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

Increase of dissolved inorganic carbon and decrease in pH in near-surface waters in the Mediterranean Sea during the past two decades

Two 3-year time series of hourly measurements of the fugacity of CO2 (fCO2) in the upper 10 m of the surface layer of the northwestern Mediterranean Sea have been recorded by CARIOCA sensors almost two decades apart, in 1995–1997 and 2013–2015. By combining them with the alkalinity derived from measured temperature and salinity, we calculate changes in pH and dissolved inorganic carbon (DIC). DIC increased in surface seawater by ∼25 µmol kg−1 and fCO2 by 40 µatm, whereas seawater pH decreased by ∼0.04 (0.0022 yr−1). The DIC increase is about 15 % larger than expected from the equilibrium with atmospheric CO2. This could result from natural variability, e.g. the increase between the two periods in the frequency and intensity of winter convection events. Likewise, it could be the signature of the contribution of the Atlantic Ocean as a source of anthropogenic carbon to the Mediterranean Sea through the Strait of Gibraltar. We then estimate that the part of DIC accumulated over the last 18 years represents ∼30 % of the total inventory of anthropogenic carbon in the Mediterranean Sea

Guselkumab for treatment of moderate-to-severe plaque psoriasis: real-life effectiveness and drug-survival for up to 148 weeks

Background: Real-world data are useful to guide the management of psoriasis. Here, we present data on the effectiveness and survival of guselkumab in moderate-to-severe chronic plaque psoriasis for up to 148 weeks. Research design and methods: Cross-sectional study of 122 patients receiving guselkumab (100 mg at weeks 0 and 4, and then every 8 weeks thereafter) for>12 weeks, from November 2018 to April 2022. Main outcome measures: Clinical features and drug survival were analyzed up to 148 weeks. Results: Obese patients (32.8%) and those receiving prior biologics (64.8%) were included. Guselkumab treatment was associated with a rapid decrease in PASI, from 16.2 to 3.2 at week 12, and long-term improvements in all subgroups (97.6%, 82.9%, and 63.4% of patients, respectively, achieved PASI 75, 90, and 100 after 148 weeks). More non-obese than obese patients achieved PASI 100 at week 148 (86.4% vs 38.9%), as did bio-naïve vs bio-experienced patients (86.7% vs 50.0%). Previous biologic therapy was a negative prognostic factor for achieving PASI 100 over the long-term by multivariate analysis (p = 0.005). Overall, 96% of patients were on treatment after 2 years. Conclusions: Real-world data confirm the long-term effectiveness of guselkumab in patients with psoriasis

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

Photometric redshifts for the Kilo-Degree Survey. Machine-learning analysis with artificial neural networks

We present a machine-learning photometric redshift analysis of the Kilo-Degree Survey Data Release 3, using two neural-network based techniques: ANNz2 and MLPQNA. Despite limited coverage of spectroscopic training sets, these ML codes provide photo-zs of quality comparable to, if not better than, those from the BPZ code, at least up to zphot<0.9 and r<23.5. At the bright end of r<20, where very complete spectroscopic data overlapping with KiDS are available, the performance of the ML photo-zs clearly surpasses that of BPZ, currently the primary photo-z method for KiDS. Using the Galaxy And Mass Assembly (GAMA) spectroscopic survey as calibration, we furthermore study how photo-zs improve for bright sources when photometric parameters additional to magnitudes are included in the photo-z derivation, as well as when VIKING and WISE infrared bands are added. While the fiducial four-band ugri setup gives a photo-z bias $\delta z=-2e-4$ and scatter $\sigma_z<0.022$ at mean z = 0.23, combining magnitudes, colours, and galaxy sizes reduces the scatter by ~7% and the bias by an order of magnitude. Once the ugri and IR magnitudes are joined into 12-band photometry spanning up to 12 $\mu$, the scatter decreases by more than 10% over the fiducial case. Finally, using the 12 bands together with optical colours and linear sizes gives $\delta z<4e-5$ and $\sigma_z<0.019$. This paper also serves as a reference for two public photo-z catalogues accompanying KiDS DR3, both obtained using the ANNz2 code. The first one, of general purpose, includes all the 39 million KiDS sources with four-band ugri measurements in DR3. The second dataset, optimized for low-redshift studies such as galaxy-galaxy lensing, is limited to r<20, and provides photo-zs of much better quality than in the full-depth case thanks to incorporating optical magnitudes, colours, and sizes in the GAMA-calibrated photo-z derivation.Comment: A&A, in press. Data available from the KiDS website http://kids.strw.leidenuniv.nl/DR3/ml-photoz.php#annz

