142 research outputs found
PyLightcurve-torch: a transit modelling package for deep learning applications in PyTorch
We present a new open source python package, based on PyLightcurve and
PyTorch, tailored for efficient computation and automatic differentiation of
exoplanetary transits. The classes and functions implemented are fully
vectorised, natively GPU-compatible and differentiable with respect to the
stellar and planetary parameters. This makes PyLightcurve-torch suitable for
traditional forward computation of transits, but also extends the range of
possible applications with inference and optimisation algorithms requiring
access to the gradients of the physical model. This endeavour is aimed at
fostering the use of deep learning in exoplanets research, motivated by an ever
increasing amount of stellar light curves data and various incentives for the
improvement of detection and characterisation techniques.Comment: 7 pages, 3 figures; submission status updated, fig 2 caption adde
The Challenge of Machine Learning in Space Weather Nowcasting and Forecasting
The numerous recent breakthroughs in machine learning (ML) make imperative to
carefully ponder how the scientific community can benefit from a technology
that, although not necessarily new, is today living its golden age. This Grand
Challenge review paper is focused on the present and future role of machine
learning in space weather. The purpose is twofold. On one hand, we will discuss
previous works that use ML for space weather forecasting, focusing in
particular on the few areas that have seen most activity: the forecasting of
geomagnetic indices, of relativistic electrons at geosynchronous orbits, of
solar flares occurrence, of coronal mass ejection propagation time, and of
solar wind speed. On the other hand, this paper serves as a gentle introduction
to the field of machine learning tailored to the space weather community and as
a pointer to a number of open challenges that we believe the community should
undertake in the next decade. The recurring themes throughout the review are
the need to shift our forecasting paradigm to a probabilistic approach focused
on the reliable assessment of uncertainties, and the combination of
physics-based and machine learning approaches, known as gray-box.Comment: under revie
AdaSwarm: Augmenting Gradient-Based Optimizers in Deep Learning with Swarm Intelligence
This paper introduces AdaSwarm, a novel gradient-free optimizer which has similar or even better performance than the Adam optimizer adopted in neural networks. In order to support our proposed AdaSwarm, a novel Exponentially weighted Momentum Particle Swarm Optimizer (EMPSO), is proposed. The ability of AdaSwarm to tackle optimization problems is attributed to its capability to perform good gradient approximations. We show that, the gradient of any function, differentiable or not, can be approximated by using the parameters of EMPSO. This is a novel technique to simulate GD which lies at the boundary between numerical methods and swarm intelligence. Mathematical proofs of the gradient approximation produced are also provided. AdaSwarm competes closely with several state-of-the-art (SOTA) optimizers. We also show that AdaSwarm is able to handle a variety of loss functions during backpropagation, including the maximum absolute error (MAE)
Deep Learning, Shallow Dips: Transit light curves have never been so trendy
At the crossroad between photometry and time-domain astronomy, light curves
are invaluable data objects to study distant events and sources of light even when
they can not be spatially resolved. In particular, the field of exoplanet sciences has
tremendously benefited from acquired stellar light curves to detect and characterise
a majority of the outer worlds that we know today. Yet, their analysis is challenged
by the astrophysical and instrumental noise often diluting the signals of interest. For
instance, the detection of shallow dips caused by transiting exoplanets in stellar light
curves typically require a precision of the order of 1 ppm to 100 ppm in units of
stellar flux, and their very study directly depends upon our capacity to correct for
instrumental and stellar trends.
The increasing number of light curves acquired from space and ground-based
telescopes—of the order of billions—opens up the possibility for global, efficient,
automated processing algorithms to replace individual, parametric and hard-coded
ones. Luckily, the field of deep learning is also progressing fast, revolutionising time
series problems and applications. This reinforces the incentive to develop data-driven
approaches hand-in-hand with existing scientific models and expertise.
With the study of exoplanetary transits in focus, I developed automated approaches to learn and correct for the time-correlated noise in and across light curves.
