Using Interstellar Clouds to Search for Galactic PeVatrons: Gamma-ray Signatures from Supernova Remnants

Interstellar clouds can act as target material for hadronic cosmic rays; gamma rays subsequently produced through inelastic proton-proton collisions and spatially associated with such clouds can provide a key indicator of efficient particle acceleration. However, even in the case that particle acceleration proceeds up to PeV energies, the system of accelerator and nearby target material must fulfil a specific set of conditions in order to produce a detectable gamma-ray flux. In this study, we rigorously characterise the necessary properties of both cloud and accelerator. By using available Supernova Remnant (SNR) and interstellar cloud catalogues, we produce a ranked shortlist of the most promising target systems, those for which a detectable gamma-ray flux is predicted, in the case that particles are accelerated to PeV energies in a nearby SNR. We discuss detection prospects for future facilities including CTA, LHAASO and SWGO; and compare our predictions with known gamma-ray sources. The four interstellar clouds with the brightest predicted fluxes >100 TeV identified by this model are located at (l,b) = (330.05, 0.13), (15.82, -0.46), (271.09, -1.26), and (21.97, -0.29). These clouds are consistently bright under a range of model scenarios, including variation in the diffusion coefficient and particle spectrum. On average, a detectable gamma-ray flux is more likely for more massive clouds; systems with lower separation distance between the SNR and cloud; and for slightly older SNRs.Comment: Accepted for publication in MNRAS. 30 pages, 16 figures, 7 table

Arc-minute-scale studies of the interstellar gas towards HESS \,J1804−-216: Still an unidentified TeV γ\gamma-ray source

The Galactic TeV $\gamma$-ray source HESS$\,$J1804$-$216 is currently an unidentified source. In an attempt to unveil its origin, we present here the most detailed study of interstellar gas using data from the Mopra Southern Galactic Plane CO Survey, 7 and 12$\,$mm wavelength Mopra surveys and Southern Galactic Plane Survey of HI. Several components of atomic and molecular gas are found to overlap HESS$\,$J1804$-$216 at various velocities along the line of sight. The CS(1-0) emission clumps confirm the presence of dense gas. Both correlation and anti-correlation between the gas and TeV $\gamma$-ray emission have been identified in various gas tracers, enabling several origin scenarios for the TeV $\gamma$-ray emission from HESS$\,$J1804$-$216. For a hadronic scenario, SNR$\,$G8.7$-$0.1 and the progenitor SNR of PSR$\,$J1803$-$2137 require cosmic ray (CR) enhancement factors of $\mathord{\sim} 50$ times the solar neighbour CR flux value to produce the TeV $\gamma$-ray emission. Assuming an isotropic diffusion model, CRs from both these SNRs require a slow diffusion coefficient, as found for other TeV SNRs associated with adjacent ISM gas. The morphology of gas located at 3.8$\,$kpc (the dispersion measure distance to PSR$\,$J1803$-$2137) tends to anti-correlate with features of the TeV emission from HESS$\,$J1804$-$216, making the leptonic scenario possible. Both pure hadronic and pure leptonic scenarios thus remain plausible.Comment: 29 pages, 23 figures, 5 tables, accepted for publication in PAS

Improving GSEA for Analysis of Biologic Pathways for Differential Gene Expression across a Binary Phenotype

Gene-set analysis evaluates the expression of biological pathways, or a priori defined gene sets, rather than that of single genes, in association with a binary phenotype, and is of great biologic interest in many DNA microarray studies. Gene Set Enrichment Analysis (GSEA) has been applied widely as a tool for gene-set analyses. We describe here some critical problems with GSEA and propose an alternative method by extending the single-gene analysis method, Significance Analysis of Microarray (SAM), to gene-set analyses (SAM-GS). Specifically, we illustrate, in a simulation study, that GSEA gives statistical significance to gene sets that have no gene associated with the phenotype (null gene sets), and has very low power to detect gene sets in which half the genes are highly associated with the phenotype (truly-associated gene sets). SAM-GS, on the other hand, performs perfectly in the simulation study: none of the null gene sets is identified with statistical significance, while all of the truly-associated gene sets are. The two methods are also compared in the analyses of three real microarray datasets and relevant pathways, the diverging results of which clearly show the advantages of SAM-GS over GSEA, both statistically and biologically

