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Showing 1-4 of about 4 results.
A deep learning view of the census of galaxy clusters in IllustrisTNGSu, Y.Zhang, Y.Liang, G.ZuHone, John A.Barnes, D. J.Jacobs, N. B.Ntampaka, MichelleForman, William R.Nulsen, Paul E. J.Kraft, Ralph P.Jones, C.DOI: info:10.1093/mnras/staa2690v. 4985620–5628
Su, Y., Zhang, Y., Liang, G., ZuHone, John A., Barnes, D. J., Jacobs, N. B., Ntampaka, Michelle, Forman, William R., Nulsen, Paul E. J., Kraft, Ralph P., and Jones, C. 2020. "A deep learning view of the census of galaxy clusters in IllustrisTNG." Monthly Notices of the Royal Astronomical Society 498:5620– 5628.
ID: 158586
Type: article
Authors: Su, Y.; Zhang, Y.; Liang, G.; ZuHone, John A.; Barnes, D. J.; Jacobs, N. B.; Ntampaka, Michelle; Forman, William R.; Nulsen, Paul E. J.; Kraft, Ralph P.; Jones, C.
Abstract: The origin of the diverse population of galaxy clusters remains an unexplained aspect of large-scale structure formation and cluster evolution. We present a novel method of using X-ray images to identify cool core (CC), weak cool core (WCC), and non-cool core (NCC) clusters of galaxies that are defined by their central cooling times. We employ a convolutional neural network, ResNet-18, which is commonly used for image analysis, to classify clusters. We produce mock Chandra X-ray observations for a sample of 318 massive clusters drawn from the IllustrisTNG simulations. The network is trained and tested with low-resolution mock Chandra images covering a central 1 Mpc square for the clusters in our sample. Without any spectral information, the deep learning algorithm is able to identify CC, WCC, and NCC clusters, achieving balanced accuracies (BAcc) of 92 per cent, 81 per cent, and 83 per cent, respectively. The performance is superior to classification by conventional methods using central gas densities, with an average ${\rm BAcc}=81{{\ \rm per\ cent}}$ , or surface brightness concentrations, giving ${\rm BAcc}=73{{\ \rm per\ cent}}$ . We use class activation mapping to localize discriminative regions for the classification decision. From this analysis, we observe that the network has utilized regions from cluster centres out to r ? 300 kpc and r ? 500 kpc to identify CC and NCC clusters, respectively. It may have recognized features in the intracluster medium that are associated with AGN feedback and disruptive major mergers.
SuperRAENN: A Semisupervised Supernova Photometric Classification Pipeline Trained on Pan-STARRS1 Medium-Deep Survey SupernovaeVillar, Victoria AshleyHosseinzadeh, GriffinBerger, EdoNtampaka, MichelleJones, David O.Challis, PeterChornock, RyanDrout, Maria R.Foley, Ryan J.Kirshner, Robert P.Lunnan, RagnhildMargutti, RaffaellaMilisavljevic, DanSanders, NathanPan, Yen-ChenRest, ArminScolnic, Daniel M.Magnier, EugeneMetcalfe, NigelWainscoat, RichardWaters, ChristopherDOI: info:10.3847/1538-4357/abc6fdv. 90594
Villar, Victoria Ashley, Hosseinzadeh, Griffin, Berger, Edo, Ntampaka, Michelle, Jones, David O., Challis, Peter, Chornock, Ryan, Drout, Maria R., Foley, Ryan J., Kirshner, Robert P., Lunnan, Ragnhild, Margutti, Raffaella, Milisavljevic, Dan, Sanders, Nathan, Pan, Yen-Chen, Rest, Armin, Scolnic, Daniel M., Magnier, Eugene, Metcalfe, Nigel, Wainscoat, Richard, and Waters, Christopher. 2020. "SuperRAENN: A Semisupervised Supernova Photometric Classification Pipeline Trained on Pan-STARRS1 Medium-Deep Survey Supernovae." The Astrophysical Journal 905:94.
ID: 158589
Type: article
Authors: Villar, Victoria Ashley; Hosseinzadeh, Griffin; Berger, Edo; Ntampaka, Michelle; Jones, David O.; Challis, Peter; Chornock, Ryan; Drout, Maria R.; Foley, Ryan J.; Kirshner, Robert P.; Lunnan, Ragnhild; Margutti, Raffaella; Milisavljevic, Dan; Sanders, Nathan; Pan, Yen-Chen; Rest, Armin; Scolnic, Daniel M.; Magnier, Eugene; Metcalfe, Nigel; Wainscoat, Richard; Waters, Christopher
Abstract: Automated classification of supernovae (SNe) based on optical photometric light-curve information is essential in the upcoming era of wide-field time domain surveys, such as the Legacy Survey of Space and Time (LSST) conducted by the Rubin Observatory. Photometric classification can enable real-time identification of interesting events for extended multiwavelength follow-up, as well as archival population studies. Here we present the complete sample of 5243 "SN-like" light curves (in gP1rP1iP1zP1) from the Pan-STARRS1 Medium-Deep Survey (PS1-MDS). The PS1-MDS is similar to the planned LSST Wide-Fast-Deep survey in terms of cadence, filters, and depth, making this a useful training set for the community. Using this data set, we train a novel semisupervised machine learning algorithm to photometrically classify 2315 new SN-like light curves with host galaxy spectroscopic redshifts. Our algorithm consists of an RF supervised classification step and a novel unsupervised step in which we introduce a recurrent autoencoder neural network (RAENN). Our final pipeline, dubbed SuperRAENN, has an accuracy of 87% across five SN classes (Type Ia, Ibc, II, IIn, SLSN-I) and macro-averaged purity and completeness of 66% and 69%, respectively. We find the highest accuracy rates for SNe Ia and SLSNe and the lowest for SNe Ibc. Our complete spectroscopically and photometrically classified samples break down into 62.0% Type Ia (1839 objects), 19.8% Type II (553 objects), 4.8% Type IIn (136 objects), 11.7% Type Ibc (291 objects), and 1.6% Type I SLSNe (54 objects).
