La caza de materia oscura en rayos-gamma con Fermi-LAT y CTA: modelado, predicciones y análisis en varios objetos astrofísicos

Tesis Doctoral inédita leída en la Universidad Autónoma de Madrid, Facultad de Ciencias, Departamento de Física Teórica. Fecha de Lectura: 25-11-2022Gravitational evidences at diferent cosmological scales hint towards the existence of a dark component of the Universe, which amounts up to the 85% of its matter density. Despite all the eforts, the dark matter (DM) has eluded any clear detection and its ultimate nature remains still unknown. The current knowledge suggests that the DM cannot be identifed with the particles comprised in the Standard Model (SM). In this context, the Weakly Interacting Massive Particles (WIMPs) provide a framework that naturally yields the measured DM relic density, while at the same time they are expected to interact with SM particles. From these interactions, one of the most promising channels are γ-rays, which travel the Universe without defection, pointing back towards its original source. This Thesis has been devoted to unveil the unknown properties of the dark matter focusing on the so-called γ-ray indirect DM searches. We have conducted an exhaustive study of the DM contents and distribution in diferent interesting astrophysical objects and computed the state-of-the-art predictions for their annihilation and decay DM fuxes. Then, we have used these models to both, perform searches in existing F ermi-LAT data and obtain the prospects for the future Cherenkov Telescope Array (CTA). In the absence of detection, we proceed to a systematic search starting with galaxy clusters, known to be very good candidates to search DM emission. We compute the sensitivity to difuse γ-ray emission from Perseus, one of the most massive local galaxy clusters, of the future CTA, including in the analysis the CR-induced γ-rays as a background in a template ftting analysis. Staying with galaxy clusters, we then use 12 years of Fermi-LAT data from nearly 50 local galaxy clusters, searching for a DM-induced γ-ray signal, modelled including the expected substructures. We also explore introducing new targets in the quest of DM. For this, we perform the frst DM γ-ray search in dwarf irregular galaxies (dIrrs) using Fermi-LAT data. Finally, we collect the results of the previous studies to model the DM content of a subsample of objects that we have already investigated (dIrrs and clusters) and also of representative dark subhalos of the Milky Wa

Constraining the dark matter contribution of γ rays in clusters of galaxies using Fermi -LAT data

Clusters of galaxies are the largest gravitationally bound systems in the Universe. Their dynamics are dominated by dark matter (DM), which makes them among the best targets for indirect DM searches. We analyze 12 years of data collected by the Fermi Large Area Telescope (Fermi-LAT) in the direction of 49 clusters of galaxies selected for their proximity to the Earth and their high x-ray flux, which makes them the most promising targets. We first create physically motivated models for the DM density around each cluster considering different assumptions for the substructure distribution. Then we perform a combined search for a γ-ray signal in the Fermi-LAT data between 500 MeV and 1 TeV. We find a signal of γ rays potentially associated with DM that is at a statistical significance of 2.5σ-3.0σ when considering a slope for the subhalo mass distribution α=1.9 and minimum mass of Mmin=10-6M⊙. The best-fit DM mass and annihilation cross sections for a bb¯ annihilation channel are mχ=40-60 GeV and (σv)=(2-4)×10-25 cm3/s. When we consider α=2.0 and Mmin=10-9M⊙, the best fit of the cross section reduces to (σv)=(4-10)×10-26 cm3/s. For both DM substructure models there is a tension between the values of (σv) that we find and the upper limits obtained with the nondetection of a γ-ray flux from Milky Way dwarf spheroidal galaxies. This signal is thus more likely associated with γ rays produced in the intracluster region by cosmic rays colliding with gas and photon fieldsM. D. M.’s research is supported by Fellini—Fellowship for Innovation at INFN, funded by the European Union’s Horizon 2020 research programme under the Marie Sklodowska-Curie Cofund Action, Grant Agreement No. 754496. J. P. R.’s work is supported by Grant No. SEV-2016-0597-17-2 funded by MCIN/AEI/ 10.13039/501100011033 and “ESF Investing in your future.” M. A. S. C. was also supported by the Atracción de Talento Contracts No. 2016-T1/TIC-1542 and No. 2020- 5A/TIC-19725 granted by the Comunidad de Madrid in Spain. The work of J. P. R. and M. A. S. C. was additionally supported by the Grants No. PGC2018-095161-B-I00, No. PID2021-125331NB-I0