Improving gene set analysis of microarray data by SAM-GS

<p>Abstract</p> <p>Background</p> <p><it>Gene-set </it>analysis evaluates the expression of biological pathways, or <it>a priori </it>defined gene sets, rather than that of individual genes, in association with a binary phenotype, and is of great biologic interest in many DNA microarray studies. Gene Set Enrichment Analysis (GSEA) has been applied widely as a tool for gene-set analyses. We describe here some critical problems with GSEA and propose an alternative method by extending the individual-gene analysis method, Significance Analysis of Microarray (SAM), to gene-set analyses (SAM-GS).</p> <p>Results</p> <p>Using a mouse microarray dataset with simulated gene sets, we illustrate that GSEA gives statistical significance to gene sets that have no gene associated with the phenotype (null gene sets), and has very low power to detect gene sets in which half the genes are moderately or strongly associated with the phenotype (truly-associated gene sets). SAM-GS, on the other hand, performs very well. The two methods are also compared in the analyses of three real microarray datasets and relevant pathways, the diverging results of which clearly show advantages of SAM-GS over GSEA, both statistically and biologically. In a microarray study for identifying biological pathways whose gene expressions are associated with <it>p53 </it>mutation in cancer cell lines, we found biologically relevant performance differences between the two methods. Specifically, there are 31 additional pathways identified as significant by SAM-GS over GSEA, that are associated with the presence vs. absence of <it>p53</it>. Of the 31 gene sets, 11 actually involve <it>p53 </it>directly as a member. A further 6 gene sets directly involve the extrinsic and intrinsic apoptosis pathways, 3 involve the cell-cycle machinery, and 3 involve cytokines and/or JAK/STAT signaling. Each of these 12 gene sets, then, is in a direct, well-established relationship with aspects of <it>p53 </it>signaling. Of the remaining 8 gene sets, 6 have plausible, if less well established, links with <it>p53</it>.</p> <p>Conclusion</p> <p>We conclude that GSEA has important limitations as a gene-set analysis approach for microarray experiments for identifying biological pathways associated with a binary phenotype. As an alternative statistically-sound method, we propose SAM-GS. A free Excel Add-In for performing SAM-GS is available for public use.</p

Data compression for the First G-APD Cherenkov Telescope

The First Geiger-mode Avalanche photodiode (G-APD) Cherenkov Telescope (FACT) has been operating on the Canary island of La Palma since October 2011. Operations were automated so that the system can be operated remotely. Manual interaction is required only when the observation schedule is modified due to weather conditions or in case of unexpected events such as a mechanical failure. Automatic operations enabled high data taking efficiency, which resulted in up to two terabytes of FITS files being recorded nightly and transferred from La Palma to the FACT archive at ISDC in Switzerland. Since long term storage of hundreds of terabytes of observations data is costly, data compression is mandatory. This paper discusses the design choices that were made to increase the compression ratio and speed of writing of the data with respect to existing compression algorithms. Following a more detailed motivation, the FACT compression algorithm along with the associated I/O layer is discussed. Eventually, the performances of the algorithm is compared to other approaches.Comment: 17 pages, accepted to Astronomy and Computing special issue on astronomical file format

FACT - Monitoring Blazars at Very High Energies

The First G-APD Cherenkov Telescope (FACT) was built on the Canary Island of La Palma in October 2011 as a proof of principle for silicon based photosensors in Cherenkov Astronomy. The scientific goal of the project is to study the variability of active galatic nuclei (AGN) at TeV energies. Observing a small sample of TeV blazars whenever possible, an unbiased data sample is collected. This allows to study the variability of the selected objects on timescales from hours to years. Results from the first three years of monitoring will be presented. To provide quick flare alerts to the community and trigger multi-wavelength observations, a quick look analysis has been installed on-site providing results publicly online within the same night. In summer 2014, several flare alerts were issued. Results of the quick look analysis are summarized.Comment: 2014 Fermi Symposium proceedings - eConf C14102.

