Publications
D’Isanto, A., Cavuoti, S., Brescia, M., Donalek, C., Longo, G., Riccio, G., Djorgovski, S.G., 2016. An analysis of feature relevance in the classification of astronomical transients with machine learning methods. MNRAS, 457, 3, 3119-3132
Polsterer, K.L., D’Isanto, A., Gieseke, F., 2016, Uncertain Photometric Redshifts. (only arXiv, provisional)
D’Isanto, A., 2016. Uncertain Photometric Redshifts with Deep Learning Methods. Proceedings IAU Symposium No. 325, 2016
D’Isanto, A., Polsterer, K.L., 2017. Uncertain Photometric Redshifts via Combining Deep Convolutional and Mixture Density Networks. ESANN 2017 Proceedings
A. D’Isanto and K. L. Polsterer, 2018, A&A, 609, A111. Photometric redshift estimation via deep learning – Generalized and pre-classification-less, image based, fully probabilistic redshifts
A. D’Isanto, S. Cavuoti, F. Gieseke and K. L. Polsterer, 2018, A&A, 616, A97, Return of the features – Efficient feature selection and interpretation for photometric redshifts.
My research interests
I am mainly interested in the application of machine learning, deep learning and data mining techniques to Astrophysical problems, in particular in the regime of Big Data.
Currently I am working on the cosmological field, being interested in the problem of photometric redshifts determination in the form of density distributions.
Current projects
- Probabilistic photometric redshifts via deep learning on COSMOS data
- Efficient feature selection for photometric redshift estimation on galaxies and automatically generated features interpretation (provisional)
- ESCAPE project