In particular, I present (i) a deep recurrent model trained via a forecasting objective
to detrend individual transit light curves (e.g. from the Spitzer space telescope); (ii)
the power of a Transformer-based model leveraging whole datasets of light curves
(e.g. from large transit surveys) to learn the trend via a masked objective; (iii) a
hybrid and flexible framework to combine neural networks with transit physics
Expect the Unexpected: Deciphering Exoplanetary Signals with Machine Learning Techniques
The field of exoplanets has enjoyed unprecedented growth in the past decades, planets are being discovered at an exponential rate. With the launch of next-generation facilities in the coming decades, the arrival of high-quality spectroscopic data is expected to bring about yet another revolutionary change in our understanding of these remote worlds. The field has been actively developing tools to comprehend the large stream of incoming data, and among them, Machine Learning techniques are building up momentum as an alternative to conventional approaches. In this work, I developed methodologies to uncover potential biases in the interpretation of the exoplanetary atmosphere introduced during data analysis. I showed that naively combining observations from different instruments might lead to biased results, and in some extreme cases like WASP-96 b, it is impossible to com- bine observations. A new scheme of retrieval framework, namely the L - retrieval, holds the potential to detect incompatibility among different datasets by combining light-curve fitting with atmospheric radiative transfer modelling. This work also documents the application of ML techniques to two distinct fields of exoplanetary science: a planet signal detection pipeline for direct imaging data and a suite of diagnostic tools designed for the characterisation of exoplanets. In both approaches, I pioneered the integration of Explainable AI techniques to improve the reliability of the deep learning models. Initial successes of these novel methodologies have provided an exciting prospect to tackle upcoming challenges with the use of Artificial Intelligence. How- ever, significant work remains to progress these models from their current proof-of- concept stage to general application framework. In this thesis, I will discuss their current limitations, potential future, and the next steps required
Domain adaptation based transfer learning approach for solving PDEs on complex geometries
In machine learning, if the training data is independently and identically distributed as the test data then a trained model can make an accurate predictions for new samples of data. Conventional machine learning has a strong dependence on massive amounts of training data which are domain specific to understand their latent patterns. In contrast, Domain adaptation and Transfer learning methods are sub-fields within machine learning that are concerned with solving the inescapable problem of insufficient training data by relaxing the domain dependence hypothesis. In this contribution, this issue has been addressed and by making a novel combination of both the methods we develop a computationally efficient and practical algorithm to solve boundary value problems based on nonlinear partial differential equations. We adopt a meshfree analysis framework to integrate the prevailing geometric modelling techniques based on NURBS and present an enhanced deep collocation approach that also plays an important role in the accuracy of solutions. We start with a brief introduction on how these methods expand upon this framework. We observe an excellent agreement between these methods and have shown that how fine-tuning a pre-trained network to a specialized domain may lead to an outstanding performance compare to the existing ones. As proof of concept, we illustrate the performance of our proposed model on several benchmark problems. © 2022, The Author(s)
Application of machine learning techniques to weather forecasting
Weather forecasting is, still today, a human based activity. Although computer simulations play a major role in modelling the state and evolution of the atmosphere,
there is a lack of methodologies to automate the interpretation of the information
generated by these models. This doctoral thesis explores the use of machine learning
methodologies to solve specific problems in meteorology and particularly focuses
on the exploration of methodologies to improve the accuracy of numerical weather
prediction models using machine learning. The work presented in this manuscript
contains two different approaches using machine learning. In the first part, classical
methodologies, such as multivariate non-parametric regression and binary trees are
explored to perform regression on meteorological data. In this first part, we particularly focus on forecasting wind, where the circular nature of this variable opens
interesting challenges for classic machine learning algorithms and techniques. The
second part of this thesis, explores the analysis of weather data as a generic structured prediction problem using deep neural networks. Neural networks, such as
convolutional and recurrent networks provide a method for capturing the spatial
and temporal structure inherent in weather prediction models. This part explores
the potential of deep convolutional neural networks in solving difficult problems in
meteorology, such as modelling precipitation from basic numerical model fields. The
research performed during the completion of this thesis demonstrates that collaboration between the machine learning and meteorology research communities is mutually beneficial and leads to advances in both disciplines. Weather forecasting models
and observational data represent unique examples of large (petabytes), structured
and high-quality data sets, that the machine learning community demands for developing the next generation of scalable algorithms
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