Jonathan Lin, Frank Barrows, Francesco Caravelli
The advent of memristive devices offers a promising avenue for efficient and scalable analog computing, particularly for linear algebra operations essential in various scientific and engineering applications. This paper investigates the potential of memristive crossbars in implementing matrix inversion algorithms. We explore both static and dynamic approaches, emphasizing the advantages of analog and in-memory computing for matrix operations beyond multiplication. Our results demonstrate that memristive arrays can significantly reduce computational complexity and power consumption compared to traditional digital methods for certain matrix tasks. Furthermore, we address the challenges of device variability, precision, and scalability, providing insights into the practical implementation of these algorithms.
Nemanja Jovanovic, Pradip Gatkine, Narsireddy Anugu, Rodrigo Amezcua-Correa, Ritoban Basu Thakur, Charles Beichman, Chad Bender, Jean-Philippe Berger, Azzurra Bigioli, Joss Bland-Hawthorn, Guillaume Bourdarot, Charles M. Bradford, Ronald Broeke, Julia Bryant, Kevin Bundy, Ross Cheriton, Nick Cvetojevic, Momen Diab, Scott A. Diddams, Aline N. Dinkelaker, Jeroen Duis, Stephen Eikenberry, Simon Ellis, Akira Endo, Donald F. Figer, Michael Fitzgerald, Itandehui Gris-Sanchez, Simon Gross, Ludovic Grossard, Olivier Guyon, Sebastiaan Y. Haffert, Samuel Halverson, Robert J. Harris, Jinping He, Tobias Herr, Philipp Hottinger, Elsa Huby, Michael Ireland, Rebecca Jenson-Clem, Jeffrey Jewell, Laurent Jocou, Stefan Kraus, Lucas Labadie, Sylvestre Lacour, Romain Laugier, Katarzyna Ławniczuk, Jonathan Lin, Stephanie Leifer, Sergio Leon-Sava, Guillermo Martin, Frantz Martinache, Marc-Antoine Martinod, Benjamin A. Mazin, Stefano Minardi, John D. Monnier, Reinan Moreira, Denis Mourard, Abani Shankar Nayak, Barnaby Norris, Ewelina Obrzud, Karine Perraut, François Reynaud, Steph Sallum, David Schiminovich, Christian Schwab, Eugene Serbayn, Sherif Soliman, Andreas Stoll, Liang Tang, Peter Tuthill, Kerry Vahala, Gautam Vasisht, Sylvain Veilleux, Alexander B. Walter, Edward J. Wollack, Yinzi Xin, Zongyin Yang, Stephanos Yerolatsitis, Yang Zhang, Chang-Ling Zou
Photonics offer numerous functionalities that can be used to realize astrophotonic instruments. The most spectacular example to date is the ESO Gravity instrument at the Very Large Telescope in Chile. Integrated astrophotonic devices stand to offer critical advantages for instrument development, including extreme miniaturization, as well as integration, superior thermal and mechanical stabilization owing to the small footprint, and high replicability offering cost savings. Numerous astrophotonic technologies have been developed to address shortcomings of conventional instruments to date, including for example the development of photonic lanterns, complex aperiodic fiber Bragg gratings, complex beam combiners to enable long baseline interferometry, and laser frequency combs for high precision spectral calibration of spectrometers. Despite these successes, the facility implementation of photonic solutions in astronomical instrumentation is currently limited because of (1) low throughputs from coupling to fibers, coupling fibers to chips, propagation and bend losses, device losses, etc, (2) difficulties with scaling to large channel count devices needed for large bandwidths and high resolutions, and (3) efficient integration of photonics with detectors, to name a few. In this roadmap, we identify 24 areas that need further development. We outline the challenges and advances needed across those areas covering design tools, simulation capabilities, fabrication processes, the need for entirely new components, integration and hybridization and the characterization of devices. To realize these advances the astrophotonics community will have to work cooperatively with industrial partners who have more advanced manufacturing capabilities. With the advances described herein, multi-functional instruments will be realized leading to novel observing capabilities for both ground and space platforms.
