arXiv subjects

Joongoo Lee

Publications and source records attributed to Joongoo Lee.

5 recordsLinked to original sources

arXiv2022

Deep Learning-based Search for Microlensing Signature from Binary Black Hole Events in GWTC-1 and -2

We present the result of the first deep learning-based search for the signature of microlensing in gravitational waves. This search seeks the signature induced by lenses with masses between $10^3M_\odot$--$10^5M_\odot$ from spectrograms of the binary black hole events in the first and second gravitational-wave transient catalogs. We use a deep learning model trained with spectrograms of simulated noisy gravitational-wave signals to classify the events into two classes, lensed or unlensed. We introduce ensemble learning and a majority voting-based consistency test for the predictions of ensemble learners. The classification scheme of this search primarily classifies one event, GW190707_093326, into the lensed class. To verify the primary classification of this event, we also examine the median probability to the lensed class and observe the resulting value, $0.984^{+0.012}_{-0.342}$, agrees with an empirical criterion $>\!0.6$ for claiming the detection of a lensed signal. However, the uncertainty of the estimated $p$-value for the median probability and error, ranging from 0 to 0.1, convinces us GW190707_093326 is less likely a lensed event because it includes $p\!\geq\!0.05$ where the unlensed hypothesis is true. Therefore, we conclude our search finds no significant evidence of microlensing signature from the evaluated binary black hole events.

gr-qc↗

arXiv2021

Estimation of Photometric Redshifts. II. Identification of Out-of-Distribution Data with Neural Networks

In this study, we propose a three-stage training approach of neural networks for both photometric redshift estimation of galaxies and detection of out-of-distribution (OOD) objects. Our approach comprises supervised and unsupervised learning, which enables using unlabeled (UL) data for OOD detection in training the networks. Employing the UL data, which is the dataset most similar to the real-world data, ensures a reliable usage of the trained model in practice. We quantitatively assess the model performance of photometric redshift estimation and OOD detection using in-distribution (ID) galaxies and labeled OOD (LOOD) samples such as stars and quasars. Our model successfully produces photometric redshifts matched with spectroscopic redshifts for the ID samples and identifies well the LOOD objects with more than 98% accuracy. Although quantitative assessment with the UL samples is impracticable due to the lack of labels and spectroscopic redshifts, we also find that our model successfully estimates reasonable photometric redshifts for ID-like UL samples and filter OOD-like UL objects. The code for the model implementation is available at https://github.com/GooLee0123/MBRNN_OOD.

astro-ph.IM↗

arXiv2021

Estimation of Photometric Redshifts. I. Machine Learning Inference for Pan-STARRS1 Galaxies Using Neural Networks

We present a new machine learning model for estimating photometric redshifts with improved accuracy for galaxies in Pan-STARRS1 data release 1. Depending on the estimation range of redshifts, this model based on neural networks can handle the difficulty for inferring photometric redshifts. Moreover, to reduce bias induced by the new model's ability to deal with estimation difficulty, it exploits the power of ensemble learning. We extensively examine the mapping between input features and target redshift spaces to which the model is validly applicable to discover the strength and weaknesses of trained model. Because our trained model is well calibrated, our model produces reliable confidence information about objects with non-catastrophic estimation. While our model is highly accurate for most test examples residing in the input space, where training samples are densely populated, its accuracy quickly diminishes for sparse samples and unobserved objects (i.e., unseen samples) in training. We report that out-of-distribution (OOD) samples for our model contain both physically OOD objects (i.e., stars and quasars) and galaxies with observed properties not represented by training data. The code for our model is available at https://github.com/GooLee0123/MBRNN for other uses of the model and retraining the model with different data.

astro-ph.IM↗

arXiv2021

Deep Learning Model on Gravitational Waveforms in Merging and Ringdown Phases of Binary Black Hole Coalescences

The waveform templates of the matched filtering-based gravitational-wave search ought to cover wide range of parameters for the prosperous detection. Numerical relativity (NR) has been widely accepted as the most accurate method for modeling the waveforms. Still, it is well-known that NR typically requires a tremendous amount of computational costs. In this paper, we demonstrate a proof-of-concept of a novel deterministic deep learning (DL) architecture that can generate gravitational waveforms from the merger and ringdown phases of the non-spinning binary black hole coalescence. Our model takes ${\cal O}$(1) seconds for generating approximately $1500$ waveforms with a 99.9\% match on average to one of the state-of-the-art waveform approximants, the effective-one-body. We also perform matched filtering with the DL-waveforms and find that the waveforms can recover the event time of the injected gravitational-wave signals.

astro-ph.IM↗

arXiv2021

Identification of Lensed Gravitational Waves with Deep Learning

Similar to light, gravitational waves (GWs) can be lensed. Such lensing phenomena can magnify the waves, create multiple images observable as repeated events, and superpose several waveforms together, inducing potentially discernible patterns on the waves. In particular, when the lens is small, $\lesssim 10^5 M_\odot$, it can produce lensed images with time delays shorter than the typical gravitational-wave signal length that conspire together to form ``beating patterns''. We present a proof-of-principle study utilizing deep learning for identification of such a lensing signature. We bring the excellence of state-of-the-art deep learning models at recognizing foreground objects from background noises to identifying lensed GWs from noise present spectrograms. We assume the lens mass is around $10^3 M_\odot$ -- $10^5 M_\odot$, which can produce the order of millisecond time delays between two images of lensed GWs. We discuss the feasibility of distinguishing lensed GWs from unlensed ones and estimating physical and lensing parameters. Suggested method may be of interest to the study of more complicated lensing configurations for which we do not have accurate waveform templates.

gr-qc↗