Photometric and spectroscopic analysis of LBV candidate J004341.84+411112.0 in M31

We study Luminous Blue Variable (LBV) candidate J004341.84+411112.0 in the Andromeda galaxy. We present optical spectra of the object obtained with the 6-m telescope of SAO RAS. The candidate shows typical LBV features in its spectra: broad and strong hydrogen lines and the HeI lines with P Cigni profiles. Its remarkable spectral resemblance to the well known LBV P Cygni suggests a common nature of the objects and supports LBV classification of J004341.84+411112.0. We estimate the temperature, reddening, radius and luminosity of the star using its spectral energy distribution. Obtained bolometric luminosity of the candidate (M bol = -10.40±0.12 mag) is quite similar to those of known LBV stars in the Andromeda galaxy. We analysed ten year light curve of the object in R filter. The candidate demonstrates photometric variations of the order of 0.4 mag, with an overall brightness increasing trend ΔR > 0.1 mag. Therewith, the corresponding colour variations of the object are fully consistent with LBV behavior when a star become cooler and brighter in the optical spectral range with a nearly constant bolometric luminosity. LBV-type variability of the object, similarity of its spectrum and estimated luminosity to those of known LBVs allows us to classify J004341.84+411112.0 as a LBV.

Tangible Cultural Analytics: The Adoption of Recommender Systems Within Cultural Research

The prospect of implementing recommender systems within the context of cultural research has not been explored nearly as much compared to implementation in e-commerce websites and applications. Recommender systems allow for users to be shown new objects either based upon object similarity or based upon what the algorithm thinks the user will like – which can be derived from user feedback and comparing the user to other similar users. This paper discusses how a recommender system could benefit an augmented reality application that enables 3D viewing of artifacts – as part of the Tangible Cultural Analytics (TCA) project at Ryerson University’s Synaesthetic Lab. This paper outlines four recommender systems: 1) content-based filtering, 2) collaborative filtering, 3) cluster models 4) search based models, and 5) hybrid models; discussing the pros and cons to each. Ultimately, a content-based model without the user profile aspect was chosen for this stage in the prototype. This model showed us just how much potential these recommender systems have when helping cultural researchers uncover new relationships and pieces of history through the study and comparison of artifacts.

Tangible Cultural Analytics: The Adoption of Recommender Systems Within Cultural Research

The prospect of implementing recommender systems within the context of cultural research has not been explored nearly as much compared to implementation in e-commerce websites and applications. Recommender systems allow for users to be shown new objects either based upon object similarity or based upon what the algorithm thinks the user will like – which can be derived from user feedback and comparing the user to other similar users. This paper discusses how a recommender system could benefit an augmented reality application that enables 3D viewing of artifacts – as part of the Tangible Cultural Analytics (TCA) project at Ryerson University’s Synaesthetic Lab. This paper outlines four recommender systems: 1) content-based filtering, 2) collaborative filtering, 3) cluster models 4) search based models, and 5) hybrid models; discussing the pros and cons to each. Ultimately, a content-based model without the user profile aspect was chosen for this stage in the prototype. This model showed us just how much potential these recommender systems have when helping cultural researchers uncover new relationships and pieces of history through the study and comparison of artifacts.