Constraining the dark matter contribution of γ\gamma rays in Cluster of galaxies using Fermi-LAT data

Clusters of galaxies are the largest gravitationally-bound systems in the Universe. Their dynamics are dominated by dark matter (DM), which makes them among the best targets for indirect DM searches. We analyze 12 years of data collected by the Fermi Large Area Telescope (Fermi-LAT) in the direction of 49 clusters of galaxies selected for their proximity to the Earth and their high X-ray flux, which makes them the most promising targets. We first create physically motivated models for the DM density around each cluster considering different assumptions for the substructure distribution. Then we perform a combined search for a $\gamma$-ray signal in the {\it Fermi}-LAT data between 500 MeV and 1 TeV. We find a signal of $\gamma$ rays potentially associated with DM that is at a statistical significance of $2.5\sigma-3.0\sigma$ when considering a slope for the subhalo mass distribution $\alpha=1.9$ and minimum mass of $M_{\rm{min}}=10^{-6}$ $M_{\odot}$. The best-fit DM mass and annihilation cross-sections for a $b\bar{b}$ annihilation channel are $m_{\chi}=40-60$ GeV and $\langle \sigma v \rangle = (2-4) \times 10^{-25}$ cm$^3$/s. When we consider $\alpha=2.0$ and $M_{\rm{min}}=10^{-9}$ $M_{\odot}$, the best-fit of the cross section reduces to $\langle \sigma v \rangle = (4-10) \times 10^{-26}$ cm$^3$/s. For both DM substructure models there is a tension between the values of $\langle \sigma v \rangle$ that we find and the upper limits obtained with the non-detection of a $\gamma$-ray flux from Milky Way dwarf spheroidal galaxies. This signal is thus more likely associated with $\gamma$ rays produced in the intracluster region by cosmic rays colliding with gas and photon fields.Comment: 27 Pages, 13 Figures. Accepted for publication in the PRD journa

Spatial extension of dark subhalos as seen by Fermi-LAT and the implications for WIMP constraints

Spatial extension has been hailed as a "smoking gun"in the gamma-ray search of dark galactic subhalos, which would appear as unidentified sources for gamma-ray telescopes. In this work, we study the sensitivity of the Fermi-LAT to extended subhalos using simulated data based on a realistic sky model. We simulate spatial templates for a set of representative subhalos, whose parameters were derived from our previous work with N-body cosmological simulation data. We find that detecting an extended subhalo and finding an unequivocal signal of angular extension requires, respectively, a flux 2 to 10 times larger than in the case of a pointlike source. By studying a large grid of models, where parameters such as the WIMP mass, annihilation channel, or subhalo model are varied significantly, we obtain the response of the LAT as a function of the product of annihilation cross-section times the J factor. Indeed, we show that spatial extension can be used as an additional "filter"to reject subhalos candidates among the pool of unidentified LAT sources, as well as a smoking gun for positive identification. For instance, typical angular extensions of a few tenths of a degree are expected for the considered scenarios. Finally, we also study the impact of the obtained LAT sensitivity to such extended subhalos on the achievable dark matter constraints, which are a few times less constraining than comparable point-source limit

The identification of economically relevant health and social care services for mental disorders in the PECUNIA project

Background: Health economic research is still facing significant problems regarding the standardization and international comparability of health care services. As a result, comparative effectiveness studies and cost-effectiveness analyses are often not comparable. This study is part of the PECUNIA project, which aimed to improve the comparability of economic evaluations by developing instruments for the internationally standardized measurement and valuation of health care services for mental disorders. The aim of this study was to identify internationally relevant services in the health and social care sectors relevant for health economic studies for mental disorders. Methods: A systematic literature review on cost-of-illness studies and economic evaluations was conducted to identify relevant services, complemented by an additional grey literature search and a search of resource use measurement (RUM) questionnaires. A preliminary long-list of identified services was explored and reduced to a short-list by multiple consolidation rounds within the international research team and an external international expert survey in six European countries. Results: After duplicate removal, the systematic search yielded 15,218 hits. From these 295 potential services could be identified. The grey literature search led to 368 and the RUM search to 36 additional potential services. The consolidation process resulted in a preliminary list of 186 health and social care services which underwent an external expert survey. A final consolidation step led to a basic list of 56 services grouped into residential care, daycare, outpatient care, information for care, accessibility to care, and self-help and voluntary care. Conclusions: The initial literature searches led to an extensive number of potential service items for health and social care. Many of these items turned out to be procedures, interventions or providing professionals rather than services and were removed from further analysis. The resulting list was used as a basis for typological coding, the development of RUM questionnaires and corresponding unit costs for international mental health economic studies in the PECUNIA project.</p