Field Intercomparison of Radiometer Measurements for Ocean Colour Validation

A field intercomparison was conducted at the Acqua Alta Oceanographic Tower (AAOT) in the northern Adriatic Sea, from 9 to 19 July 2018 to assess differences in the accuracy of in- and above-water radiometer measurements used for the validation of ocean colour products. Ten measurement systems were compared. Prior to the intercomparison, the absolute radiometric calibration of all sensors was carried out using the same standards and methods at the same reference laboratory. Measurements were performed under clear sky conditions, relatively low sun zenith angles, moderately low sea state and on the same deployment platform and frame (except in-water systems). The weighted average of five above-water measurements was used as baseline reference for comparisons. For downwelling irradiance (), there was generally good agreement between sensors with differences of <6% for most of the sensors over the spectral range 400 nm–665 nm. One sensor exhibited a systematic bias, of up to 11%, due to poor cosine response. For sky radiance () the spectrally averaged difference between optical systems was <2.5% with a root mean square error (RMS) <0.01 mWm−2 nm−1 sr−1. For total above-water upwelling radiance (), the difference was <3.5% with an RMS <0.009 mWm−2 nm−1 sr−1. For remote-sensing reflectance (), the differences between above-water TriOS RAMSES were <3.5% and <2.5% at 443 and 560 nm, respectively, and were <7.5% for some systems at 665 nm. Seabird HyperSAS sensors were on average within 3.5% at 443 nm, 1% at 560 nm, and 3% at 665 nm. The differences between the weighted mean of the above-water and in-water systems was <15.8% across visible bands. A sensitivity analysis showed that accounted for the largest fraction of the variance in , which suggests that minimizing the errors arising from this measurement is the most important variable in reducing the inter-group differences in . The differences may also be due, in part, to using five of the above-water systems as a reference. To avoid this, in situ normalized water-leaving radiance () was therefore compared to AERONET-OC SeaPRiSM as an alternative reference measurement. For the TriOS-RAMSES and Seabird-Hyperspectral Surface Acquisition System (HyperSAS) sensors the differences were similar across the visible spectra with 4.7% and 4.9%, respectively. The difference between SeaPRiSM and two in-water systems at blue, green and red bands was 11.8%. This was partly due to temporal and spatial differences in sampling between the in-water and above-water systems and possibly due to uncertainties in instrument self-shading for one of the in-water measurements

A novel near real-time quality-control procedure for radiometric profiles measured by Bio-Argo floats: protocols and performances

An array of Bio-Argo floats equipped with radiometric sensors has been recently deployed in various open ocean areas representative of the diversity of trophic and bio-optical conditions prevailing in the so-called Case 1 waters. Around solar noon and almost everyday, each float acquires 0-250 m vertical profiles of Photosynthetically Available Radiation and downward irradiance at three wavelengths (380, 412 and 490 nm). Up until now, more than 6500 profiles for each radiometric channel have been acquired. As these radiometric data are collected out of operator’s control and regardless of meteorological conditions, specific and automatic data processing protocols have to be developed. Here, we present a data quality-control procedure aimed at verifying profile shapes and providing near real-time data distribution. This procedure is specifically developed to: 1) identify main issues of measurements (i.e. dark signal, atmospheric clouds, spikes and wave-focusing occurrences); 2) validate the final data with a hierarchy of tests to ensure a scientific utilization. The procedure, adapted to each of the four radiometric channels, is designed to flag each profile in a way compliant with the data management procedure used by the Argo program. Main perturbations in the light field are identified by the new protocols with good performances over the whole dataset. This highlights its potential applicability at the global scale. Finally, the comparison with modeled surface irradiances allows assessing the accuracy of quality-controlled measured irradiance values and identifying any possible evolution over the float lifetime due to biofouling and instrumental drift

Measles outbreak on a cruise ship in the western Mediterranean, February 2014, preliminary report

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A cooperative approach among methods for photometric redshifts estimation: an application to KiDS data

Photometric redshifts (photo-z) are fundamental in galaxy surveys to address different topics, from gravitational lensing and dark matter distribution to galaxy evolution. The Kilo Degree Survey (KiDS), I.e. the European Southern Observatory (ESO) public survey on the VLT Survey Telescope (VST), provides the unprecedented opportunity to exploit a large galaxy data set with an exceptional image quality and depth in the optical wavebands. Using a KiDS subset of about 25000 galaxies with measured spectroscopic redshifts, we have derived photo-z using (I) three different empirical methods based on supervised machine learning; (II) the Bayesian photometric redshift model (or BPZ); and (III) a classical spectral energy distribution (SED) template fitting procedure (LE PHARE). We confirm that, in the regions of the photometric parameter space properly sampled by the spectroscopic templates, machine learning methods provide better redshift estimates, with a lower scatter and a smaller fraction of outliers. SED fitting techniques, however, provide useful information on the galaxy spectral type, which can be effectively used to constrain systematic errors and to better characterize potential catastrophic outliers. Such classification is then used to specialize the training of regression machine learning models, by demonstrating that a hybrid approach, involving SED fitting and machine learning in a single collaborative framework, can be effectively used to improve the accuracy of photo-z estimates