Pérez F., Pichara K., Huijse P., Catelan M., Mery D. (2021)

Informative Bayesian model selection for RR Lyrae star classifiers

Monthly Notices of the Royal Astronomical Society 503 1 484–497

Rojas F., Maurin L., Dünner R., Pichara K. (2020)

Classifying CMB time-ordered data through deep neural networks

Monthly Notices of the Royal Astronomical Society 494 3 3741–3749

Becker I., Pichara K., Catelan M., Protopapas P., Aguirre C., Nikzat F. (2020)

Scalable End-to-End Recurrent Neural Network for Variable Star Classification

Monthly Notices of the Royal Astronomical Society 493 2 2981-2995

Zorich L., Pichara K., Protopapas P. (2020)

Streaming classification of variable stars

Monthly Notices of the Royal Astronomical Society 492 2 2897-2909

Ferreira Lopez C.E.,  Cross N.J.G.,  Catelan M.,  Minniti D.,  Hempel M.,  Lucas P.W.,  Angeloni R.,  Jablonsky F.,  Braga V.F., Leão I.C.,   (2020)

The VISTA Variables in the Vía Láctea infrared variability catalogue (VIVA-I)

Monthly Notices of the Royal Astronomical Society 496 2 1730-1756

Saldias-Fuentes, B., Protopapas, P., & Pichara, K. (2019, May). A Full Probabilistic Model for Yes/No Type Crowdsourcing in Multi-Class Classification. Proceedings of the 2019 SIAM International Conference on Data Mining (SDM) (pp. 756-764). SIAM. (2019)

A Full Probabilistic Model for Yes/No Type Crowdsourcing in Multi-Class Classification

Proceedings of the 2019 SIAM International Conference on Data Mining (SDM) 756-764

Pieringer C., Pichara K., Catelán M., Protopapas P. (2019)

An Algorithm for the Visualization of Relevant Patterns in Astronomical Light Curves

Monthly Notices of the Royal Astronomical Society 484 3 3071–3077

Astudillo J., Protopapas P., Pichara K., Hujise P. (2019)

An Information Theory Approach on Deciding Spectroscopic Follow Ups

Astronomical Journal 159 1 1-14

Aguirre C., Pichara K. and Becker I. (2019)

Deep multi-survey classification of variable stars

Monthly Notices of the Royal Astronomical Society 482 4 5078–5092

Sandoval A., González C., Alarcón R., Pichara K. and Montenegro M. (2018)

Centralized student performance prediction in large courses based on low-cost variables in an institutional context

Internet and Higher Education 37 76-89

Castro N., Protopapas P. and Pichara K. (2018)

Uncertain Classification of Variable Stars: Handling Observational GAPS and Noise

Astronomical Journal 155 1 15pp

Peralta M., Alarcón R., Pichara K., Mery T., Bozo J. and Cano F. (2018)

Understanding Learning Resources Metadata for Primary and Secondary Education

IEEE Transactions on Learning Technologies 11 4 456-467

Valenzuela L. and Pichara K. (2018)

Unsupervised Classification of Variable Stars

Monthly Notices of the Royal Astronomical Society 474 3 3259–3272

Benavente P., Protopapas P. and Pichara K. (2017)

Automatic Survey-invariant Classification of Variable Stars

Astrophysical Journal 845 2 84-92

Mackenzie C., Pichara K. and Protopapas P. (2016)

CLUSTERING-BASED FEATURE LEARNING ON VARIABLE STARS

Astrophysical Journal 820 2 15pp

Pichara K., Protopapas P. and León D. (2016)

META-CLASSIFICATION FOR VARIABLE STARS

Astrophysical Journal 819 1 11pp

Carrasco D., Barrientos L.F., Pichara K., Anguita T., Murphy D.N.A., Gilbank D.G., Gladders M.D., Yee H.K.C., Hsieh B.C. and López S. (2015)

Photometric classification of quasars from RCS-2 using Random Forest

Astronomy & Astrophysics 584 A44 17pp

@proceeding{doi:10.1117/12.2055579, author = {Gregorio, RodrigoSolar, MauricioMardones, DiegoPichara, KarimParada, VictorContreras, Ricardo}, title = {Automatic detectionautomatic classification of structures in astronomical images}, journal = {Proc. SPIE}, volume = {9152}, number = {}, pages = {91522R-91522R-7}, abstract = {The study of the astronomical structures is important to the astronomical community because it can help to identify objects, which can be classified based on their internal structure or their relation to other objects. For this reason, it is developed an automated tool to analyze astronomical images into its components. Firstly, a 2D images is decomposed into different spatial scales based on wavelet transform. Then, it is implemented a detection algorithms to each spatial scale, such as Clumpfind, Gaussclump, or Dendrogram techniques. The goal is to build a new algorithmtool that is available to the community and satisfies the requirements of the next Chilean Virtual Observatory (ChiVO).}, year = {2014}, doi = {10.1117/12.2055579}, URL = { http://dx.doi.org/10.1117/12.2055579}, eprint = {}} (2014)

