Search Results
Showing 1-9 of about 9 results.
Ntampaka, Michelle, Bonaca, Ana, Bose, Sownak, Eisenstein, Daniel J., Hadzhiyska, Boryana, Mason, Charlotte, Nagai, Daisuke, and Speagle, Joshua S. 2022. "A Referee Primer for Early Career Astronomers." Bulletin of the American Astronomical Society, 54 051. https://doi.org/10.3847/25c2cfeb.aa5bf2e9.
LoVoCCS. I. Survey Introduction, Data Processing Pipeline, and Early Science ResultsDOI: info:10.3847/1538-4357/ac68e8v. 93384
Fu, Shenming, Dell'Antonio, Ian, Chary, Ranga-Ram, Clowe, Douglas, Cooper, M. C., Donahue, Megan, Evrard, August, Lacy, Mark, Lauer, Tod, Liu, Binyang, McCleary, Jacqueline, Meneghetti, Massimo, Miyatake, Hironao, Montes, Mireia, Natarajan, Priyamvada, Ntampaka, Michelle, Pierpaoli, Elena, Postman, Marc, Sohn, Jubee, Umetsu, Keiichi, Utsumi, Yousuke, and Wilson, Gillian. 2022. "LoVoCCS. I. Survey Introduction, Data Processing Pipeline, and Early Science Results." The Astrophysical Journal, 933 84. https://doi.org/10.3847/1538-4357/ac68e8.
The Importance of Being Interpretable: Toward an Understandable Machine Learning Encoder for Galaxy Cluster CosmologyDOI: info:10.3847/1538-4357/ac423ev. 92645
Ntampaka, Michelle and Vikhlinin, Alexey. 2022. "The Importance of Being Interpretable: Toward an Understandable Machine Learning Encoder for Galaxy Cluster Cosmology." The Astrophysical Journal, 926 45. https://doi.org/10.3847/1538-4357/ac423e.
A Machine-learning Approach to Enhancing eROSITA ObservationsDOI: info:10.3847/1538-4357/ac9b1bv. 94060
Soltis, John, Ntampaka, Michelle, Wu, John F., Zuhone, John, Evrard, August, Farahi, Arya, Ho, Matthew, and Nagai, Daisuke. 2022. "A Machine-learning Approach to Enhancing eROSITA Observations." The Astrophysical Journal, 940 60. https://doi.org/10.3847/1538-4357/ac9b1b.
Emulating Sunyaev-Zeldovich images of galaxy clusters using autoencodersDOI: info:10.1093/mnras/stac438v. 513333–344
Rothschild, Tibor, Nagai, Daisuke, Aung, Han, Green, Sheridan B., Ntampaka, Michelle, and Zuhone, John. 2022. "Emulating Sunyaev-Zeldovich images of galaxy clusters using autoencoders." Monthly Notices of the Royal Astronomical Society, 513 333–344. https://doi.org/10.1093/mnras/stac438.
SuperRAENN: A Semisupervised Supernova Photometric Classification Pipeline Trained on Pan-STARRS1 Medium-Deep Survey SupernovaeDOI: 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. https://doi.org/10.3847/1538-4357/abc6fd.
A deep learning view of the census of galaxy clusters in IllustrisTNGDOI: 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. https://doi.org/10.1093/mnras/staa2690.
Using X-Ray Morphological Parameters to Strengthen Galaxy Cluster Mass Estimates via Machine LearningDOI: 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. https://doi.org/10.3847/1538-4357/ab426f.
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. https://doi.org/10.3847/1538-4357/ab14eb.