Photometric Redshifts With Machine Learning, Lights and Shadows on a Complex Data Science Use Case von Brescia, Massimo, Cavuoti, Stefano, Razim, Oleksandra, Amaro, Valeria, Riccio, Giuseppe, Longo, Giuseppe

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Spectroscopic and Photometric Redshift Estimation by Neural Networks for the China Space Station Optical Survey (CSS-OS) von Zhou, Xingchen, Gong, Yan, Meng, Xian-Min, Zhang, Xin, Cao, Ye, Chen, Xuelei, Amaro, Valeria, Fan, Zuhui, Fu, Liping

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Galaxy morphoto-Z with neural Networks (GaZNets): I. Optimized accuracy and outlier fraction from imaging and photometry von Li, Rui, Napolitano, Nicola R., Feng, Haicheng, Li, Ran, Amaro, Valeria, Xie, Linghua, Tortora, Crescenzo, Bilicki, Maciej, Brescia, Massimo, Cavuoti, Stefano, Radovich, Mario

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Inferring galaxy dark halo properties from visible matter with machine learning von von Marttens, Rodrigo, Casarini, Luciano, Napolitano, Nicola R, Wu, Sirui, Amaro, Valeria, Li, Rui, Tortora, Crescenzo, Canabarro, Askery, Wang, Yang

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Galaxy–galaxy lensing in the VOICE deep survey von Luo, Ruibiao, Fu, Liping, Luo, Wentao, Napolitano, Nicola R., Xie, Linghua, Radovich, Mario, Liu, Jing, Li, Rui, Amaro, Valeria, Chen, Zhu, Liu, Dezi, Fan, Zuhui, Covone, Giovanni, Vaccari, Mattia

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Weak-lensing study in VOICE survey – I. Shear measurement von Fu, Liping, Liu, Dezi, Radovich, Mario, Liu, Xiangkun, Pan, Chuzhong, Fan, Zuhui, Covone, Giovanni, Vaccari, Mattia, Amaro, Valeria, Brescia, Massimo, Capaccioli, Massimo, De Cicco, Demetra, Grado, Aniello, Limatola, Luca, Miller, Lance, Napolitano, Nicola R, Paolillo, Maurizio, Pignata, Giuliano

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Strong Lens Search in the ESO Public Survey KiDS von Napolitano, N. R., Covone, G., Roy, N., Tortora, C., La Barbera, F., Radovich, M., Getman, F., Capaccioli, M., Colonna, A., Paolillo, M., Verdoes Kleijn, G. A., Koopmans, L. V. E.

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Searching for Galaxy Clusters in the VST-KiDS Survey von Radovich, M., Puddu, E., Bellagamba, F., Moscardini, L., Roncarelli, M., Getman, F., Grado, A.

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Photometric redshifts with machine learning, lights and shadows on a complex data science use case von Brescia, Massimo, Cavuoti, Stefano, Razim, Oleksandra, Amaro, Valeria, Riccio, Giuseppe, Longo, Giuseppe

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Early Type Galaxies and Structural Parameters from ESO Public Survey KiDS von Roy, N., Napolitano, N. R., La Barbera, F., Tortora, C., Getman, F., Radovich, M., Capaccioli, M.

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Galaxy morphoto-Z with neural Networks (GaZNets). I. Optimized accuracy and outlier fraction from Imaging and Photometry von Li, Rui, Napolitano, Nicola R, Feng, Haicheng, Li, Ran, Amaro, Valeria, Xie, Linghua, Tortora, Crescenzo, Bilicki, Maciej, Brescia, Massimo, Cavuoti, Stefano, Radovich, Mario

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Inferring galaxy dark halo properties from visible matter with Machine Learning von Rodrigo von Marttens, Casarini, Luciano, Napolitano, Nicola R, Wu, Sirui, Amaro, Valeria, Li, Rui, Tortora, Crescenzo, Canabarro, Askery, Wang, Yang

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Galaxy-galaxy lensing in the VOICE deep survey von Luo, Ruibiao, Fu, Liping, Luo, Wentao, Napolitano, Nicola R, Xie, Linghua, Radovich, Mario, Liu, Jing, Li, Rui, Amaro, Valeria, Chen, Zhu, Liu, Dezi, Fan, Zuhui, Covone, Giovanni, Vaccari, Mattia

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Spectroscopic and Photometric Redshift Estimation by Neural Networks For the China Space Station Optical Survey (CSS-OS) von Zhou, Xingchen, Gong, Yan, Xian-Min Meng, Zhang, Xin, Cao, Ye, Chen, Xuelei, Amaro, Valeria, Fan, Zuhui, Fu, Liping

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Rejection criteria based on outliers in the KiDS photometric redshifts and PDF distributions derived by machine learning von Amaro, Valeria, Cavuoti, Stefano, Brescia, Massimo, Riccio, Giuseppe, Tortora, Crescenzo, D'Addona, Maurizio, Michele Delli Veneri, Napolitano, Nicola R, Radovich, Mario, Longo, Giuseppe

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Data Deluge in Astrophysics: Photometric Redshifts as a Template Use Case von Brescia, Massimo, Cavuoti, Stefano, Amaro, Valeria, Riccio, Giuseppe, Angora, Giuseppe, Vellucci, Civita, Longo, Giuseppe

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Probability density estimation of photometric redshifts based on machine learning von Cavuoti, Stefano, Brescia, Massimo, Amaro, Valeria, Vellucci, Civita, Longo, Giuseppe, Tortora, Crescenzo

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METAPHOR: Probability density estimation for machine learning based photometric redshifts von Amaro, Valeria, Cavuoti, Stefano, Brescia, Massimo, Vellucci, Civita, Tortora, Crescenzo, Longo, Giuseppe

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METAPHOR: A machine learning based method for the probability density estimation of photometric redshifts von Cavuoti, Stefano, Amaro, Valeria, Brescia, Massimo, Vellucci, Civita, Tortora, Crescenzo, Longo, Giuseppe

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Cooperative photometric redshift estimation von Cavuoti, Stefano, Tortora, Crescenzo, Brescia, Massimo, Longo, Giuseppe, Radovich, Mario, Napolitano, Nicola R, Amaro, Valeria, Vellucci, Civita

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