Faint solar analogs: at the limit of no reddening

The flux distribution of solar analogs is required for calculating the spectral albedo of Solar System bodies such as asteroids and trans-Neptunian objects. Ideally a solar analog should be comparably faint as the target of interest, but only few analogs fainter than V = 9 were identified so far. Only atmospheric parameters equal to solar guarantee a flux distribution equal to solar as well, while only photometric colors equal to solar do not. Reddening is also a factor to consider when selecting faint analog candidates. We implement the methodology for identifying faint analogs at the limit of precision allowed by current spectroscopic surveys. We quantify the precision attainable for the atmospheric parameters effective temperature ($T_{eff}$), metallicity ([Fe/H]), surface gravity (log $g$) when derived from moderate low resolution (R=8000) spectra with S/N $\sim 100$. We calibrated $T_{eff}$ and [Fe/H] as functions of equivalent widths of spectral indices by means of the PCA regression. We derive log $g$, mass, radius, and age from the atmospheric parameters, Gaia parallaxes and evolutionary tracks. We obtained $T_{eff}$/[Fe/H]/log $g$ with precision of 97 K/0.06 dex/0.05 dex. We identify five solar analogs with $V\sim10.5$ (located at $\sim135$ pc): HIP 991, HIP 5811, HIP 69477, HIP 55619 and HIP 61835. Other six stars have $T_{eff}$ close to solar but slightly lower [Fe/H]. Our analogs show no evidence of reddening but for four stars, which present $E(B-V) \geq 0.06$ mag, translating to at least a 200 K decrease in photometric $T_{eff}$.Comment: Paper accepted. Fundamental parameters of the solar analogs are in Table

Perceptual abstraction and attention

This is a report on the preliminary achievements of WP4 of the IM-CleVeR project on abstraction for cumulative learning, in particular directed to: (1) producing algorithms to develop abstraction features under top-down action influence; (2) algorithms for supporting detection of change in motion pictures; (3) developing attention and vergence control on the basis of locally computed rewards; (4) searching abstract representations suitable for the LCAS framework; (5) developing predictors based on information theory to support novelty detection. The report is organized around these 5 tasks that are part of WP4. We provide a synthetic description of the work done for each task by the partners

Novelty detection and learning drives

This document presents Deliverable 5.1 of the IM-CLeVeR (Intrinsically Motivated Cumulative Learning Versatile Robots) EU FP7 project. It represents one of two deliverables from Workpackage 5 (Novelty Detection and Drives for Autonomous Learning)