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Max Baak

Publications and source records attributed to Max Baak.

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Implications of the Pessimistic Lower Limit on the Drake Equation

The observation of life on Earth is generally accepted to be uninformative concerning the probability of life on other Earth-like planets, a belief first formalized by Brandon Carter and based on the selection effect of our existence. In a similar way, the Drake equation is either presented as estimate of the total number of active, communicative, extraterrestrial civilizations in our Galaxy ($n^g_{\rm civ}$), i.e. excluding humanity, or humanity is included in the estimate but judged to be an uninformative data point. Daniel Whitmire has recently challenged the Carter abiogenesis argument, claiming the logic behind it is flawed, as the conditional likelihoods used by Carter in Bayes' theorem are not evaluated prior to the occurrence of the evidence of life on Earth, but posterior. Doing so correctly, the anthropic selection effect is removed and the observation of life on Earth is informative after all. Following this argument, we treat the Drake equation as estimate of all technological civilizations in a statistical counting experiment and include the data point of humanity as informative evidence. This allows one to set a pessimistic lower limit on $n^o_{\rm civ}$ for the observable universe, $n^o_{\rm civ} > 0.051$ at 95\% C.L., or $n^g_{\rm civ} > 8\times10^{-13}$ at 95\% C.L. for the Galaxy. In particular, this excludes models that predict $n^o_{\rm civ}\ll 1$ for the observable universe and refines the allowable parameter space for hypotheses like Rare Earth. Our analysis substantially reduces the portion of the Drake equation parameter space that predicts humanity is alone; when applying the lower limit this study finds $P(n^o_{\rm civ}>1 |\, {\rm humanity}) = 97.6\%$, making solitude in the observable universe a disfavored outcome. For the low-end estimate of $n^o_{\rm civ}\! =\! 1$ we calculate a probability of 42\% for the existence of other communicating civilizations.

stat.OT

Active WeaSuL: Improving Weak Supervision with Active Learning

The availability of labelled data is one of the main limitations in machine learning. We can alleviate this using weak supervision: a framework that uses expert-defined rules $\boldsymbol{\lambda}$ to estimate probabilistic labels $p(y|\boldsymbol{\lambda})$ for the entire data set. These rules, however, are dependent on what experts know about the problem, and hence may be inaccurate or may fail to capture important parts of the problem-space. To mitigate this, we propose Active WeaSuL: an approach that incorporates active learning into weak supervision. In Active WeaSuL, experts do not only define rules, but they also iteratively provide the true label for a small set of points where the weak supervision model is most likely to be mistaken, which are then used to better estimate the probabilistic labels. In this way, the weak labels provide a warm start, which active learning then improves upon. We make two contributions: 1) a modification of the weak supervision loss function, such that the expert-labelled data inform and improve the combination of weak labels; and 2) the maxKL divergence sampling strategy, which determines for which data points expert labelling is most beneficial. Our experiments show that when the budget for labelling data is limited (e.g. $\leq 60$ data points), Active WeaSuL outperforms weak supervision, active learning, and competing strategies, with only a handful of labelled data points. This makes Active WeaSuL ideal for situations where obtaining labelled data is difficult.

cs.LG

Interpolation between multi-dimensional histograms using a new non-linear moment morphing method

A prescription is presented for the interpolation between multi-dimensional distribution templates based on one or multiple model parameters. The technique uses a linear combination of templates, each created using fixed values of the model's parameters and transformed according to a specific procedure, to model a non-linear dependency on model parameters and the dependency between them. By construction the technique scales well with the number of input templates used, which is a useful feature in modern day particle physics, where a large number of templates is often required to model the impact of systematic uncertainties.

physics.data-an

The global electroweak Standard Model fit after the Higgs discovery

We present an update of the global Standard Model (SM) fit to electroweak precision data under the assumption that the new particle discovered at the LHC is the SM Higgs boson. In this scenario all parameters entering the calculations of electroweak precision observalbes are known, allowing, for the first time, to over-constrain the SM at the electroweak scale and assert its validity. Within the SM the W boson mass and the effective weak mixing angle can be accurately predicted from the global fit. The results are compatible with, and exceed in precision, the direct measurements. An updated determination of the S, T and U parameters, which parametrize the oblique vacuum corrections, is given. The obtained values show good consistency with the SM expectation and no direct signs of new physics are seen. We conclude with an outlook to the global electroweak fit for a future e+e- collider.

hep-ph