Automatic detection and automatic classification of structures in astronomical images

Proceedings SPIE 9152

@proceeding{doi:10.1117/12.2056766, author = {Solar, MauricioFariña, WalterMardones, DiegoAntognini, JonathanPichara, KarimNagar, NeilParada, VictorIbsen, JorgeNyman, LarsMarroquin, José}, title = {Chilean virtual observatoryintegration with ALMA}, journal = {Proc. SPIE}, volume = {9150}, number = {}, pages = {91501O-91501O-10}, abstract = {The Virtual Observatories strive to interoperate, exchange datashare services as if it was only one big VO. In this work, the state of the art of VOs will be presentedsummarized in a schematic diagram with the frequency range of the observed data that every VO publishes. Chile, currently a member of the IVOA, collaborates with the Atacama Large Millimeter/submillimeter Array (ALMA), to study and propose ways to adequate the data generated by ALMA to thedifferent data model proposed by the IVOA.}, year = {2014}, doi = {10.1117/12.2056766}, URL = { http://dx.doi.org/10.1117/12.2056766}, eprint = {}} (2014)

Chilean virtual observatory and integration with ALMA

Proceedings SPIE 9150

Pichara K. and Soto A. (2014)

Local feature selection using gaussian process regression

Intelligent Data Analysis

Num I., Pichara K., Protopapas P. and Kim D-W. (2014)

SUPERVISED DETECTION OF ANOMALOUS LIGHTCURVES IN MASSIVE ASTRONOMICAL CATALOGS

Astrophysical Journal 793 1 16pp

Angeloni R., Contreras Ramos R., Catelan M., DéKány I., Gran F., Alonso-García J., Hempel M., Navarrete C., Andrews H., Aparicio A., Beamín J.C., Berger C., Borissova J., Contreras Peña C., Cunial A., de Grijs R., Espinoza N., Eyheramendy S., Ferreira Lop (2014)

The VVV Templates Project Towards an automated classification of VVV light-curves. I. Building a database of stellar variability in the near-infrared

Astronomy & Astrophysics 567 100 11pp

Pichara K. and Protopapas P. (2013)

Automatic classification of variable stars in catalogs with missing data

Astrophysical Journal 777 2

Pichara K., Protopapas P., Kim D.W., Marquette J.B. and Tisserand P. (2012)

An improved quasar detection method in EROS-2 and MACHO LMC data sets. http://dx.doi.org/10.1111/j.1365-2966.2012.22061.x

Monthly Notices of the Royal Astronomical Society 427 2 1284–1297

@INPROCEEDINGS{2011rrls.conf..145C, author = {{Catelan}, M.{Minniti}, D.{Lucas}, P.~W.{Alonso-Garc{'{i}}a}, J.{Angeloni}, R.{Beam{'{i}}n}, J.~C.{Bonatto}, C.{Borissova}, J.{Contreras}, C.{Cross}, N.{D{'e}k{'a}any}, I.{Emerson}, J.~P.{Eyheramendy}, S.{Geisler}, D.{Gonz{'a}lez-Solares}, E.{Helminiak}, K.~G.{Hempel}, M.{Irwin}, M.~J.{Ivanov}, V.~D.{Jord{'a}n}, A.{Kerins}, E.{Kurtev}, R.{Mauro}, F.{Moni Bidin}, C.{Navarrete}, C.{P{'e}rez}, P.{Pichara}, K.{Read}, M.{Rejkuba}, M.{Saito}, R.~K.{Sale}, S.~E.{Toledo}, I.}, title = "{The Vista Variables in the V{'{i}}a L{'a}ctea (VVV) ESO Public Survey: Current StatusFirst Results}", booktitle = {RR Lyrae Stars, Metal-Poor Stars, and the Galaxy}, year = 2011, archivePrefix = "arXiv", eprint = {1105.1119}, primaryClass = "astro-ph.GA", editor = {{McWilliam}, A.}, month = aug, pages = {145}, adsurl = {http://adsabs.harvard.edu/abs/2011rrls.conf..145C}, adsnote = {Provided by the SAO/NASA Astrophysics Data System}} (2012)

The Vista Variables in the Via Lactea (VVV) ESO Public Survey: Current Status and First Results

RR Lyrae Stars, Metal-Poor Stars, and the Galaxy 5 145

Pichara K. and Soto A. (2011)

Active learning and subspace clustering for anomaly detection. http://dx.doi.org/10.3233/IDA-2010-0461

Intelligent Data Analysis 15 2 151-171