Using X-Ray Morphological Parameters to Strengthen Galaxy Cluster Mass Estimates via Machine LearningGreen, Sheridan B.Ntampaka, MichelleNagai, DaisukeLovisari, LorenzoDolag, KlausEckert, DominiqueZuHone, John A.DOI: info:10.3847/1538-4357/ab426fv. 88433
Green, Sheridan B., Ntampaka, Michelle, Nagai, Daisuke, Lovisari, Lorenzo, Dolag, Klaus, Eckert, Dominique, and ZuHone, John A. 2019. "Using X-Ray Morphological Parameters to Strengthen Galaxy Cluster Mass Estimates via Machine Learning." The Astrophysical Journal 884:33.
ID: 154618
Type: article
Authors: Green, Sheridan B.; Ntampaka, Michelle; Nagai, Daisuke; Lovisari, Lorenzo; Dolag, Klaus; Eckert, Dominique; ZuHone, John A.
Abstract: We present a machine-learning approach for estimating galaxy cluster masses, trained using both Chandra and eROSITA mock X-ray observations of 2041 clusters from the Magneticum simulations. We train a random forest (RF) regressor, an ensemble learning method based on decision tree regression, to predict cluster masses using an input feature set. The feature set uses core-excised X-ray luminosity and a variety of morphological parameters, including surface brightness concentration, smoothness, asymmetry, power ratios, and ellipticity. The regressor is cross-validated and calibrated on a training sample of 1615 clusters (80% of sample), and then results are reported as applied to a test sample of 426 clusters (20% of sample). This procedure is performed for two different mock observation series in an effort to bracket the potential enhancement in mass predictions that can be made possible by including dynamical state information. The first series is computed from idealized Chandra-like mock cluster observations, with high spatial resolution, long exposure time (1 Ms), and the absence of background. The second series is computed from realistic-condition eROSITA mocks with lower spatial resolution, short exposures (2 ks), instrument effects, and background photons modeled. We report a 20% reduction in the mass estimation scatter when either series is used in our RF model compared to a standard regression model that only employs core-excised luminosity. The morphological parameters that hold the highest feature importance are smoothness, asymmetry, and surface brightness concentration. Hence these parameters, which encode the dynamical state of the cluster, can be used to make more accurate predictions of cluster masses in upcoming surveys, offering a crucial step forward for cosmological analyses.
A Deep Learning Approach to Galaxy Cluster X-Ray MassesNtampaka, MichelleZuHone, J.Eisenstein, D.Nagai, D.Vikhlinin, A.Hernquist, L.Marinacci, F.Nelson, D.Pakmor, R.Pillepich, A.Torrey, P.Vogelsberger, M.DOI: info:10.3847/1538-4357/ab14ebv. 87682
Ntampaka, Michelle, ZuHone, J., Eisenstein, D., Nagai, D., Vikhlinin, A., Hernquist, L., Marinacci, F., Nelson, D., Pakmor, R., Pillepich, A., Torrey, P., and Vogelsberger, M. 2019. "A Deep Learning Approach to Galaxy Cluster X-Ray Masses." The Astrophysical Journal 876:82.
ID: 151867
Type: article
Authors: Ntampaka, Michelle; ZuHone, J.; Eisenstein, D.; Nagai, D.; Vikhlinin, A.; Hernquist, L.; Marinacci, F.; Nelson, D.; Pakmor, R.; Pillepich, A.; Torrey, P.; Vogelsberger, M.
Abstract: We present a machine-learning (ML) approach for estimating galaxy cluster masses from Chandra mock images. We utilize a Convolutional Neural Network (CNN), a deep ML tool commonly used in image recognition tasks. The CNN is trained and tested on our sample of 7896 Chandra X-ray mock observations, which are based on 329 massive clusters from the {\text{}}{IllustrisTNG} simulation. Our CNN learns from a low resolution spatial distribution of photon counts and does not use spectral information. Despite our simplifying assumption to neglect spectral information, the resulting mass values estimated by the CNN exhibit small bias in comparison to the true masses of the simulated clusters (‑0.02 dex) and reproduce the cluster masses with low intrinsic scatter, 8% in our best fold and 12% averaging over all. In contrast, a more standard core-excised luminosity method achieves 15%–18% scatter. We interpret the results with an approach inspired by Google DeepDream and find that the CNN ignores the central regions of clusters, which are known to have high scatter with mass.