Nemanja Jovanovic, Yinzi Xin, Michael P. Fitzgerald, Olivier Guyon, Peter Tuthill, Barnaby Norris, Pradip Gatkine, Greg Sercel, Svarun Soda, Yoo Jung Kim, Jonathan Lin, Sergio Leon-Saval, Rodrigo Amezcua-Correa, Stephanos Yerolatsitis, Julien Lozi, Sebastien Vievard, Chris Betters, Steph Sallum, Daniel Levinstein, Dimitri Mawet, Jeffrey Jewell, J. Kent Wallace, Nick Cvetojevic
Sep 15, 2023·astro-ph.IM·PDF Astrophysical research into exoplanets has delivered thousands of confirmed planets orbiting distant stars. These planets span a wide ranges of size and composition, with diversity also being the hallmark of system configurations, the great majority of which do not resemble our own solar system. Unfortunately, only a handful of the known planets have been characterized spectroscopically thus far, leaving a gaping void in our understanding of planetary formation processes and planetary types. To make progress, astronomers studying exoplanets will need new and innovative technical solutions. Astrophotonics -- an emerging field focused on the application of photonic technologies to observational astronomy -- provides one promising avenue forward. In this paper we discuss various astrophotonic technologies that could aid in the detection and subsequent characterization of planets and in particular themes leading towards the detection of extraterrestrial life.
Yoo Jung Kim, Michael P. Fitzgerald, Jonathan Lin, Steph Sallum, Yinzi Xin, Nemanja Jovanovic, Sergio Leon-Saval, Christopher Betters, Pradip Gatkine, Olivier Guyon, Julien Lozi, Dimitri Mawet, Barnaby Norris, Sébastien Vievard
Nov 30, 2023·astro-ph.IM·PDF Photonic lanterns (PLs) are tapered waveguides that gradually transition from a multi-mode fiber geometry to a bundle of single-mode fibers. In astronomical applications, PLs can efficiently couple multi-mode telescope light into a multi-mode fiber entrance and convert it into multiple single-mode beams. The output beams are highly stable and suitable for feeding into high-resolution spectrographs or photonic chip beam combiners. For instance, by using relative intensities in the output cores as a function of wavelength, PLs can enable spectroastrometry. In addition, by interfering beams in the output cores with a beam combiner in the backend, PLs can be used for high-throughput interferometric imaging. When used on an Extremely Large Telescope (ELT), with its increased sensitivity and angular resolution, the imaging and spectroastrometric capabilities of PLs will be extended to higher contrast and smaller angular scales. We study the potential spectroastrometry and imaging science cases of PLs on ELTs, including study of exomoons, broad-line regions of quasars, and inner circumstellar disks.
Yoo Jung Kim, Michael P. Fitzgerald, Jonathan Lin, Julien Lozi, Sébastien Vievard, Yinzi Xin, Daniel Levinstein, Nemanja Jovanovic, Sergio Leon-Saval, Christopher Betters, Olivier Guyon, Barnaby Norris, Steph Sallum
Spectroastrometry, which measures wavelength-dependent shifts in the center of light, is well-suited for studying objects whose morphology changes with wavelength at very high angular resolutions. Photonic lantern (PL)-fed spectrometers have potential to enable measurement of spectroastrometric signals because the relative intensities between the PL output SMFs contain spatial information on the input scene. In order to use PL output spectra for spectroastrometric measurements, it is important to understand the wavelength-dependent behaviors of PL outputs and develop methods to calibrate the effects of time-varying wavefront errors in ground-based observations. We present experimental characterizations of the 3-port PL on the SCExAO testbed at the Subaru Telescope. We develop spectral response models of the PL and verify the behaviors with lab experiments. We find sinusoidal behavior of astrometric sensitivity of the 3-port PL as a function of wavelength, as expected from numerical simulations. Furthermore, we compare experimental and numerically simulated coupling maps and discuss their potential use for offsetting pointing errors. We then present a method of building PL spectral response models (solving for the transfer matrices as a function of wavelength) using coupling maps, which can be used for further calibration strategies.