FACT - How stable are the silicon photon detectors?

The First G-APD Cherenkov telescope (FACT) is the first telescope using silicon photon detectors (G-APD aka. SiPM). The use of Silicon devices promise a higher photon detection efficiency, more robustness and higher precision than photo-multiplier tubes. Since the properties of G-APDs depend on auxiliary parameters like temperature, a feedback system adapting the applied voltage accordingly is mandatory. In this presentation, the feedback system, developed and in operation for FACT, is presented. Using the extraction of a single photon-equivalent (pe) spectrum as a reference, it can be proven that the sensors can be operated with very high precision. The extraction of the single-pe, its spectrum up to 10\,pe, its properties and their precision, as well as their long-term behavior during operation are discussed. As a by product a single pulse template is obtained. It is shown that with the presented method, an additional external calibration device can be omitted. The presented method is essential for the application of G-APDs in future projects in Cherenkov astronomy and is supposed to result in a more stable and precise operation than possible with photo-multiplier tubes

FACT - Threshold prediction for higher duty cycle and improved scheduling

The First G-APD Cherenkov telescope (FACT) is the first telescope using silicon photon detectors (G-APD aka. SiPM). The use of Silicon devices promise a higher photon detection efficiency, more robustness and higher precision than photo-multiplier tubes. Being operated during different light-conditions, the threshold settings of a Cherenkov telescope have to be adapted to feature the lowest possible threshold but also an efficient suppression of triggers from night-sky background photons. Usually this threshold is set either by experience or a mini-ratescan. Since the measured current through the sensors is directly correlated with the noise level, the current can be used to set the best threshold at any time. Due to the correlation between the physical threshold and the final energy threshold, the current can also be used as a measure for the energy threshold of any observation. This presentation introduces a method which uses the properties of the moon and the source position to predict the currents and the corresponding energy threshold for every upcoming observation allowing to adapt the observation schedule accordingly

A Biological Evaluation of Six Gene Set Analysis Methods for Identification of Differentially Expressed Pathways in Microarray Data

Gene-set analysis of microarray data evaluates biological pathways, or gene sets, for their differential expression by a phenotype of interest. In contrast to the analysis of individual genes, gene-set analysis utilizes existing biological knowledge of genes and their pathways in assessing differential expression. This paper evaluates the biological performance of five gene-set analysis methods testing “self-contained null hypotheses” via subject sampling, along with the most popular gene-set analysis method, Gene Set Enrichment Analysis (GSEA). We use three real microarray analyses in which differentially expressed gene sets are predictable biologically from the phenotype. Two types of gene sets are considered for this empirical evaluation: one type contains “truly positive” sets that should be identified as differentially expressed; and the other type contains “truly negative” sets that should not be identified as differentially expressed. Our evaluation suggests advantages of SAM-GS, Global, and ANCOVA Global methods over GSEA and the other two methods

FACT - Long-term Monitoring of Bright TeV-Blazars

Since October 2011, the First G-APD Cherenkov Telescope (FACT) is operated successfully on the Canary Island of La Palma. Apart from the proof of principle for the use of G-APDs in Cherenkov telescopes, the major goal of the project is the dedicated long-term monitoring of a small sample of bright TeV blazars. The unique properties of G-APDs permit stable observations also during strong moon light. Thus a superior sampling density is provided on time scales at which the blazar variability amplitudes are expected to be largest, as exemplified by the spectacular variations of Mrk 501 observed in June 2012. While still in commissioning, FACT monitored bright blazars like Mrk 421 and Mrk 501 during the past 1.5 years so far. Preliminary results including the Mrk 501 flare from June 2012 will be presented.Comment: 4 pages, 4 figures, presented at the 33rd ICRC (2013