# On the Quality of Deep Representations for Kepler Light Curves Using Variational Auto-Encoders
## 5 Conclusions  
In this work, we used variational (stochastic) auto-encoder models to learn quality deep representations for global-folded transit light curves. We focused on adapting the variational auto-encoders to properly include the time information as delta times outperformed all of their deterministic competitors. We presented two methods, one of which included a re-scaling pre-processing of time series (as an end-to-end architecture) which led to significant improvements on different quality evaluation schemes that we proposed. The evaluation of the learned representation showed that the variational proposals obtain a better quality. This means that the representation is: (i) more informative, i.e., more independent features, so more information could be stored; (ii) more useful, i.e., more effective for the classification of exoplanets; and (iii) more robust, i.e., learn a smoother light curve reconstruction. By adding the re-scaling into the model, these three effects increase. For example, the S-VRAE\({}_{t}\) model ends up being almost as informative as the optimal PCA, at the same time that it produces a denoising effect on the reconstruction that is visually comparable to a Mandel-Aglot fitted model. Furthermore, the model achieves a classification performance quite close to its maximum model capacity. Future RemarksFuture work includes performing a sensitivity analysis on the architecture parameters. Furthermore, we believe that interpreting these informative, useful, and robust features by mapping each dimension to an astronomical concept could enrich current knowledge about orbiting objects. Furthermore, this could be a useful tool for characterizing exoplanets and identifying non-conventional planetary properties, such as magnetic bow shocks, exomoons, or exorings. Another exciting line of study considers the use of the S-VRAE\({}_{t}\) model to help elucidate which characteristics of a transit model are most relevant. With this, we could generate new synthetic transit light curves and face other learning tasks such as classification problems. F.M. : conceptualization, methodology, software, validation, formal analysis, visualization, writing--original draft, writing--review and editing; P.O. : methodology, software, formal analysis, writing--original draft, writing--review and editing; M.B. : conceptualization, writing--original draft, writing--review and editing; visualization; G.M. : data curation, writing--review and editing; M.A. : conceptualization, project administration, funding acquisition, supervision, resources. All authors have read and agreed to the published version of the manuscript. This research was funded by PIIC-DGIP of Universidad Tecnica Federico Santa Maria, ANID-Basal Project FB0008 (AC3E) and ANID PIA/APOYO AFB180002 (CCTVal). Not applicable. The data used in this work may have been obtained from the MAST: [[https://dx.doi.org/10.17909/T9](https://dx.doi.org/10.17909/T9) RP4V]([https://dx.doi.org/10.17909/T9](https://dx.doi.org/10.17909/T9) RP4V) accessed on 20 September 2019. This work was possible thanks to the Chilean Virtual Observatory, ChiVO. The authors declare no conflict of interest. The funding sponsors had no role in the decisions regarding the work and manuscript. The following abbreviations are used in this manuscript: