Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The development of synoptic sky surveys has led to a massive amount of data for which resources needed for analysis are beyond human capabilities. In order to process this information and to extract all possible knowledge, machine learning techniques become necessary. Here  present a new methodology to automatically discover unknown variable objects in large astronomical catalogs. With the aim of taking full advantage of all information  have about known objects, our method is based on a supervised algorithm. In particular,  train a random forest classifier using known variability classes of objects and obtain votes for each of the objects in the training set.  then model this voting distribution with a Bayesian network and obtain the joint voting distribution among the training objects. Consequently, an unknown object is considered as an outlier insofar it has a low joint probability. By leaving out one of the classes on the training set,  perform a validity test and show that when the random forest classifier attempts to classify unknown light curves (the class left out), it votes with an unusual distribution among the classes. This rare voting is detected by the Bayesian network and expressed as a low jointmore » probability. Our method is suitable for exploring massive data sets given that the training process is performed offline.  tested our algorithm on 20 million light curves from the MACHO catalog and generated a list of anomalous candidates. After analysis,  divided the candidates into two main classes of outliers: artifacts and intrinsic outliers. Artifacts re principally due to air mass variation, seasonal variation, bad calibration, or instrumental errors and re consequently removed from our outlier list and added to the training set. After retraining,  selected about 4000 objects, which  passed to a post-analysis stage by performing a cross-match with all publicly available catalogs. Within these candidates  identified certain known but rare objects such as eclipsing Cepheids, blue variables, cataclysmic variables, and X-ray sources. For some outliers there was no additional information. Among them  identified three unknown variability types and a few individual outliers that will be follod up in order to perform a deeper analysis.« less
['Isadora Nun', 'Karim Pichara', 'Pavlos Protopapas', 'Dae-Won Kim']
I am writing a research paper on a idea Supervised detection of anomalous light-curves in massive astronomical catalogs
[{"from": "human", "value": "I am writing a research paper on a idea Supervised detection of anomalous light-curves in massive astronomical catalogs"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: The development of synoptic sky surveys has led to a massive amount of data for which resources needed for analysis are beyond human capabilities. In order to process this information and to extract all possible knowledge, machine learning techniques become necessary. Here  present a new methodology to automatically discover unknown variable objects in large astronomical catalogs. With the aim of taking full advantage of all information  have about known objects, our method is based on a supervised algorithm. In particular,  train a random forest classifier using known variability classes of objects and obtain votes for each of the objects in the training set.  then model this voting distribution with a Bayesian network and obtain the joint voting distribution among the training objects. Consequently, an unknown object is considered as an outlier insofar it has a low joint probability. By leaving out one of the classes on the training set,  perform a validity test and show that when the random forest classifier attempts to classify unknown light curves (the class left out), it votes with an unusual distribution among the classes. This rare voting is detected by the Bayesian network and expressed as a low jointmore\u00a0\u00bb probability. Our method is suitable for exploring massive data sets given that the training process is performed offline.  tested our algorithm on 20 million light curves from the MACHO catalog and generated a list of anomalous candidates. After analysis,  divided the candidates into two main classes of outliers: artifacts and intrinsic outliers. Artifacts re principally due to air mass variation, seasonal variation, bad calibration, or instrumental errors and re consequently removed from our outlier list and added to the training set. After retraining,  selected about 4000 objects, which  passed to a post-analysis stage by performing a cross-match with all publicly available catalogs. Within these candidates  identified certain known but rare objects such as eclipsing Cepheids, blue variables, cataclysmic variables, and X-ray sources. For some outliers there was no additional information. Among them  identified three unknown variability types and a few individual outliers that will be follod up in order to perform a deeper analysis.\u00ab\u00a0less"}]