1,753 research outputs found
A Meta-Learning Approach to One-Step Active Learning
We consider the problem of learning when obtaining the training labels is
costly, which is usually tackled in the literature using active-learning
techniques. These approaches provide strategies to choose the examples to label
before or during training. These strategies are usually based on heuristics or
even theoretical measures, but are not learned as they are directly used during
training. We design a model which aims at \textit{learning active-learning
strategies} using a meta-learning setting. More specifically, we consider a
pool-based setting, where the system observes all the examples of the dataset
of a problem and has to choose the subset of examples to label in a single
shot. Experiments show encouraging results
First Impressions: Early-Time Classification of Supernovae using Host Galaxy Information and Shallow Learning
Substantial effort has been devoted to the characterization of transient
phenomena from photometric information. Automated approaches to this problem
have taken advantage of complete phase-coverage of an event, limiting their use
for triggering rapid follow-up of ongoing phenomena. In this work, we introduce
a neural network with a single recurrent layer designed explicitly for early
photometric classification of supernovae. Our algorithm leverages transfer
learning to account for model misspecification, host galaxy photometry to solve
the data scarcity problem soon after discovery, and a custom weighted loss to
prioritize accurate early classification. We first train our algorithm using
state-of-the-art transient and host galaxy simulations, then adapt its weights
and validate it on the spectroscopically-confirmed SNe Ia, SNe II, and SNe Ib/c
from the Zwicky Transient Facility Bright Transient Survey. On observed data,
our method achieves an overall accuracy of % within 3 days of an
event's discovery, and an accuracy of % within 30 days of discovery.
At both early and late phases, our method achieves comparable or superior
results to the leading classification algorithms with a simpler network
architecture. These results help pave the way for rapid photometric and
spectroscopic follow-up of scientifically-valuable transients discovered in
massive synoptic surveys.Comment: 24 pages, 8 figures. Accepted to Ap
Dalek -- a deep-learning emulator for TARDIS
Supernova spectral time series contain a wealth of information about the
progenitor and explosion process of these energetic events. The modeling of
these data requires the exploration of very high dimensional posterior
probabilities with expensive radiative transfer codes. Even modest
parametrizations of supernovae contain more than ten parameters and a detailed
exploration demands at least several million function evaluations. Physically
realistic models require at least tens of CPU minutes per evaluation putting a
detailed reconstruction of the explosion out of reach of traditional
methodology. The advent of widely available libraries for the training of
neural networks combined with their ability to approximate almost arbitrary
functions with high precision allows for a new approach to this problem.
Instead of evaluating the radiative transfer model itself, one can build a
neural network proxy trained on the simulations but evaluating orders of
magnitude faster. Such a framework is called an emulator or surrogate model. In
this work, we present an emulator for the TARDIS supernova radiative transfer
code applied to Type Ia supernova spectra. We show that we can train an
emulator for this problem given a modest training set of a hundred thousand
spectra (easily calculable on modern supercomputers). The results show an
accuracy on the percent level (that are dominated by the Monte Carlo nature of
TARDIS and not the emulator) with a speedup of several orders of magnitude.
This method has a much broader set of applications and is not limited to the
presented problem.Comment: 6 pages;5 figures submitted to AAS Journals. Constructive Criticism
invite
The emptiness inside: Finding gaps, valleys, and lacunae with geometric data analysis
Discoveries of gaps in data have been important in astrophysics. For example,
there are kinematic gaps opened by resonances in dynamical systems, or
exoplanets of a certain radius that are empirically rare. A gap in a data set
is a kind of anomaly, but in an unusual sense: Instead of being a single
outlier data point, situated far from other data points, it is a region of the
space, or a set of points, that is anomalous compared to its surroundings. Gaps
are both interesting and hard to find and characterize, especially when they
have non-trivial shapes. We present in this paper a statistic that can be used
to estimate the (local) "gappiness" of a point in the data space. It uses the
gradient and Hessian of the density estimate (and thus requires a
twice-differentiable density estimator). This statistic can be computed at
(almost) any point in the space and does not rely on optimization; it allows to
highlight under-dense regions of any dimensionality and shape in a general and
efficient way. We illustrate our method on the velocity distribution of nearby
stars in the Milky Way disk plane, which exhibits gaps that could originate
from different processes. Identifying and characterizing those gaps could help
determine their origins. We provide in an Appendix implementation notes and
additional considerations for finding under-densities in data, using critical
points and the properties of the Hessian of the density.Comment: 17 pages, 10 figures. Submitted to AJ. Comments welcomed. Revision:
added 3D gridding + restructured outline: implementation notes (Quadratic
Kernel) and methods for approx critical points and 1d-valley now in Anne
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