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In this work we developed a deep learning technique that successfully solves a non-linear dynamic control problem. Instead of directly tackling the control problem, we combined methods in probabilistic neural networks and a Kalman-filter-inspired model to build a non-linear state estimator for the system. We then used the estimated states to implement a trivial controller for the now fully observable system. We applied this technique to a crucial non-linear control problem that arises in the operation of the Laser Interferometer Gravitational-Wave Observatory (LIGO) system, an interferometric gravitational-wave observatory. We demonstrated in simulation that our approach can learn from data to estimate the state of the system, allowing a successful control of the interferometer’s mirror. We also developed a computationally efficient model that can run in real time at high sampling rate on a single modern CPU core, one of the key requirements for the implementation of our solution in the LIGO digital control system. We believe these techniques could be used to help tackle similar non-linear control problems in other applications.

 

Blistering is a phenomenon sometimes observed in sputtered-deposited thin films but seldom investigated in detail. Here, we consider the case of titania-doped germania (TGO)/silica multi-layers deposited by ion beam sputtering. TGO is a candidate as high refractive index material in the Bragg mirrors for the next iteration of gravitational waves detectors. It needs to be annealed at 600°C for 100h in order to reach the desired relaxation state. However under some growth conditions, in 52-layer TGO/silica stacks, blistering occurs upon annealing at a temperature near 500°C, which corresponds to the temperature where Ar desorbs from TGO. In order to better understand the blistering phenomenon, we measure the Ar transport in single layers of TGO and silica. In the case of <1 μm-thick TGO layers, the Ar desorption is mainly limited by detrapping. The transport model also correctly predicts the evolution of the total amount of Ar in a 8.5 μm stack of TGO and silica layers annealed at 450°C, but in that case, the process is mainly limited by diffusion. Since Ar diffusion is an order of magnitude slower in TGO compared to silica, we observe a correspondingly strong accumulation of Ar in TGO. The Ar transport model is used to explain some regimes of the blisters growth, and we find indications that Ar accumulation is a driver for their growth in general, but the blisters nucleation remains a complex phenomenon influenced by several other factors including stress, substrate roughness, and impurities.

 

We present a closed-form analytical expression that describes the effect of clipping by circular apertures on laser beams, described in terms of an expansion in eigenmodes of the paraxial wave equation, in either the Laguerre–Gauss or Hermite–Gauss bases. This result provides a faster and more accurate way to compute the coupling between higher-order modes, compared to commonly used grid-based numerical integration.

 

Ground-based gravitational-wave (GW) detectors are a frontier large-scale experiment in experimental astrophysics. Given the elusive nature of GWs, the ground-based detectors have complex interacting systems made up of exquisitely sensitive instruments which makes them susceptible to terrestrial noise sources. As these noise transients - termed as glitches - appear in the detector's main data channel, they can mask or mimic real GW signals resulting in false alarms in the detection pipelines. Given their high rate of occurrence compared to astrophysical signals, it is vital to examine these glitches and probe their origin in the detector's environment and instruments in order to possibly eliminate them from the science data. In this paper we present a tensor factorization-based data mining approach to finding witness events to these glitches in the network of heterogeneous sensors that monitor the detectors and build a catalog which can aid human operators in diagnosing the sources of these noise transients. Available from: https://openreview.net/forum?id=O9q0ma6Oh5e 

Glasses are nonequilibrium solids with properties highly dependent on their method of preparation. In vapor-deposited molecular glasses, structural organization could be readily tuned with deposition rate and substrate temperature. Here, we show that the atomic arrangement of strong network-forming GeO 2 glass is modified at medium range (<2 nm) through vapor deposition at elevated temperatures. Raman spectral signatures distinctively show that the population of six-membered GeO 4 rings increases at elevated substrate temperatures. Deposition near the glass transition temperature is more efficient than postgrowth annealing in modifying atomic structure at medium range. The enhanced medium-range organization correlates with reduction of the room temperature internal friction. Identifying the microscopic origin of room temperature internal friction in amorphous oxides is paramount to design the next-generation interference coatings for mirrors of the end test masses of gravitational wave interferometers, in which the room temperature internal friction is a main source of noise limiting their sensitivity.