245,167 research outputs found
Photometric classification of emission line galaxies with machine-learning methods
In this paper, we discuss an application of machine-learning-based methods to the identification of candidate active galactic nucleus (AGN) from optical survey data and to the automatic classification of AGNs in broad classes. We applied four different machine-learning algorithms, namely the Multi Layer Perceptron, trained, respectively, with the Conjugate Gradient, the Scaled Conjugate Gradient, the Quasi Newton learning rules and the Support Vector Machines, to tackle the problem of the classification of emission line galaxies in different classes, mainly AGNs versus non-AGNs, obtained using optical photometry in place of the diagnostics based on line intensity ratios which are classically used in the literature. Using the same photometric features, we discuss also the behaviour of the classifiers on finer AGN classification tasks, namely Seyfert I versus Seyfert II, and Seyfert versus LINER. Furthermore, we describe the algorithms employed, the samples of spectroscopically classified galaxies used to train the algorithms, the procedure followed to select the photometric parameters and the performances of our methods in terms of multiple statistical indicators. The results of the experiments show that the application of self-adaptive data mining algorithms trained on spectroscopic data sets and applied to carefully chosen photometric parameters represents a viable alternative to the classical methods that employ time-consuming spectroscopic observations
AGN automatic photometric classification
In this paper, we discuss an application of machine-learning-based methods to the identification of candidate active galactic nucleus (AGN) from optical survey data and to the automatic classification ofAGNs in broad classes. We applied four different machine-learning algorithms, namely the Multi Layer Perceptron, trained, respectively, with the Conjugate Gradient, the Scaled Conjugate Gradient, the Quasi Newton learning rules and the Support Vector Machines, Q4 to tackle the problem of the classification of emission line galaxies in different classes, mainly AGNs versus non-AGNs, obtained using optical photometry in place of the diagnostics based on line intensity ratios which are classically used in the literature. Using the same photometric features, we discuss also the behaviour of the classifiers on finer AGN classification tasks, namely Seyfert I versus Seyfert II, and Seyfert versus LINER. Furthermore, we describe the algorithms employed, the samples of spectroscopically classified galaxies used to train the algorithms, the procedure followed to select the photometric parameters and the performances of our methods in terms of multiple statistical indicators. The results of the experiments show that the application of self-adaptive data mining algorithms trained on spectroscopic data sets and applied to carefully chosen photometric parameters represents a viable alternative to the classical methods that employ time-consuming spectroscopic observations
Quantifying correlations between galaxy emission lines and stellar continua
We analyse the correlations between continuum properties and emission line
equivalent widths of star-forming and active galaxies from the Sloan Digital
Sky Survey. Since upcoming large sky surveys will make broad-band observations
only, including strong emission lines into theoretical modelling of spectra
will be essential to estimate physical properties of photometric galaxies. We
show that emission line equivalent widths can be fairly well reconstructed from
the stellar continuum using local multiple linear regression in the continuum
principal component analysis (PCA) space. Line reconstruction is good for
star-forming galaxies and reasonable for galaxies with active nuclei. We
propose a practical method to combine stellar population synthesis models with
empirical modelling of emission lines. The technique will help generate more
accurate model spectra and mock catalogues of galaxies to fit observations of
the new surveys. More accurate modelling of emission lines is also expected to
improve template-based photometric redshift estimation methods. We also show
that, by combining PCA coefficients from the pure continuum and the emission
lines, automatic distinction between hosts of weak active galactic nuclei
(AGNs) and quiescent star-forming galaxies can be made. The classification
method is based on a training set consisting of high-confidence starburst
galaxies and AGNs, and allows for the similar separation of active and
star-forming galaxies as the empirical curve found by Kauffmann et al. We
demonstrate the use of three important machine learning algorithms in the
paper: k-nearest neighbour finding, k-means clustering and support vector
machines.Comment: 14 pages, 14 figures. Accepted by MNRAS on 2015 December 22. The
paper's website with data and code is at
http://www.vo.elte.hu/papers/2015/emissionlines
Exploring SSD Detector for Power Line Insulator Detection on Edge Platform
Power line insulator detection is pivotal for the consistent performance of the entire power system. It forms the basis of Unmanned Aerial Vehicle (UAV) inspection, an emerging trend in power line surveillance. This paper addresses the challenge of insulator detection in cluttered aerial images, given the constraints of a limited dataset and lower computational resources, specifically on the NVIDIA Jetson Nano platform. We have developed two approaches based on active and passive deep learning algorithms, underpinned by the Single Shot Multibox Detector (SSD) meta-architecture with MobileNetV2 as its backbone - SSD300 and SSD640. The proposal models managed a frame rate of 9 fps in 10W power mode and 5.6 fps in 5W power mode. Our experiments demonstrated that the proposed active learning model could conduct robust insulator detection, achieving a mAP of 94.5% while using only 43% of the total dataset, comparable to the traditional deep learning approach's 94.6% mAP using the entire dataset. Significantly, the active learning model seeks feedback during the training process, enabling it to learn from its mistakes and enhance accuracy over time. This also contributes to improved generalizability and interpretability of the model by seeking diverse and representative samples during training, all while reducing the computational and annotation overhead
