From regular black holes to horizonless objects: quasi-normal modes, instabilities and spectroscopy

We study gravitational and test-field perturbations for the two possible families of spherically symmetric black-hole mimickers that smoothly interpolate between regular black holes and horizonless compact objects accordingly to the value of a regularization parameter. One family can be described by the Bardeen-like metrics, and the other by the Simpson-Visser metric. We compute the spectrum of quasi-normal modes (QNMs) of these spacetimes enlightening a common misunderstanding regarding this computation present in the recent literature. In both families, we observe long-living modes for values of the regularization parameter corresponding to ultracompact, horizonless configurations. Such modes appear to be associated with the presence of a stable photon sphere and are indicative of potential non-linear instabilities. In general, the QNM spectra of both families display deviations from the standard spectrum of GR singular BHs. In order to address the future detectability of such deviations in the gravitational-wave ringdown signal, we perform a preliminary study, finding that third generation ground-based detectors might be sensible to macroscopic values of the regularization parameter

METAPHOR: Probability density estimation for machine learning based photometric redshifts

We present METAPHOR (Machine-learning Estimation Tool for Accurate PHOtometric Redshifts), a method able to provide a reliable PDF for photometric galaxy redshifts estimated through empirical techniques. METAPHOR is a modular workflow, mainly based on the MLPQNA neural network as internal engine to derive photometric galaxy redshifts, but giving the possibility to easily replace MLPQNA with any other method to predict photo-z's and their PDF. We present here the results about a validation test of the workflow on the galaxies from SDSS-DR9, showing also the universality of the method by replacing MLPQNA with KNN and Random Forest models. The validation test include also a comparison with the PDF's derived from a traditional SED template fitting method (Le Phare).Comment: proceedings of the International Astronomical Union, IAU-325 symposium, Cambridge University pres

Probability density estimation of photometric redshifts based on machine learning

Photometric redshifts (photo-z's) provide an alternative way to estimate the distances of large samples of galaxies and are therefore crucial to a large variety of cosmological problems. Among the various methods proposed over the years, supervised machine learning (ML) methods capable to interpolate the knowledge gained by means of spectroscopical data have proven to be very effective. METAPHOR (Machine-learning Estimation Tool for Accurate PHOtometric Redshifts) is a novel method designed to provide a reliable PDF (Probability density Function) of the error distribution of photometric redshifts predicted by ML methods. The method is implemented as a modular workflow, whose internal engine for photo-z estimation makes use of the MLPQNA neural network (Multi Layer Perceptron with Quasi Newton learning rule), with the possibility to easily replace the specific machine learning model chosen to predict photo-z's. After a short description of the software, we present a summary of results on public galaxy data (Sloan Digital Sky Survey - Data Release 9) and a comparison with a completely different method based on Spectral Energy Distribution (SED) template fitting.Comment: 2016 IEEE Symposium Series on Computational Intelligence, SSCI 2016 784995

Stable Rotating Regular Black Holes

We present a rotating regular black hole whose inner horizon has zero surface gravity for any value of the spin parameter, and is therefore stable against mass inflation. Our metric is built by combining two successful strategies for regularizing singularities, i.e. by replacing the mass parameter with a function of $r$ and by introducing a conformal factor. The mass function controls the properties of the inner horizon, whose displacement away from the Kerr geometry's inner horizon is quantified in terms of a parameter $e$; while the conformal factor regularizes the singularity in a way that is parametrized by the dimensionful quantity $b$. The resulting line element not only avoids the stability issues that are common to regular black hole models endowed with inner horizons, but is also free of problematic properties of the Kerr geometry, such as the existence of closed timelike curves. While the proposed metric has all the phenomenological relevant features of singular rotating black holes -- such as ergospheres, light ring and innermost stable circular orbit -- showing a remarkable similarity to a Kerr black hole in its exterior, it allows nonetheless sizable deviations, especially for large values of the spin parameter $a$. In this sense, the proposed rotating "inner-degenarate" regular black hole solution is not only amenable to further theoretical investigations but most of all can represent a viable geometry to contrast to the Kerr one in future phenomenological tests.Comment: 12 pages; formatting matched to submitted versio

DAMEWARE - Data Mining & Exploration Web Application Resource

Astronomy is undergoing through a methodological revolution triggered by an unprecedented wealth of complex and accurate data. DAMEWARE (DAta Mining & Exploration Web Application and REsource) is a general purpose, Web-based, Virtual Observatory compliant, distributed data mining framework specialized in massive data sets exploration with machine learning methods. We present the DAMEWARE (DAta Mining & Exploration Web Application REsource) which allows the scientific community to perform data mining and exploratory experiments on massive data sets, by using a simple web browser. DAMEWARE offers several tools which can be seen as working environments where to choose data analysis functionalities such as clustering, classification, regression, feature extraction etc., together with models and algorithms.Comment: User Manual of the DAMEWARE Web Application, 51 page

Statistical analysis of probability density functions for photometric redshifts through the KiDS-ESO-DR3 galaxies