Yinzi Xin, Nemanja Jovanovic, Garreth Ruane, Dimitri Mawet, Michael P. Fitzgerald, Daniel Echeverri, Jonathan Lin, Sergio Leon-Saval, Pradip Gatkine, Yoo Jung Kim, Barnaby Norris, Steph Sallum
Sep 15, 2022·astro-ph.IM·PDF Coronagraphs allow for faint off-axis exoplanets to be observed, but are limited to angular separations greater than a few beam widths. Accessing closer-in separations would greatly increase the expected number of detectable planets, which scales inversely with the inner working angle. The Vortex Fiber Nuller (VFN) is an instrument concept designed to characterize exoplanets within a single beam-width. It requires few optical elements and is compatible with many coronagraph designs as a complementary characterization tool. However, the peak throughput for planet light is limited to about 20%, and the measurement places poor constraints on the planet location and flux ratio. We propose to augment the VFN design by replacing its single-mode fiber with a six-port mode-selective photonic lantern, retaining the original functionality while providing several additional ports that reject starlight but couple planet light. We show that the photonic lantern can also be used as a nuller without a vortex. We present monochromatic simulations characterizing the response of the Photonic Lantern Nuller (PLN) to astrophysical signals and wavefront errors, and show that combining exoplanet flux from the nulled ports significantly increases the overall throughput of the instrument. We show using synthetically generated data that the PLN detects exoplanets more effectively than the VFN. Furthermore, with the PLN, the exoplanet can be partially localized, and its flux ratio constrained. The PLN has the potential to be a powerful characterization tool complementary to traditional coronagraphs in future high-contrast instruments.
Yoo Jung Kim, Michael P. Fitzgerald, Sébastien Vievard, Jonathan Lin, Yinzi Xin, Miles Lucas, Olivier Guyon, Julien Lozi, Vincent Deo, Elsa Huby, Sylvestre Lacour, Manon Lallement, Rodrigo Amezcua-Correa, Sergio Leon-Saval, Barnaby Norris, Mathias Nowak, Steph Sallum, Jehanne Sarrazin, Adam Taras, Stephanos Yerolatsitis, Nemanja Jovanovic
Oct 22, 2025·astro-ph.IM·PDF Resolving fine details of astronomical objects provides critical insights into their underlying physical processes. This drives in part the desire to construct ever-larger telescopes and interferometer arrays and to observe at shorter wavelength to lower the diffraction limit of angular resolution. Alternatively, one can aim to overcome the diffraction limit by extracting more information from a single telescope's aperture. A promising way to do this is spatial mode-based imaging, which projects focal-plane field onto a set of spatial modes before detection, retaining focal-plane phase information crucial at small angular scales but typically lost in intensity imaging. However, the practical implementation of mode-based imaging in astronomy from the ground has been challenged by atmospheric turbulence. Here, we present the first on-sky demonstration of a subdiffraction-limited, mode-based measurement using a photonic lantern (PL)-fed spectrometer installed on the SCExAO instrument at the Subaru Telescope. We introduce a novel calibration strategy that mitigates time-varying wavefront error and misalignment effects, leveraging simultaneously recorded focal-plane images and using a spectral-differential technique that self-calibrates the data. Observing the classical Be star $β$ CMi, we detected spectral-differential spatial signals and reconstructed images of its H$α$-emitting disk. We achieved an unprecedented H$α$ photocenter precision of 50$μ$as in about 10-minute observation with a single telescope, measuring the disk's near-far side asymmetry for the first time. This work demonstrates the high precision, efficiency, and practicality of photonic mode-based imaging techniques to recover subdiffraction-limited information, opening new avenues for high angular resolution spectroscopic studies in astronomy.