Predicting Solar Flares Using CNN and LSTM on Two Solar Cycles of Active Region Data
We consider the flare prediction problem that distinguishes flare-imminent
active regions that produce an M- or X-class flare in the future 24 hours, from
quiet active regions that do not produce any flare within hours. Using
line-of-sight magnetograms and parameters of active regions in two data
products covering Solar Cycle 23 and 24, we train and evaluate two deep
learning algorithms -- CNN and LSTM -- and their stacking ensembles. The
decisions of CNN are explained using visual attribution methods. We have the
following three main findings. (1) LSTM trained on data from two solar cycles
achieves significantly higher True Skill Scores (TSS) than that trained on data
from a single solar cycle with a confidence level of at least 0.95. (2) On data
from Solar Cycle 23, a stacking ensemble that combines predictions from LSTM
and CNN using the TSS criterion achieves significantly higher TSS than the
"select-best" strategy with a confidence level of at least 0.95. (3) A visual
attribution method called Integrated Gradients is able to attribute the CNN's
predictions of flares to the emerging magnetic flux in the active region. It
also reveals a limitation of CNN as a flare prediction method using
line-of-sight magnetograms: it treats the polarity artifact of line-of-sight
magnetograms as positive evidence of flares.Comment: 31 pages, 16 figures, accepted in the Ap
Input-driven unsupervised learning in recurrent neural networks
Understanding the theoretical foundations of how memories are encoded and retrieved in neural populations is a central challenge in neuroscience. A popular theoretical scenario for modeling memory function is an attractor neural network with Hebbian learning (e.g. the Hopfield model). The model simplicity and the locality of the synaptic update rules come at the cost of a limited storage capacity, compared with the capacity achieved with supervised learning algorithms, whose biological plausibility is questionable. Here, we present an on-line learning rule for a recurrent neural network that achieves near-optimal performance without an explicit supervisory error signal and using only locally accessible information, and which is therefore biologically plausible. The fully connected network consists of excitatory units with plastic recurrent connections and non-plastic inhibitory feedback stabilizing the network dynamics; the patterns to be memorized are presented on-line as strong afferent currents, producing a bimodal distribution for the neuron synaptic inputs ('local fields'). Synapses corresponding to active inputs are modified as a function of the position of the local field with respect to three thresholds. Above the highest threshold, and below the lowest threshold, no plasticity occurs. In between these two thresholds, potentiation/depression occurs when the local field is above/below an intermediate threshold. An additional parameter of the model allows to trade storage capacity for robustness, i.e. increased size of the basins of attraction. We simulated a network of 1001 excitatory neurons implementing this rule and measured its storage capacity for different sizes of the basins of attraction: our results show that, for any given basin size, our network more than doubles the storage capacity, compared with a standard Hopfield network. Our learning rule is consistent with available experimental data documenting how plasticity depends on firing rate. It predicts that at high enough firing rates, no potentiation should occu
Graph-based Semi-Supervised & Active Learning for Edge Flows
We present a graph-based semi-supervised learning (SSL) method for learning
edge flows defined on a graph. Specifically, given flow measurements on a
subset of edges, we want to predict the flows on the remaining edges. To this
end, we develop a computational framework that imposes certain constraints on
the overall flows, such as (approximate) flow conservation. These constraints
render our approach different from classical graph-based SSL for vertex labels,
which posits that tightly connected nodes share similar labels and leverages
the graph structure accordingly to extrapolate from a few vertex labels to the
unlabeled vertices. We derive bounds for our method's reconstruction error and
demonstrate its strong performance on synthetic and real-world flow networks
from transportation, physical infrastructure, and the Web. Furthermore, we
provide two active learning algorithms for selecting informative edges on which
to measure flow, which has applications for optimal sensor deployment. The
first strategy selects edges to minimize the reconstruction error bound and
works well on flows that are approximately divergence-free. The second approach
clusters the graph and selects bottleneck edges that cross cluster-boundaries,
which works well on flows with global trends
Mistake-Driven Learning in Text Categorization
Learning problems in the text processing domain often map the text to a space
whose dimensions are the measured features of the text, e.g., its words. Three
characteristic properties of this domain are (a) very high dimensionality, (b)
both the learned concepts and the instances reside very sparsely in the feature
space, and (c) a high variation in the number of active features in an
instance. In this work we study three mistake-driven learning algorithms for a
typical task of this nature -- text categorization. We argue that these
algorithms -- which categorize documents by learning a linear separator in the
feature space -- have a few properties that make them ideal for this domain. We
then show that a quantum leap in performance is achieved when we further modify
the algorithms to better address some of the specific characteristics of the
domain. In particular, we demonstrate (1) how variation in document length can
be tolerated by either normalizing feature weights or by using negative
weights, (2) the positive effect of applying a threshold range in training, (3)
alternatives in considering feature frequency, and (4) the benefits of
discarding features while training. Overall, we present an algorithm, a
variation of Littlestone's Winnow, which performs significantly better than any
other algorithm tested on this task using a similar feature set.Comment: 9 pages, uses aclap.st
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