Search for: All records

When strong gravitational lenses are to be used as an astrophysical or cosmological probe, models of their mass distributions are often needed. We present a new, time-efficient automation code for the uniform modeling of strongly lensed quasars withGLEE, a lens-modeling software for multiband data. By using the observed positions of the lensed quasars and the spatially extended surface brightness distribution of the host galaxy of the lensed quasar, we obtain a model of the mass distribution of the lens galaxy. We applied this uniform modeling pipeline to a sample of nine strongly lensed quasars for which images were obtained with the Wide Field Camera 3 of theHubbleSpace Telescope. The models show well-reconstructed light components and a good alignment between mass and light centroids in most cases. We find that the automated modeling code significantly reduces the input time during the modeling process for the user. The time for preparing the required input files is reduced by a factor of 3 from ~3 h to about one hour. The active input time during the modeling process for the user is reduced by a factor of 10 from ~ 10 h to about one hour per lens system. This automated uniform modeling pipeline can efficiently produce uniform models of extensive lens-system samples that can be used for further cosmological analysis. A blind test that compared our results with those of an independent automated modeling pipeline based on the modeling softwareLenstronomyrevealed important lessons. Quantities such as Einstein radius, astrometry, mass flattening, and position angle are generally robustly determined. Other quantities, such as the radial slope of the mass density profile and predicted time delays, depend crucially on the quality of the data and on the accuracy with which the point spread function is reconstructed. Better data and/or a more detailed analysis are necessary to elevate our automated models to cosmography grade. Nevertheless, our pipeline enables the quick selection of lenses for follow-up and further modeling, which significantly speeds up the construction of cosmography-grade models. This important step forward will help us to take advantage of the increase in the number of lenses that is expected in the coming decade, which is an increase of several orders of magnitude.

 

ABSTRACT We present the first intensive continuum reverberation mapping study of the high accretion-rate Seyfert galaxy Mrk 110. The source was monitored almost daily for more than 200 d with the Swift X-ray and ultraviolet (UV)/optical telescopes, supported by ground-based observations from Las Cumbres Observatory, the Liverpool Telescope, and the Zowada Observatory, thus extending the wavelength coverage to 9100 Å. Mrk 110 was found to be significantly variable at all wavebands. Analysis of the intraband lags reveals two different behaviours, depending on the time-scale. On time-scales shorter than 10 d the lags, relative to the shortest UV waveband (∼1928 Å), increase with increasing wavelength up to a maximum of ∼2 d lag for the longest waveband (∼9100 Å), consistent with the expectation from disc reverberation. On longer time-scales, however, the g-band lags the Swift BAT hard X-rays by ∼10 d, with the z-band lagging the g-band by a similar amount, which cannot be explained in terms of simple reprocessing from the accretion disc. We interpret this result as an interplay between the emission from the accretion disc and diffuse continuum radiation from the broad-line region. 

The origin of high-energy galactic cosmic rays is yet to be understood, but some galactic cosmic-ray accelerators can accelerate cosmic rays up to PeV energies. The high-energy cosmic rays are expected to interact with the surrounding material or radiation, resulting in the production of gamma-rays and neutrinos. To optimize for the detection of such associated production of gamma-rays and neutrinos for a given source morphology and spectrum, a multimessenger analysis that combines gamma-rays and neutrinos is required. In this study, we use the Multi-Mission Maximum Likelihood framework with IceCube Maximum Likelihood Analysis software and HAWC Accelerated Likelihood to search for a correlation between 22 known gamma-ray sources from the third HAWC gamma-ray catalog and 14 yr of IceCube track-like data. No significant neutrino emission from the direction of the HAWC sources was found. We report the best-fit gamma-ray model and 90% CL neutrino flux limit from the 22 sources. From the neutrino flux limit, we conclude that, for five of the sources, the gamma-ray emission observed by HAWC cannot be produced purely from hadronic interactions. We report the limit for the fraction of gamma-rays produced by hadronic interactions for these five sources.

 

Gravitational time delays provide a powerful one-step measurement of H0, independent of all other probes. One key ingredient in time-delay cosmography are high-accuracy lens models. Those are currently expensive to obtain, both, in terms of computing and investigator time (105–106 CPU hours and ∼0.5–1 yr, respectively). Major improvements in modelling speed are therefore necessary to exploit the large number of lenses that are forecast to be discovered over the current decade. In order to bypass this roadblock, we develop an automated modelling pipeline and apply it to a sample of 31 lens systems, observed by the Hubble Space Telescope in multiple bands. Our automated pipeline can derive models for 30/31 lenses with few hours of human time and <100 CPU hours of computing time for a typical system. For each lens, we provide measurements of key parameters and predictions of magnification as well as time delays for the multiple images. We characterize the cosmography-readiness of our models using the stability of differences in the Fermat potential (proportional to time delay) with respect to modelling choices. We find that for 10/30 lenses, our models are cosmography or nearly cosmography grade (<3 per cent and 3–5 per cent variations). For 6/30 lenses, the models are close to cosmography grade (5–10 per cent). These results utilize informative priors and will need to be confirmed by further analysis. However, they are also likely to improve by extending the pipeline modelling sequence and options. In conclusion, we show that uniform cosmography grade modelling of large strong lens samples is within reach.

 

Active galactic nuclei (AGN) are prime candidate sources of the high-energy, astrophysical neutrinos detected by IceCube. This is demonstrated by the real-time multimessenger detection of the blazar TXS 0506+056 and the recent evidence of neutrino emission from NGC 1068 from a separate time-averaged study. However, the production mechanism of the astrophysical neutrinos in AGN is not well established, which can be resolved via correlation studies with photon observations. For neutrinos produced due to photohadronic interactions in AGN, in addition to a correlation of neutrinos with high-energy photons, there would also be a correlation of neutrinos with photons emitted at radio wavelengths. In this work, we perform an in-depth stacking study of the correlation between 15 GHz radio observations of AGN reported in the MOJAVE XV catalog, and 10 yr of neutrino data from IceCube. We also use a time-dependent approach, which improves the statistical power of the stacking analysis. No significant correlation was found for both analyses, and upper limits are reported. When compared to the IceCube diffuse flux, at 100 TeV and for a spectral index of 2.5, the upper limits derived are ∼3% and ∼9% for the time-averaged and time-dependent cases, respectively.

 

Name that Neutrinois a citizen science project where volunteers aid in classification of events for the IceCube Neutrino Observatory, an immense particle detector at the geographic South Pole. From March 2023 to September 2023, volunteers did classifications of videos produced from simulated data of both neutrino signal and background interactions.Name that Neutrinoobtained more than 128,000 classifications by over 1800 registered volunteers that were compared to results obtained by a deep neural network machine-learning algorithm. Possible improvements for bothName that Neutrinoand the deep neural network are discussed.