Constraining Dark Matter Annihilation with Fermi-LAT Observations of Ultra-Faint Compact Stellar Systems

Recent results from numerical simulations and models of galaxy formation suggest that recently discovered ultra-faint compact stellar systems (UFCSs) in the halo of the Milky Way (MW) may be some of the smallest and faintest galaxies. If this is the case, these systems would be attractive targets for indirect searches of weakly interacting massive particle (WIMP) dark matter (DM) annihilation due to their relative proximity and high expected DM content. In this study, we analyze 14.3 years of gamma-ray data collected by the Fermi-LAT coincident with 26 UFCSs. No significant excess gamma-ray emission is detected, and we present gamma-ray flux upper limits for these systems. Assuming that the UFCSs are dark-matter-dominated galaxies consistent with being among the faintest and least massive MW dwarf spheroidal (dSphs) satellite galaxies, we derive the projected sensitivity for a dark matter annihilation signal. We find that observations of UFCSs have the potential to yield some of the most powerful constraints on DM annihilation, with sensitivity comparable to observations of known dSphs and the Galactic center. This result emphasizes the importance of precise kinematic studies of UFCSs to empirically determine their DM content.Comment: 11 pages, 4 figures, 1 table, submitted to ApJ

Event reconstruction using pattern spectra and convolutional neural networks for the Cherenkov Telescope Array

The Cherenkov Telescope Array (CTA) is the future observatory for ground-based imaging atmospheric Cherenkov telescopes. Each telescope will provide a snapshot of gamma-ray induced particle showers by capturing the induced Cherenkov emission at ground level. The simulation of such events provides camera images that can be used as training data for convolutional neural networks (CNNs) to differentiate signals from background events and to determine the energy of the initial gamma-ray events. Pattern spectra are commonly used tools for image classification and provide the distributions of the sizes and shapes of features comprising an image. The application of pattern spectra on a CNN allows the selection of relevant combinations of features within an image. In this work, we generate pattern spectra from simulated gamma-ray images to train a CNN for signal-background separation and energy reconstruction for CTA. We compare our results to a CNN trained with CTA images and find that the pattern spectra-based analysis is computationally less expensive but not competitive with the purely CTA images-based analysis. Thus, we conclude that the CNN must rely on additional features in the CTA images not captured by the pattern spectra

Southern African Large Telescope Spectroscopy of BL Lacs for the CTA project

In the last two decades, very-high-energy gamma-ray astronomy has reached maturity: over 200 sources have been detected, both Galactic and extragalactic, by ground-based experiments. At present, Active Galactic Nuclei (AGN) make up about 40% of the more than 200 sources detected at very high energies with ground-based telescopes, the majority of which are blazars, i.e. their jets are closely aligned with the line of sight to Earth and three quarters of which are classified as high-frequency peaked BL Lac objects. One challenge to studies of the cosmological evolution of BL Lacs is the difficulty of obtaining redshifts from their nearly featureless, continuum-dominated spectra. It is expected that a significant fraction of the AGN to be detected with the future Cherenkov Telescope Array (CTA) observatory will have no spectroscopic redshifts, compromising the reliability of BL Lac population studies, particularly of their cosmic evolution. We started an effort in 2019 to measure the redshifts of a large fraction of the AGN that are likely to be detected with CTA, using the Southern African Large Telescope (SALT). In this contribution, we present two results from an on-going SALT program focused on the determination of BL Lac object redshifts that will be relevant for the CTA observatory

Southern African Large Telescope Spectroscopy of BL Lacs for the CTA project

In the last two decades, very-high-energy gamma-ray astronomy has reached maturity: over 200 sources have been detected, both Galactic and extragalactic, by ground-based experiments. At present, Active Galactic Nuclei (AGN) make up about 40% of the more than 200 sources detected at very high energies with ground-based telescopes, the majority of which are blazars, i.e. their jets are closely aligned with the line of sight to Earth and three quarters of which are classified as high-frequency peaked BL Lac objects. One challenge to studies of the cosmological evolution of BL Lacs is the difficulty of obtaining redshifts from their nearly featureless, continuum-dominated spectra. It is expected that a significant fraction of the AGN to be detected with the future Cherenkov Telescope Array (CTA) observatory will have no spectroscopic redshifts, compromising the reliability of BL Lac population studies, particularly of their cosmic evolution. We started an effort in 2019 to measure the redshifts of a large fraction of the AGN that are likely to be detected with CTA, using the Southern African Large Telescope (SALT). In this contribution, we present two results from an on-going SALT program focused on the determination of BL Lac object redshifts that will be relevant for the CTA observatory