Jonathan Lin, Aman Desai, Frank Barrows, Francesco Caravelli
Machine learning is a powerful method of extracting meaning from data; unfortunately, current digital hardware is extremely energy-intensive. There is interest in an alternative analog computing implementation that could match the performance of traditional machine learning while being significantly more energy-efficient. However, it remains unclear how to train such analog computing systems while adhering to locality constraints imposed by the physical (as opposed to digital) nature of these systems. Local learning algorithms such as Equilibrium Propagation and Coupled Learning have been proposed to address this issue. In this paper, we develop an algorithm to exactly calculate gradients using a graph theoretic and analytical framework for Kirchhoff's laws. We also introduce Generalized Equilibrium Propagation, a framework encompassing a broad class of Hebbian learning algorithms, including Coupled Learning and Equilibrium Propagation, and show how our algorithm compares. We demonstrate our algorithm using numerical simulations and show that we can train resistor networks without the need for a replica or readout over all resistors, only at the output layer. We also show that under the analytical gradient approach, it is possible to update only a subset of the resistance values without a strong degradation in performance.
Andy A. Shen, Aidan McLoughlin, Zoe Vernon, Jonathan Lin, Richard A. D. Carano, Peter J. Bickel, Zhuang Song, Haiyan Huang
Multiple sclerosis is a chronic autoimmune disease that affects the central nervous system. Understanding multiple sclerosis progression and identifying the implicated brain structures is crucial for personalized treatment decisions. Deformation-based morphometry utilizes anatomical magnetic resonance imaging to quantitatively assess volumetric brain changes at the voxel level, providing insight into how each brain region contributes to clinical progression with regards to neurodegeneration. Utilizing such voxel-level data from a relapsing multiple sclerosis clinical trial, we extend a model-agnostic feature importance metric to identify a robust and predictive feature set that corresponds to clinical progression. These features correspond to brain regions that are clinically meaningful in MS disease research, demonstrating their scientific relevance. When used to predict progression using classical survival models and 3D convolutional neural networks, the identified regions led to the best-performing models, demonstrating their prognostic strength. We also find that these features generalize well to other definitions of clinical progression and can compensate for the omission of highly prognostic clinical features, underscoring the predictive power and clinical relevance of deformation-based morphometry as a regional identification tool.
Yoo Jung Kim, Michael P. Fitzgerald, Jonathan Lin, Yinzi Xin, Daniel Levinstein, Steph Sallum, Nemanja Jovanovic, Sergio Leon-Saval
Sep 13, 2024·astro-ph.IM·PDF We investigate the potential of photonic lantern (PL) fiber fed spectrometers for two-dimensional spectroastrometry. Spectroastrometry, a technique for studying small angular scales by measuring centroid shifts as a function of wavelength, is typically conducted using long-slit spectrographs. However, slit-based spectroastrometry requires observations with multiple position angles to measure two-dimensional spectroastrometric signals. In a typical configuration of PL-fed spectrometers, light from the focal plane is coupled into the few-moded PL, which is then split into several single-mode outputs, with the relative intensities containing astrometric information. The single-moded beams can be fed into a high-resolution spectrometer to measure wavelength-dependent centroid shifts. We perform numerical simulations of a standard 6-port PL and demonstrate its capability of measuring spectroastrometric signals. The effects of photon noise, wavefront errors, and chromaticity are investigated. When the PL is designed to have large linear responses to tip-tilts at the wavelengths of interest, the centroid shifts can be efficiently measured. Furthermore, we provide mock observations of detecting accreting protoplanets. PL spectroastrometry is potentially a simple and efficient technique for detecting spectroastrometric signals.
Frank Barrows, Jonathan Lin, Francesco Caravelli, Dante R. Chialvo
Hardware-based neuromorphic computing remains an elusive goal with the potential to profoundly impact future technologies and deepen our understanding of emergent intelligence. The learning-from-mistakes algorithm is one of the few training algorithms inspired by the brain's simple learning rules, utilizing inhibition and pruning to demonstrate self-organized learning. Here we implement this algorithm in purely neuromorphic memristive hardware through a co-design process. This implementation requires evaluating hardware trade-offs and constraints. It has been shown that learning-from-mistakes successfully trains small networks to function as binary classifiers and perceptrons. However, without tailoring the hardware to the algorithm, performance decreases exponentially as the network size increases. When implementing neuromorphic algorithms on neuromorphic hardware, we investigate the trade-offs between depth, controllability, and capacity, the latter being the number of learnable patterns. We emphasize the significance of topology and the use of governing equations, demonstrating theoretical tools to aid in the co-design of neuromorphic hardware and algorithms. We provide quantitative techniques to evaluate the computational capacity of a neuromorphic device based on the measurements performed and the underlying circuit structure. This approach shows that breaking the symmetry of a neural network can increase both the controllability and average network capacity. By pruning the circuit, neuromorphic algorithms in all-memristive device circuits leverage stochastic resources to drive local contrast in network weights. Our combined experimental and simulation efforts explore the parameters that make a network suited for displaying emergent intelligence from simple rules.
