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Register a new userHow can we distinguish transient pulsars from SETI beacons?
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James Benford
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How would observers differentiate Beacons from pulsars or other exotic sources, in light of likely Beacon observables? Bandwidth, pulse width and frequency may be distinguishing features. Such transients could be evidence of civilizations slightly higher than ourselves on the Kardashev scale.
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Possible inaccuracies in the determination of periods from short-term time series caused by disregard of the real course of light curves and instrumental trends are documented on the example of the period analysis of simulated TESS-like light curve by notorious Lomb-Scargle method.
What makes a task relatively more or less difficult for a machine compared to a human? Much AI/ML research has focused on expanding the range of tasks that machines can do, with a focus on whether machines can beat humans. Allowing for differences in scale, we can seek interesting (anomalous) pairs of tasks T, T. We define interesting in this way: The harder to learn relation is reversed when comparing human intelligence (HI) to AI. While humans seems to be able to understand problems by formulating rules, ML using neural networks does not rely on constructing rules. We discuss a novel approach where the challenge is to perform well under rules that have been created by human beings. We suggest that this provides a rigorous and precise pathway for understanding the difference between the two kinds of learning. Specifically, we suggest a large and extensible class of learning tasks, formulated as learning under rules. With these tasks, both the AI and HI will be studied with rigor and precision. The immediate goal is to find interesting groundtruth rule pairs. In the long term, the goal will be to understand, in a generalizable way, what distinguishes interesting pairs from ordinary pairs, and to define saliency behind interesting pairs. This may open new ways of thinking about AI, and provide unexpected insights into human learning.
One of the crucial windows for distinguishing astrophysical black holes from primordial black holes is through the redshift evolution of their respective merger rates. The low redshift population of black holes of astrophysical origin is expected to follow the star formation rate. The corresponding peak in their merger rate peaks at a redshift smaller than that of the star formation rate peak ($z_p approx 2$), depending on the time delay between the formation and mergers of black holes. Black holes of primordial origin are going to be present before the formation of the stars, and the merger rate of these sources at high redshift is going to be large. We propose a joint estimation of a hybrid merger rate from the stochastic gravitational wave background, which can use the cosmic history of merger rates to distinguish between the two populations of black holes. Using the latest bounds on the amplitude of the stochastic gravitational wave background amplitude from the third observation run of LIGO/Virgo, we obtain weak constraints at $68%$ C.L. on the primordial black hole merger rate index $2.56_{-1.76}^{+1.64}$ and astrophysical black hole time delay $6.7_{-4.74}^{+4.22}$ Gyr. We should be able to distinguish between the different populations of black holes with the forthcoming O5 and A+ detector sensitivities.
The creation of a 3D map of the bulge using RRLyrae (RRL) is one of the main goals of the VVV(X) surveys. The overwhelming number of sources under analysis request the use of automatic procedures. In this context, previous works introduced the use of Machine Learning (ML) methods for the variable star classification. Our goal is the development and analysis of an automatic procedure, based on ML, for the identification of RRLs in the VVV Survey. This procedure will be use to generate reliable catalogs integrated over several tiles in the survey. After the reconstruction of light-curves, we extract a set of period and intensity-based features. We use for the first time a new subset of pseudo color features. We discuss all the appropriate steps needed to define our automatic pipeline: selection of quality measures; sampling procedures; classifier setup and model selection. As final result, we construct an ensemble classifier with an average Recall of 0.48 and average Precision of 0.86 over 15 tiles. We also make available our processed datasets and a catalog of candidate RRLs. Perhaps most interestingly, from a classification perspective based on photometric broad-band data, is that our results indicate that Color is an informative feature type of the RRL that should be considered for automatic classification methods via ML. We also argue that Recall and Precision in both tables and curves are high quality metrics for this highly imbalanced problem. Furthermore, we show for our VVV data-set that to have good estimates it is important to use the original distribution more than reduced samples with an artificial balance. Finally, we show that the use of ensemble classifiers helps resolve the crucial model selection step, and that most errors in the identification of RRLs are related to low quality observations of some sources or to the difficulty to resolve the RRL-C type given the date.
We present a new analysis on how to distinguish between isotropic and anisotropic cosmological models based on tracking the angular displacements of a large number of distant quasars over an extended period of time, and then performing a multipole-vector decomposition of the resulting displacement maps. We find that while the GAIA mission operating at its nominal specifications does not have sufficient angular resolution to resolve anisotropic universes from isotropic ones using this method within a reasonable timespan of ten years, a next-generation GAIA-like survey with a resolution ten times better should be equal to the task. Distinguishing between different anisotropic models is however more demanding. Keeping the observational timespan to ten years, we find that the angular resolution of the survey will need to be of order 0.1 micro-arcsecond in order for certain rotating anisotropic models to produce a detectable signature that is also unique to models of this class. However, should such a detection become possible, it would immediately allow us to rule out large local void models.
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