3 research outputs found
A model local interpretation routine for deep learning based radio galaxy classification
Radio galaxy morphological classification is one of the critical steps when
producing source catalogues for large-scale radio continuum surveys. While many
recent studies attempted to classify source radio morphology from survey image
data using deep learning algorithms (i.e., Convolutional Neural Networks), they
concentrated on model robustness most time. It is unclear whether a model
similarly makes predictions as radio astronomers did. In this work, we used
Local Interpretable Model-agnostic Explanation (LIME), an state-of-the-art
eXplainable Artificial Intelligence (XAI) technique to explain model prediction
behaviour and thus examine the hypothesis in a proof-of-concept manner. In what
follows, we describe how \textbf{LIME} generally works and early results about
how it helped explain predictions of a radio galaxy classification model using
this technique.Comment: 4 pages, 1 figure, accepted summary paper for URSI GASS 2023 J0
Galaxy Light profile neural Networks (GaLNets). II. Bulge-Disc decomposition in optical space-based observations
Bulge-disk (B-D) decomposition is an effective diagnostic to characterize the
galaxy morphology and understand its evolution across time. So far,
high-quality data have allowed detailed B-D decomposition to redshift below
0.5, with limited excursions over small volumes at higher redshifts.
Next-generation large sky space surveys in optical, e.g. from the China Space
Station Telescope (CSST), and near-infrared, e.g. from the space EUCLID
mission, will produce a gigantic leap in these studies as they will provide
deep, high-quality photometric images over more than 15000 deg2 of the sky,
including billions of galaxies. Here, we extend the use of the Galaxy Light
profile neural Network (GaLNet) to predict 2-S\'ersic model parameters,
specifically from CSST data. We simulate point-spread function (PSF) convolved
galaxies, with realistic B-D parameter distributions, on CSST mock observations
to train the new GaLNet and predict the structural parameters (e.g. magnitude,
effective radius, Sersic index, axis ratio, etc.) of both bulge and disk
components. We find that the GaLNet can achieve very good accuracy for most of
the B-D parameters down to an -band magnitude of 23.5 and redshift 1.
The best accuracy is obtained for magnitudes, implying accurate bulge-to-total
(B/T) estimates. To further forecast the CSST performances, we also discuss the
results of the 1-S\'ersic GaLNet and show that CSST half-depth data will allow
us to derive accurate 1-component models up to 24 and redshift
z1.7
Mechanochemically accessing a challenging-to-synthesize depolymerizable polymer
Polymers with low ceiling temperatures (Tc) are highly desirable as they can depolymerize under mild conditions, but they typically suffer from demanding synthetic conditions and poor stability. Here, the authors envision that this challenge can be addressed by developing high-Tc polymers that can be converted into low-Tc polymers on demand