Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userLearning the Lantern: Neural network applications to broadband photonic lantern modelling
461
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
Photonic lanterns allow the decomposition of highly multimodal light into a simplified modal basis such as single-moded and/or few-moded. They are increasingly finding uses in astronomy, optics and telecommunications. Calculating propagation through a photonic lantern using traditional algorithms takes $sim 1$ hour per simulation on a modern CPU. This paper demonstrates that neural networks can bridge the disparate opto-electronic systems, and when trained can achieve a speed-up of over 5 orders of magnitude. We show that this approach can be used to model photonic lanterns with manufacturing defects as well as successfully generalising to polychromatic data. We demonstrate two uses of these neural network models, propagating seeing through the photonic lantern as well as performing global optimisation for purposes such as photonic lantern funnels and photonic lantern nullers.
rate research
Read More
The PolyOculus technology produces large-area-equivalent telescopes by using fiber optics to link modules of multiple semi-autonomous, small, inexpensive, commercial-off-the-shelf telescopes. Crucially, this scalable design has construction costs which are > 10x lower than equivalent traditional large-area telescopes. We have developed a novel photonic lantern approach for the PolyOculus fiber optic linkages which potentially offers substantial advantages over previously considered free-space optical linkages, including much higher coupling efficiencies. We have carried out the first laboratory tests of a photonic lantern prototype developed for PolyOculus, and demonstrate broadband efficiencies of ~91%, confirming the outstanding performance of this technology.
In an attempt to develop a streamlined astrophotonic instrument, we demonstrate the realization of an all-photonic device capable of both multimode to single mode conversion and spectral dispersion on an 8-m class telescope with efficient coupling. The device was a monolithic photonic spectrograph which combined an integrated photonic lantern, and an efficient arrayed waveguide grating device. During on-sky testing, we discovered a previously unreported type of noise that made spectral extraction and calibration extremely difficult. The source of the noise was traced to a wavelength-dependent loss mechanism between the feed fibers multimode near-field pattern, and the modal acceptance profile of the integrated photonic lantern. Extensive modeling of the photonic components replicates the wavelength-dependent loss, and demonstrates an identical effect on the final spectral output. We outline that this could be mitigated by directly injecting into the integrated photonic lantern.
Realization of deep learning with coherent optical field has attracted remarkably attentions presently, which benefits on the fact that optical matrix manipulation can be executed at speed of light with inherent parallel computation as well as low latency. Photonic neural network has a significant potential for prediction-oriented tasks. Yet, real-value Backpropagation behaves somewhat intractably for coherent photonic intelligent training. We develop a compatible learning protocol in complex space, of which nonlinear activation could be selected efficiently depending on the unveiled compatible condition. Compatibility indicates that matrix representation in complex space covers its real counterpart, which could enable a single channel mingled training in real and complex space as a unified model. The phase logical XOR gate with Mach-Zehnder interferometers and diffractive neural network with optical modulation mechanism, implementing intelligent weight learned from compatible learning, are presented to prove the availability. Compatible learning opens an envisaged window for deep photonic neural network.
We present a new concept of an integrated optics component capable of measuring the complex amplitudes of the modes at the tip of a multimode waveguide. The device uses a photonic lantern to split the optical power carried by an $N$-modes waveguide among a collection of single-mode waveguides that excite a periodic array of at least $N^2$ single-mode evanescently-coupled waveguides. The power detected at each output of the array is a linear combination of the products of the modal amplitudes-a relation that can, under suitable conditions, be inverted allowing the derivation of the amplitudes and relative phases of the modal mixture at the input. The expected performance of the device is discussed and its application to the real-time measurement of modal instability in high power fiber lasers is proposed.
Over a century ago, Ivan P. Pavlov, in a classic experiment, demonstrated how dogs can learn to associate a ringing bell with food, thereby causing a ring to result in salivation. Today, however, it is rare to find the use of Pavlovian type associative learning for artificial intelligence (AI) applications. Instead, other biologically-inspired learning concepts, in particular artificial neural networks (ANNs) have flourished, yielding extensive impact on a wide range of fields including finance, healthcare and transportation. However, learning in such conventional ANNs, in particular in the form of modern deep neural networks (DNNs) are usually carried out using the backpropagation method, is computationally and energy intensive. Here we report the experimental demonstration of backpropagation-free learning, achieved using a single (or monadic) associative hardware element. This is realized on an integrated photonic platform using phase change materials combined with on-chip cascaded directional couplers. We link associative learning with supervised learning, based on their common goal of associating certain inputs with correct outputs. We then expand the concept to develop larger-scale supervised learning networks using our monadic Pavlovian photonic hardware, developing a distinct machine-learning framework based on single-element associations and, importantly, using backpropagation-free single-layer weight architectures to approach general learning tasks. Our approach not only significantly reduces the computational burden imposed by learning in conventional neural network approaches, thereby increasing speed and decreasing energy use during learning, but also offers higher bandwidth inherent to a photonic implementation, paving the way for future deployment of fast photonic artificially intelligent machines.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