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

High-energy gamma rays are promising tools to constrain or reveal the nature of dark matter, in particular Weakly Interacting Massive Particles. Being well into its pre-construction phase, the Cherenkov Telescope Array (CTA) will soon probe the sky in the 20 GeV - 300 TeV energy range. Thanks to its improved energy and angular resolutions as well as significantly larger effective area when compared to the current generation of Cherenkov telescopes, CTA is expected to probe heavier dark matter, with unprecedented sensitivity, reaching the thermal annihilation cross-section at 1 TeV. This talk will summarise the planned dark matter search strategies with CTA, focusing on the signal from the Galactic centre. As observed with the Fermi LAT at lower energies, this region is rather complex and CTA will be the first ground-based observatory sensitive to the large scale diffuse astrophysical emission from that region. We report on the collaboration effort to study the impact of such extended astrophysical backgrounds on the dark matter search, based on Fermi-LAT data in order to guide our observational strategies, taking into account various sources of systematic uncertainty

Sensitivity of CTA to gamma-ray emission from the Perseus galaxy cluster

In these proceedings we summarize the current status of the study of the sensitivity of the Cherenkov Telescope Array (CTA) to detect diffuse gamma-ray emission from the Perseus galaxy cluster. Gamma-ray emission is expected in galaxy clusters both from interactions of cosmic rays (CR) with the intra-cluster medium, or as a product of annihilation or decay of dark matter (DM) particles in case they are weakly interactive massive particles (WIMPs). The observation of Perseus constitutes one of the Key Science Projects to be carried out by the CTA Consortium. In this contribution, we focus on the DM-induced component of the flux. Our DM modelling includes the substructures we expect in the main halo which will boost the annihilation signal significantly. We adopt an ON/OFF observation strategy and simulate the expected gamma-ray signals. Finally we compute the expected CTA sensitivity using a likelihood maximization analysis including the most recent CTA instrument response functions. In absence of signal, we show that CTA will allow us to provide stringent and competitive constraints on TeV DM, especially for the case of DM decay

Effects of Semantic Analysis on Named-Entity Recognition with Conditional Random Fields

We propose a novel Named Entity Recognition (NER) system based on a machine learning technique and a semantic network. The NER system is able to exploit the advantages of semantic information, coming from Expert System proprietary technology, Cogito. NER is a task of Natural Language Processing (NLP) which consists in detecting, from an unformatted text source and classify, Named Entities (NE), i.e. real-world entities that can be denoted with a rigid designator. To address this problem, the chosen approach is a combination of machine learning and deep semantic processing. The machine learning method used is Conditional Random Fields (CRF). CRF is particularly suitable for the task because it analyzes an input sequence considering the whole sequence, instead of one item at a time. CRF has been trained not only with classical information, available after a simple computation or anyway with little effort, but with semantic information too. Semantic information is obtained with Sensigrafo and Semantic Disambiguator, which are the proprietary semantic network and semantic engine of Expert System, respectively. The results are encouraging, as we can experimentally prove the improvements in the NER task obtained by exploiting semantics

Effects of semantic analysis on named-entity recognition with conditional random fields

We propose a novel Named Entity Recognition (NER) system based on a machine learning technique and a semantic network. The NER system is able to exploit the advantages of semantic information, coming from Expert System proprietary technology, Cogito. NER is a task of Natural Language Processing (NLP) which consists in detecting, from an unformatted text source and classify, Named Entities (NE), i.e. real-world entities that can be denoted with a rigid designator. To address this problem, the chosen approach is a combination of machine learning and deep semantic processing. The machine learning method used is Conditional Random Fields (CRF). CRF is particularly suitable for the task because it analyzes an input sequence considering the whole sequence, instead of one item at a time. CRF has been trained not only with classical information, available after a simple computation or anyway with little effort, but with semantic information too. Semantic information is obtained with Sensigrafo and Semantic Disambiguator, which are the proprietary semantic network and semantic engine of Expert System, respectively. The results are encouraging, as we can experimentally prove the improvements in the NER task obtained by exploiting semantics

High-Density Near-Ultraviolet Silicon Photomultipliers: Characterization of photosensors for Cherenkov light detection

In recent years, Silicon Photomultipliers (SiPMs) have proven to be highly suitable devices for applications where high sensitivity to low-intensity light and fast responses are required. Among their many advantages are their low operational voltage when compared with classical photomultiplier tubes, mechanical robustness, and increased photon detection efficiency (PDE). Here we present a full characterization of a SiPM device technology developed in Italy by Fondazione Bruno Kessler, which is suitable for Cherenkov light detection in the Near-Ultraviolet (NUV) band. This device is a High-Density (HD) NUV SiPM, based on a microcell of 40 mu m x 40 mu m and with an area of 6 x 6 mm2, providing low levels of dark noise and high PDE peaking in the NUV band. This particular device has been selected to equip a part of the focal plane of the Schwarzschild-Couder Telescope (SCT) prototype proposed for the Cherenkov Telescope Array (CTA) Observatory