Optimum spectral window for imaging of art with optical coherence tomography

Optical Coherence Tomography (OCT) has been shown to have potential for important applications in the field of art conservation and archaeology due to its ability to image subsurface microstructures non-invasively. However, its depth of penetration in painted objects is limited due to the strong scattering properties of artists’ paints. VIS-NIR (400 nm – 2400 nm) reflectance spectra of a wide variety of paints made with historic artists’ pigments have been measured. The best spectral window with which to use optical coherence tomography (OCT) for the imaging of subsurface structure of paintings was found to be around 2.2 μm. The same spectral window would also be most suitable for direct infrared imaging of preparatory sketches under the paint layers. The reflectance spectra from a large sample of chemically verified pigments provide information on the spectral transparency of historic artists’ pigments/paints as well as a reference set of spectra for pigment identification. The results of the paper suggest that broadband sources at ~2 microns are highly desirable for OCT applications in art and potentially material science in general

A Decade of Multiwavelength Observations of the TeV Blazar 1ES 1215+303: Extreme Shift of the Synchrotron Peak Frequency and Long-term Optical-Gamma-Ray Flux Increase

Blazars are known for their variability on a wide range of timescales at all wavelengths. Most studies of TeV gamma-ray blazars focus on short timescales, especially during flares. With a decade of observations from the Fermi-LAT and VERITAS, we present an extensive study of the long-term multiwavelength radio-to-gamma-ray flux-density variability, with the addition of a couple of short-time radio-structure and optical polarization observations of the blazar 1ES 1215+303 (z = 0.130), with a focus on its gamma-ray emission from 100 MeV to 30 TeV. Multiple strong GeV gamma-ray flares, a long-term increase in the gamma-ray and optical flux baseline, and a linear correlation between these two bands are observed over the ten-year period. Typical HBL behaviors are identified in the radio morphology and broadband spectrum of the source. Three stationary features in the innermost jet are resolved by Very Long Baseline Array at 43.1, 22.2, and 15.3 GHz. We employ a two-component synchrotron self-Compton model to describe different flux states of the source, including the epoch during which an extreme shift in energy of the synchrotron peak frequency from infrared to soft X-rays is observed

Periastron Observations of TeV Gamma-Ray Emission from a Binary System with a 50-year Period

We report on observations of the pulsar/Be star binary system PSR J2032+4127/MT91 213 in the energy range between 100 GeV and 20 TeV with the Very Energetic Radiation Imaging Telescope Array and Major Atmospheric Gamma Imaging Cherenkov telescope arrays. The binary orbit has a period of approximately 50 years, with the most recent periastron occurring on 2017 November 13. Our observations span from 18 months prior to periastron to one month after. A new point-like gamma-ray source is detected, coincident with the location of PSR J2032+4127/MT91 213. The gamma-ray light curve and spectrum are well characterized over the periastron passage. The flux is variable over at least an order of magnitude, peaking at periastron, thus providing a firm association of the TeV source with the pulsar/Be star system. Observations prior to periastron show a cutoff in the spectrum at an energy around 0.5 TeV. This result adds a new member to the small population of known TeV binaries, and it identifies only the second source of this class in which the nature and properties of the compact object are firmly established. We compare the gamma-ray results with the light curve measured with the X-ray Telescope on board the Neil Gehrels Swift Observatory and with the predictions of recent theoretical models of the system. We conclude that significant revision of the models is required to explain the details of the emission that we have observed, and we discuss the relationship between the binary system and the overlapping steady extended source, TeV J2032+4130

Observation of the Gamma-Ray Binary HESS J0632+057 with the HESS, MAGIC, and VERITAS Telescopes

The results of gamma-ray observations of the binary system HESS J0632 + 057 collected during 450 hr over 15 yr, between 2004 and 2019, are presented. Data taken with the atmospheric Cherenkov telescopes H.E.S.S., MAGIC, and VERITAS at energies above 350 GeV were used together with observations at X-ray energies obtained with Swift-XRT, Chandra, XMM-Newton, NuSTAR, and Suzaku. Some of these observations were accompanied by measurements of the H alpha emission line. A significant detection of the modulation of the very high-energy gamma-ray fluxes with a period of 316.7 +/- 4.4 days is reported, consistent with the period of 317.3 +/- 0.7 days obtained with a refined analysis of X-ray data. The analysis of data from four orbital cycles with dense observational coverage reveals short-timescale variability, with flux-decay timescales of less than 20 days at very high energies. Flux variations observed over a timescale of several years indicate orbit-to-orbit variability. The analysis confirms the previously reported correlation of X-ray and gamma-ray emission from the system at very high significance, but cannot find any correlation of optical H alpha parameters with fluxes at X-ray or gamma-ray energies in simultaneous observations. The key finding is that the emission of HESS J0632 + 057 in the X-ray and gamma-ray energy bands is highly variable on different timescales. The ratio of gamma-ray to X-ray flux shows the equality or even dominance of the gamma-ray energy range. This wealth of new data is interpreted taking into account the insufficient knowledge of the ephemeris of the system, and discussed in the context of results reported on other gamma-ray binary systems

Sensitivity of the Cherenkov Telescope Array to a dark matter signal from the Galactic centre

We provide an updated assessment of the power of the Cherenkov Telescope Array (CTA) to search for thermally produced dark matter at the TeV scale, via the associated gamma-ray signal from pair-annihilating dark matter particles in the region around the Galactic centre. We find that CTA will open a new window of discovery potential, significantly extending the range of robustly testable models given a standard cuspy profile of the dark matter density distribution. Importantly, even for a cored profile, the projected sensitivity of CTA will be sufficient to probe various well-motivated models of thermally produced dark matter at the TeV scale. This is due to CTA's unprecedented sensitivity, angular and energy resolutions, and the planned observational strategy. The survey of the inner Galaxy will cover a much larger region than corresponding previous observational campaigns with imaging atmospheric Cherenkov telescopes. CTA will map with unprecedented precision the large-scale diffuse emission in high-energy gamma rays, constituting a background for dark matter searches for which we adopt state-of-the-art models based on current data. Throughout our analysis, we use up-to-date event reconstruction Monte Carlo tools developed by the CTA consortium, and pay special attention to quantifying the level of instrumental systematic uncertainties, as well as background template systematic errors, required to probe thermally produced dark matter at these energies

Investigating a Deep Learning Method to Analyze Images from Multiple Gamma-ray Telescopes

Se deposita versión preprint de la ponenciaImaging atmospheric Cherenkov telescope (IACT) arrays record images from air showers initiated by gamma rays entering the atmosphere, allowing astrophysical sources to be observed at very high energies. To maximize IACT sensitivity, gamma-ray showers must be efficiently distinguished from the dominant background of cosmic-ray showers using images from multiple telescopes. A combination of convolutional neural networks (CNNs) with a recurrent neural network (RNN) has been proposed to perform this task. Using CTLearn, an open source Python package using deep learning to analyze data from IACTs, with simulated data from the upcoming Cherenkov Telescope Array (CTA), we implement a CNN-RNN network and find no evidence that sorting telescope images by total amplitude improves background rejection performance.National Science Foundation (NSF)Spanish MINECO/ERDF UEDepto. de Estructura de la Materia, Física Térmica y ElectrónicaFac. de Ciencias FísicasInstituto de Física de Partículas y del Cosmos (IPARCOS)TRUEpu