Systematic Collaborative Reanalysis of Genomic Data Improves Diagnostic Yield in Neurologic Rare Diseases

Altres ajuts: Generalitat de Catalunya, Departament de Salut; Generalitat de Catalunya, Departament d'Empresa i Coneixement i CERCA Program; Ministerio de Ciencia e Innovación; Instituto Nacional de Bioinformática; ELIXIR Implementation Studies (CNAG-CRG); Centro de Investigaciones Biomédicas en Red de Enfermedades Raras; Centro de Excelencia Severo Ochoa; European Regional Development Fund (FEDER).Many patients experiencing a rare disease remain undiagnosed even after genomic testing. Reanalysis of existing genomic data has shown to increase diagnostic yield, although there are few systematic and comprehensive reanalysis efforts that enable collaborative interpretation and future reinterpretation. The Undiagnosed Rare Disease Program of Catalonia project collated previously inconclusive good quality genomic data (panels, exomes, and genomes) and standardized phenotypic profiles from 323 families (543 individuals) with a neurologic rare disease. The data were reanalyzed systematically to identify relatedness, runs of homozygosity, consanguinity, single-nucleotide variants, insertions and deletions, and copy number variants. Data were shared and collaboratively interpreted within the consortium through a customized Genome-Phenome Analysis Platform, which also enables future data reinterpretation. Reanalysis of existing genomic data provided a diagnosis for 20.7% of the patients, including 1.8% diagnosed after the generation of additional genomic data to identify a second pathogenic heterozygous variant. Diagnostic rate was significantly higher for family-based exome/genome reanalysis compared with singleton panels. Most new diagnoses were attributable to recent gene-disease associations (50.8%), additional or improved bioinformatic analysis (19.7%), and standardized phenotyping data integrated within the Undiagnosed Rare Disease Program of Catalonia Genome-Phenome Analysis Platform functionalities (18%)

The SADDEN DEATH Study: Results from a Pilot Study in Non-ICU COVID-19 Spanish Patients

Introduction: The worldwide pandemic, coronavirus disease 2019 (COVID-19) is a novel infection with serious clinical manifestations, including death. Our aim is to describe the first non-ICU Spanish deceased series with COVID-19, comparing specifically between unexpected and expected deaths. Methods: In this single-centre study, all deceased inpatients with laboratory-confirmed COVID-19 who had died from March 4 to April 16, 2020 were consecutively included. Demographic, clinical, treatment, and laboratory data, were analyzed and compared between groups. Factors associated with unexpected death were identified by multivariable logistic regression methods. Results: In total, 324 deceased patients were included. Median age was 82 years (IQR 76–87); 55.9% males. The most common cardiovascular risk factors were hypertension (78.4%), hyperlipidemia (57.7%), and diabetes (34.3%). Other common comorbidities were chronic kidney disease (40.1%), chronic pulmonary disease (30.3%), active cancer (13%), and immunosuppression (13%). The Confusion, BUN, Respiratory Rate, Systolic BP and age ≥65 (CURB-65) score at admission was >2 in 40.7% of patients. During hospitalization, 77.8% of patients received antivirals, 43.3% systemic corticosteroids, and 22.2% full anticoagulation. The rate of bacterial co-infection was 5.5%, and 105 (32.4%) patients had an increased level of troponin I. The median time from initiation of therapy to death was 5 days (IQR 3.0–8.0). In 45 patients (13.9%), the death was exclusively attributed to COVID-19, and in 254 patients (78.4%), both COVID-19 and the clinical status before admission contributed to death. Progressive respiratory failure was the most frequent cause of death (92.0%). Twenty-five patients (7.7%) had an unexpected death. Factors independently associated with unexpected death were male sex, chronic kidney disease, insulin-treated diabetes, and functional independence. Conclusions: This case series provides in-depth characterization of hospitalized non-ICU COVID-19 patients who died in Madrid. Male sex, insulin-treated diabetes, chronic kidney disease, and independency for activities of daily living are predictors of unexpected death