Despite the high accuracy of photometric redshifts (zphot) derived using Machine Learning (ML) methods, the quantification of errors through reliable and accurate Probability Density Functions (PDFs) is still an open problem. First, because it is difficult to accurately assess the contribution from different sources of errors, namely internal to the method itself and from the photometric features defining the available parameter space. Second, because the problem of defining a robust statistical method, always able to quantify and qualify the PDF estimation validity, is still an open issue. We present a comparison among PDFs obtained using three different methods on the same data set: two ML techniques, METAPHOR (Machine-learning Estimation Tool for Accurate PHOtometric Redshifts) and ANNz2, plus the spectral energy distribution template fitting method, BPZ. The photometric data were extracted from the KiDS (Kilo Degree Survey) ESO Data Release 3, while the spectroscopy was obtained from the GAMA (Galaxy and Mass Assembly) Data Release 2. The statistical evaluation of both individual and stacked PDFs was done through quantitative and qualitative estimators, including a dummy PDF, useful to verify whether different statistical estimators can correctly assess PDF quality. We conclude that, in order to quantify the reliability and accuracy of any zphot PDF method, a combined set of statistical estimators is required.Comment: Accepted for publication by MNRAS, 20 pages, 14 figure

METAPHOR: a machine-learning-based method for the probability density estimation of photometric redshifts

A variety of fundamental astrophysical science topics require the determination of very accurate photometric redshifts (photo-z). A wide plethora of methods have been developed, based either on template models fitting or on empirical explorations of the photometric parameter space. Machine-learning-based techniques are not explicitly dependent on the physical priors and able to produce accurate photo-z estimations within the photometric ranges derived from the spectroscopic training set. These estimates, however, are not easy to characterize in terms of a photo-z probability density function (PDF), due to the fact that the analytical relation mapping the photometric parameters on to the redshift space is virtually unknown. We present METAPHOR (Machine-learning Estimation Tool for Accurate PHOtometric Redshifts), a method designed to provide a reliable PDF of the error distribution for empirical techniques. The method is implemented as a modular workflow, whose internal engine for photo-z estimation makes use of the MLPQNA neural network (Multi Layer Perceptron with Quasi Newton learning rule), with the possibility to easily replace the specific machine-learning model chosen to predict photo-z. We present a summary of results on SDSS-DR9 galaxy data, used also to perform a direct comparison with PDFs obtained by the LE PHARE spectral energy distribution template fitting. We show that METAPHOR is capable to estimate the precision and reliability of photometric redshifts obtained with three different self-adaptive techniques, I.e. MLPQNA, Random Forest and the standard K-Nearest Neighbors models

Oxycodone/Acetaminophen: The Tailoring Combination Treatment for Specific Clinical Profile of Opioid Well-Responsive Cancer Pain

Background: International guidelines recommend moderate-to-severe cancer pain to be treated with strong opioids. However, pain management remains an unsolved matter, at least in the demanding oncology and palliative care setting. Although cancer pain consists of multiple components, which interact in complex ways where combination therapy can better intercept multiple pain characteristics, few studies have used a non-opioid/opioid association to exploit possible synergistic actions. Even the efforts of a recent approach emphasizing appropriate pain assessment and accurate classification to obtain personalized pain management have not produced a satisfactory analgesic strategy. Objective: This analysis was intended to evaluate the effectiveness of the immediate release fixed combination of oxycodone/acetaminophen (OxyIR/Par) for the treatment of moderate-to-severe intensity background pain used alone or in combination with other strong opioids in cancer patients with breakthrough cancer pain (BTcP). This is a secondary analysis of a wider observational, prospective, multicenter study [Italian Oncologic Pain multiSetting Multicentric Survey (IOPS-MS)] performed on 179 patients treated with opioids for cancer pain who received the fixed combination of oxycodone/acetaminophen (OxyIR/Par) for the treatment of background pain (BGP). Results: Cancer patients with breakthrough cancer pain and controlled BGP (Background Pain) were classified according to the presence of analgesic therapy with tablets of fixed combination OxyIR/Par alone (group A, n=120) or tablets of fixed combination OxyIR/Par combined with other strong opioids (group B, n=59). Clinical features of group A were different to group B: higher mean Karnofsky Performance Status Index 70.3% (95% CI=67.2-73.5; median=70, CI=60-80) vs 58.3 (95% CI=53.4-63.2; median=50, CI=45-70) (P<0.001), and mainly group A patients were treated in an ambulatory setting (55.0% group A vs 33.9% group B) (p<0.001). Both groups had managed BGP with similar mean dosages (group A: 12.0, CI=10.5-13.4; group B: 13.1, CI=11.0-15.1) and frequencies of OxyIR/Par alone for group A and in association to other opioids for group B, but Breakthrough cancer Pain (BTcP) exhibited different characteristics in the two groups, showing a lower mean intensity numerical rating scale (NRS) of 7.5 (95% CI=7.2-7.7; median=7, CI=7-8 group A) vs 7.9 (95% CI=7.6, 8.2; median= 8, CI=7-9 group B) (P=0.04) and a higher percentage of patients had a faster onset, defined as the maximum intensity reached in less than 10 minutes, 81.7% (N=98) in group A vs 59.3% (n=35) in group B (P=0.002). Conclusion: This is the first analysis about the efficacy of an immediate-release fixed combination of OxyIR/Par in the real world for moderate-to-severe background cancer pain and breakthrough cancer pain. The oral fixed combination OxyIR/Par provided an adequate level of analgesia for moderate-severe background cancer pain, in a different cohort of cancer patients with different performance status, both in ambulatory and palliative settings. The low dosage of fixed combination OxyIR/Par was effective alone or in association with other opioids