Frank Barrows, Guanming Zhang, Satyam Anand, Zixi Chen, Jonathan Lin, Aman Desai, Stefano Martiniani, Francesco Caravelli
We present a unified framework for embedding and analyzing dynamical systems using generalized projection operators rooted in local conservation laws. By representing physical, biological, and engineered systems as graphs with incidence and cycle matrices, we derive dual projection operators that decompose network fluxes and potentials. This formalism aligns with principles of non-equilibrium thermodynamics and captures a broad class of systems governed by flux-forcing relationships and local constraints. We extend this approach to collective dynamics through the PRojective Embedding of Dynamical Systems (PrEDS), which lifts low-dimensional dynamics into a high-dimensional space, enabling both replication and recovery of the original dynamics. When systems fall within the PrEDS class, their collective behavior can be effectively approximated through projection onto a mean-field space. We demonstrate the versatility of PrEDS across diverse domains, including resistive and memristive circuits, adaptive flow networks (e.g., slime molds), elastic string networks, and particle swarms. Notably, we establish a direct correspondence between PrEDS and swarm dynamics, revealing new insights into optimization and self-organization. Our results offer a general theoretical foundation for analyzing complex networked systems and for designing systems that self-organize through local interactions.
Milton Gomez, Marie McGraw, Saranya Ganesh S., Frederick Iat-Hin Tam, Ilia Azizi, Samuel Darmon, Monika Feldmann, Stella Bourdin, Louis Poulain--Auzéau, Suzana J. Camargo, Jonathan Lin, Dan Chavas, Chia-Ying Lee, Ritwik Gupta, Andrea Jenney, Tom Beucler
TCBench is a benchmark for evaluating global, short to medium-range (1-5 days) forecasts of tropical cyclone (TC) track and intensity. To allow a fair and model-agnostic comparison, TCBench builds on the IBTrACS observational dataset and formulates TC forecasting as predicting the time evolution of an existing tropical system conditioned on its initial position and intensity. TCBench includes state-of-the-art dynamical (TIGGE) and neural weather models (AIFS, Pangu-Weather, FourCastNet v2, GenCast). If not readily available, baseline tracks are consistently derived from model outputs using the TempestExtremes library. For evaluation, TCBench provides deterministic and probabilistic storm-following metrics. On 2023 test cases, neural weather models skillfully forecast TC tracks, while skillful intensity forecasts require additional steps such as post-processing. Designed for accessibility, TCBench helps AI practitioners tackle domain-relevant TC challenges and equips tropical meteorologists with data-driven tools and workflows to improve prediction and TC process understanding. By lowering barriers to reproducible, process-aware evaluation of extreme events, TCBench aims to democratize data-driven TC forecasting.
