Purification, crystallization and preliminary X-ray diffraction studies of N-acetylglucosamine-phosphate mutase from Candida albicans

Preliminary X-ray diffraction studies on N-acetylglucosamine-phosphate mutase from C. albicans are reported

Purification, crystallization and preliminary X-ray diffraction studies of UDP-N-acetylglucosamine pyrophosphorylase from Candida albicans

UDP-N-acetylglucosamine pyrophosphorylase was purified and crystallized and X-ray diffraction data were collected to 2.3 Å resolution

Chasing Gravitational Waves with the Chereknov Telescope Array

Presented at the 38th International Cosmic Ray Conference (ICRC 2023), 2023 (arXiv:2309.08219)2310.07413International audienceThe detection of gravitational waves from a binary neutron star merger by Advanced LIGO and Advanced Virgo (GW170817), along with the discovery of the electromagnetic counterparts of this gravitational wave event, ushered in a new era of multimessenger astronomy, providing the first direct evidence that BNS mergers are progenitors of short gamma-ray bursts (GRBs). Such events may also produce very-high-energy (VHE, > 100GeV) photons which have yet to be detected in coincidence with a gravitational wave signal. The Cherenkov Telescope Array (CTA) is a next-generation VHE observatory which aims to be indispensable in this search, with an unparalleled sensitivity and ability to slew anywhere on the sky within a few tens of seconds. New observing modes and follow-up strategies are being developed for CTA to rapidly cover localization areas of gravitational wave events that are typically larger than the CTA field of view. This work will evaluate and provide estimations on the expected number of of gravitational wave events that will be observable with CTA, considering both on- and off-axis emission. In addition, we will present and discuss the prospects of potential follow-up strategies with CTA

Sensitivity of the Cherenkov Telescope Array to the gamma-ray emission from neutrino sources detected by IceCube

Gamma-ray observations of the astrophysical neutrino sources are fundamentally important for understanding the underlying neutrino production mechanism. We investigate the Cherenkov Telescope Array (CTA) ability to detect the very-high-energy (VHE) gamma-ray counterparts to the neutrino-emitting Active Galaxies. The CTA performance under different configurations and array layouts is computed based on the neutrino and gamma-ray simulations of steady and transient types of sources, assuming that the neutrino events are detected with the IceCube neutrino telescope. The CTA detection probability is calculated for both CTA sites taking into account the visibility constraints. We find that, under optimal observing conditions, CTA could observe the VHE gamma-ray emission from at least 3 neutrino events per year

Performance of a proposed event-type based analysis for the Cherenkov Telescope Array

The Cherenkov Telescope Array (CTA) will be the next-generation observatory in the field of very-high-energy (20 GeV to 300 TeV) gamma-ray astroparticle physics. Classically, data analysis in the field maximizes sensitivity by applying quality cuts on the data acquired. These cuts, optimized using Monte Carlo simulations, select higher quality events from the initial dataset. Subsequent steps of the analysis typically use the surviving events to calculate one set of instrument response functions (IRFs). An alternative approach is the use of event types, as implemented in experiments such as the Fermi-LAT. In this approach, events are divided into sub-samples based on their reconstruction quality, and a set of IRFs is calculated for each sub-sample. The sub-samples are then combined in a joint analysis, treating them as independent observations. This leads to an improvement in performance parameters such as sensitivity, angular and energy resolution. Data loss is reduced since lower quality events are included in the analysis as well, rather than discarded. In this study, machine learning methods will be used to classify events according to their expected angular reconstruction quality. We will report the impact on CTA high-level performance when applying such an event-type classification, compared to the classical procedure