gravitational-waves from both transient and persistent sources and include details on the provenance of these datasets in order to support analyses of LIGO data by the broader community. Finally, we explain anticipated changes in the role of detector characterization and current efforts to prepare for the high rate of gravitational-wave alerts and events in future observing runs

GWpy: A Python package for gravitational-wave astrophysics

GWpy is a Python software package that provides an intuitive, object-oriented interface through which to access, process, and visualise data from gravitational-wave detectors. GWpy provides a number of new utilities for studying data, as well as an improved user interface for a number of existing tools. The ease-of-use, along with extensive online documentation and examples, has resulted in widespread adoption of GWpy as a basis for Python software development in the international gravitational-wave community

All-sky search for short gravitational-wave bursts in the second Advanced LIGO and Advanced Virgo run

We present the results of a search for short-duration gravitational-wave transients in the data from the second observing run of Advanced LIGO and Advanced Virgo. We search for gravitational-wave transients with a duration of milliseconds to approximately one second in the 32-4096 Hz frequency band with minimal assumptions about the signal properties, thus targeting a wide variety of sources. We also perform a matched-filter search for gravitational-wave transients from cosmic string cusps for which the waveform is well modeled. The unmodeled search detected gravitational waves from several binary black hole mergers which have been identified by previous analyses. No other significant events have been found by either the unmodeled search or the cosmic string search. We thus present the search sensitivities for a variety of signal waveforms and report upper limits on the source rate density as a function of the characteristic frequency of the signal. These upper limits are a factor of 3 lower than the first observing run, with a 50% detection probability for gravitational-wave emissions with energies of ∼10-9 Mc2 at 153 Hz. For the search dedicated to cosmic string cusps we consider several loop distribution models, and present updated constraints from the same search done in the first observing run

Search for Eccentric Binary Black Hole Mergers with Advanced LIGO and Advanced Virgo during Their First and Second Observing Runs

When formed through dynamical interactions, stellar-mass binary black holes (BBHs) may retain eccentric orbits (e > 0.1 at 10 Hz) detectable by ground-based gravitational-wave detectors. Eccentricity can therefore be used to differentiate dynamically formed binaries from isolated BBH mergers. Current template-based gravitational-wave searches do not use waveform models associated with eccentric orbits, rendering the search less efficient for eccentric binary systems. Here we present the results of a search for BBH mergers that inspiral in eccentric orbits using data from the first and second observing runs (O1 and O2) of Advanced LIGO and Advanced Virgo. We carried out the search with the coherent WaveBurst algorithm, which uses minimal assumptions on the signal morphology and does not rely on binary waveform templates. We show that it is sensitive to binary mergers with a detection range that is weakly dependent on eccentricity for all bound systems. Our search did not identify any new binary merger candidates. We interpret these results in light of eccentric binary formation models. We rule out formation channels with rates ⪆100 Gpc-3 yr-1 for e > 0.1, assuming a black hole mass spectrum with a power-law index ≲2