Eric L. Nielsen, Robert J. De Rosa, Bruce Macintosh, Jason J. Wang, Jean-Baptiste Ruffio, Eugene Chiang, Mark S. Marley, Didier Saumon, Dmitry Savransky, S. Mark Ammons, Vanessa P. Bailey, Travis Barman, Celia Blain, Joanna Bulger, Jeffrey Chilcote, Tara Cotten, Ian Czekala, Rene Doyon, Gaspard Duchene, Thomas M. Esposito, Daniel Fabrycky, Michael P. Fitzgerald, Katherine B. Follette, Jonathan J. Fortney, Benjamin L. Gerard, Stephen J. Goodsell, James R. Graham, Alexandra Z. Greenbaum, Pascale Hibon, Sasha Hinkley, Lea A. Hirsch, Justin Hom, Li-Wei Hung, Rebekah Ilene Dawson, Patrick Ingraham, Paul Kalas, Quinn Konopacky, James E. Larkin, Eve J. Lee, Jonathan W. Lin, Jerome Maire, Franck Marchis, Christian Marois, Stanimir Metchev, Maxwell A. Millar-Blanchaer, Katie M. Morzinski, Rebecca Oppenheimer, David Palmer, Jennifer Patience, Marshall Perrin, Lisa Poyneer, Laurent Pueyo, Roman R. Rafikov, Abhijith Rajan, Julien Rameau, Fredrik T. Rantakyro, Bin Ren, Adam C. Schneider, Anand Sivaramakrishnan, Inseok Song, Remi Soummer, Melisa Tallis, Sandrine Thomas, Kimberly Ward-Duong, Schuyler Wolff
Apr 10, 2019·astro-ph.EP·PDF We present a statistical analysis of the first 300 stars observed by the Gemini Planet Imager Exoplanet Survey (GPIES). This subsample includes six detected planets and three brown dwarfs; from these detections and our contrast curves we infer the underlying distributions of substellar companions with respect to their mass, semi-major axis, and host stellar mass. We uncover a strong correlation between planet occurrence rate and host star mass, with stars M $>$ 1.5 $M_\odot$ more likely to host planets with masses between 2-13 M$_{\rm Jup}$ and semi-major axes of 3-100 au at 99.92% confidence. We fit a double power-law model in planet mass (m) and semi-major axis (a) for planet populations around high-mass stars (M $>$ 1.5M$_\odot$) of the form $\frac{d^2 N}{dm da} \propto m^αa^β$, finding $α$ = -2.4 $\pm$ 0.8 and $β$ = -2.0 $\pm$ 0.5, and an integrated occurrence rate of $9^{+5}_{-4}$% between 5-13 M$_{\rm Jup}$ and 10-100 au. A significantly lower occurrence rate is obtained for brown dwarfs around all stars, with 0.8$^{+0.8}_{-0.5}$% of stars hosting a brown dwarf companion between 13-80 M$_{\rm Jup}$ and 10-100 au. Brown dwarfs also appear to be distributed differently in mass and semi-major axis compared to giant planets; whereas giant planets follow a bottom-heavy mass distribution and favor smaller semi-major axes, brown dwarfs exhibit just the opposite behaviors. Comparing to studies of short-period giant planets from the RV method, our results are consistent with a peak in occurrence of giant planets between ~1-10 au. We discuss how these trends, including the preference of giant planets for high-mass host stars, point to formation of giant planets by core/pebble accretion, and formation of brown dwarfs by gravitational instability.
Xian Wei, Muyu Wang, Shing-Ho Jonathan Lin, Zhengyu Li, Jian Yang, Arafat Al-Jawari, Xuan Tang
Self-attention modules have demonstrated remarkable capabilities in capturing long-range relationships and improving the performance of point cloud tasks. However, point cloud objects are typically characterized by complex, disordered, and non-Euclidean spatial structures with multiple scales, and their behavior is often dynamic and unpredictable. The current self-attention modules mostly rely on dot product multiplication and dimension alignment among query-key-value features, which cannot adequately capture the multi-scale non-Euclidean structures of point cloud objects. To address these problems, this paper proposes a self-attention plug-in module with its variants, Multi-scale Geometry-aware Transformer (MGT). MGT processes point cloud data with multi-scale local and global geometric information in the following three aspects. At first, the MGT divides point cloud data into patches with multiple scales. Secondly, a local feature extractor based on sphere mapping is proposed to explore the geometry inner each patch and generate a fixed-length representation for each patch. Thirdly, the fixed-length representations are fed into a novel geodesic-based self-attention to capture the global non-Euclidean geometry between patches. Finally, all the modules are integrated into the framework of MGT with an end-to-end training scheme. Experimental results demonstrate that the MGT vastly increases the capability of capturing multi-scale geometry using the self-attention mechanism and achieves strong competitive performance on mainstream point cloud benchmarks.