Browsing by Author "Andrianomena, Sambatra"

Now showing 1 - 5 of 5
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    Classifying galaxies according to their H I content
    (Oxford University Press, 2020) Andrianomena, Sambatra; Rafieferantsoa, Mika; Dave, Romeel
    We use machine learning to classify galaxies according to their H I content, based on both their optical photometry and environmental properties. The data used for our analyses are the outputs in the range z = 0–1 from MUFASA cosmological hydrodynamic simulation. In our previous paper, where we predicted the galaxy H I content using the same input features, H I-rich galaxies were only selected for the training. In order for the predictions on real observation data to be more accurate, the classifiers built in this study will first establish if a galaxy is H I rich (⁠log(MHI/M∗)>−2⁠) before estimating its neutral hydrogen content using the regressors developed in the first paper.
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    Constraining the astrophysics and cosmology from 21 cm tomography using deep learning with the SKA
    (Oxford University Press, 2020) Hassan, Sultan; Andrianomena, Sambatra; Doughty, Caitlin
    Future Square Kilometre Array (SKA) surveys are expected to generate huge data sets of 21 cm maps on cosmological scales from the Epoch of Reionization. We assess the viability of exploiting machine learning techniques, namely, convolutional neural networks (CNNs), to simultaneously estimate the astrophysical and cosmological parameters from 21 cm maps from seminumerical simulations. We further convert the simulated 21 cm maps into SKA-like mock maps using the detailed SKA antennae distribution, thermal noise, and a recipe for foreground cleaning. We successfully design two CNN architectures (VGGNet-like and ResNet-like) that are both efficiently able to extract simultaneously three astrophysical parameters, namely the photon escape fraction (fesc), the ionizing emissivity power dependence on halo mass (Cion), and the ionizing emissivity redshift evolution index (Dion), and three cosmological parameters, namely the matter density parameter (Ωm), the dimensionless Hubble constant (h), and the matter fluctuation amplitude (σ8), from 21 cm maps at several redshifts.
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    Predicting the neutral hydrogen content of galaxies from optical data using machine learning
    (Oxford University Press, 2018) Rafieferantsoa, Mika; Andrianomena, Sambatra; Dave, Romeel
    We develop a machine learning-based framework to predict the Hi content of galaxies using more straightforwardly observable quantities such as optical photometry and environmental parameters. We train the algorithm on z = 0 - 2 outputs from the Mufasa cosmological hydrodynamic simulation, which includes star formation, feedback, and a heuristic model to quench massive galaxies that yields a reasonable match to a range of survey data including Hi. We employ a variety of machine learning methods (regressors), and quantify their performance using the root mean square error (rmse) and the Pearson correlation coefficient (r). Considering SDSS photometry, 3rd nearest neighbor environment and line of sight peculiar velocities as features, we obtain r > 0:8 accuracy of the Hi-richness prediction, corresponding to rmse< 0:3. Adding near-IR photometry to the features yields some improvement to the prediction. Compared to all the regressors, random forest shows the best performance, with r > 0:9 at z = 0, followed by a Deep Neural Network with r > 0:85. All regressors exhibit a declining performance with increasing redshift, which limits the utility of this approach to z ~<1, and they tend to somewhat over-predict the Hi content of low-Hi galaxies which might be due to Eddington bias in the training sample.We test our approach on the RESOLVE survey data. Training on a subset of RESOLVE, we find that our machine learning method can reasonably well predict the Hi-richness of the remaining RESOLVE data, with rmse~ 0:28. Whenwe train on mock data fromMufasa and test onRESOLVE, this increases to rmse~ 0:45. Our method will be useful for making galaxy-by-galaxy survey predictions and incompleteness corrections for upcoming Hi 21cm surveys such as the LADUMA and MIGHTEE surveys on MeerKAT, over regions where photometry is already available.
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    Probabilistic learning for pulsar classification
    (IOP Publishing, 2022) Andrianomena, Sambatra
    In this work, we explore the possibility of using probabilistic learning to identify pulsar candidates. We make use of Deep Gaussian Process (DGP) and Deep Kernel Learning (DKL). Trained on a balanced training set in order to avoid the effect of class imbalance, the performance of the models, achieving relatively high probability of differentiating the positive class from the negative one (roc-auc ∼ 0.98), is very promising overall. We estimate the predictive entropy of each model predictions and find that DKL is more confident than DGP in its predictions and provides better uncertainty calibration. Upon investigating the effect of training with imbalanced dataset on the models, results show that each model performance decreases with an increasing number of the majority class in the training set. Interestingly, with a number of negative class 10× that of positive class, the models still provide reasonably well calibrated uncertainty, i.e. an expected Uncertainty Calibration Error (UCE) less than 6%.
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    Timing and noise analysis of five millisecond pulsars observed with MeerKAT
    (Oxford University Press, 2025) Andrianomena, Sambatra; Geyer, Marisa; Enwelum, U
    Millisecond pulsars (MSPs) in binary systems are precise laboratories for tests of gravity and the physics of dense matter. Their orbits can show relativistic effects that provide a measurement of the neutron star mass and the pulsars are included in timing array experiments that search for gravitational waves. Neutron star mass measurements are key to eventually solving the neutron star equation of state and these can be obtained by a measure of the Shapiro delay if the orbit is viewed near edge-on. Here, we report on the timing and noise analysis of five MSPs observed with the MeerKAT radio telescope: PSRs J0900–3144, J0921–5202, J1216–6410, J1327–0755, and J1543–5149. We searched for the Shapiro delay in all of the pulsars and obtain weak detections for PSRs J0900–3144, J1216–6410, and J1327–0755. We report a higher significance detection of the Shapiro delay for PSR J1543–5149, giving a precise pulsar mass of Mp = 1.349+0.043-0.061M☉ and companion white-dwarf mass Mc = 0.223+0.005-0.007M☉. This is an atypically low-mass measurement for a recycled MSP. In addition to these Shapiro delays, we also obtain timing model parameters including proper motions and parallax constraints for most of the pulsars.