Monte Carlo studies on the sensitivity of the HEGRA imaging atmospheric Cerenkov telescope system in observations of extended gamma-ray sources

In this paper, we present the results of Monte Carlo simulations of atmospheric showers induced by diffuse gamma rays as detected by the high-energy gamma ray astronomy (HEGRA) system of five imaging atmospheric Cerenkov telescopes (IACTs). We have investigated the sensitivity of observations on extended gamma ray emission over the entire field of view of the instrument. We discuss a technique to search for extended gamma ray sources within the field of view of the instrument. We give estimates for HEGRA sensitivity of observations on extended TeV gamma ray sources.Comment: 21 pages, 7 figures, accepted for publication in "Journal of Physics G: Nuclear and Particle Physics

Comparison of techniques to reconstruct VHE gamma-ray showers from multiple stereoscopic Cherenkov images

For air showers observed simultaneously by more than two imaging atmospheric Cherenkov telescopes, the shower geometry is overconstrained by the images and image information should be combined taking into account the quality of the images. Different algorithms are discussed and tested experimentally using data obtained from observations of Mkn 501 with the HEGRA IACT system. Most of these algorithms provide an estimate of the accuracy of the reconstruction of shower geometry on an event-by-event basis, allowing, e.g., to select higher-quality subsamples for precision measurements.Comment: 14 Pages, 6 figures, Late

Improved energy resolution for VHE gamma-ray astronomy with systems of Cherenkov telescopes

We present analysis techniques to improve the energy resolution of stereoscopic systems of imaging atmospheric Cherenkov telescopes, using the HEGRA telescope system as an example. The techniques include (i) the determination of the height of the shower maximum, which is then taken into account in the energy determination, and (ii) the determination of the location of the shower core with the additional constraint that the direction of the gamma rays is known a priori. This constraint can be applied for gamma-ray point sources, and results in a significant improvement in the localization of the shower core, which translates into better energy resolution. Combining both techniques, the HEGRA telescopes reach an energy resolution between 9% and 12%, over the entire energy range from 1 TeV to almost 100 TeV. Options for further improvements of the energy resolution are discussed.Comment: 13 Pages, 7 figures, Latex. Astroparticle Physics, in pres

Photometric Supernova Cosmology with BEAMS and SDSS-II

Supernova cosmology without spectroscopic confirmation is an exciting new frontier which we address here with the Bayesian Estimation Applied to Multiple Species (BEAMS) algorithm and the full three years of data from the Sloan Digital Sky Survey II Supernova Survey (SDSS-II SN). BEAMS is a Bayesian framework for using data from multiple species in statistical inference when one has the probability that each data point belongs to a given species, corresponding in this context to different types of supernovae with their probabilities derived from their multi-band lightcurves. We run the BEAMS algorithm on both Gaussian and more realistic SNANA simulations with of order 10^4 supernovae, testing the algorithm against various pitfalls one might expect in the new and somewhat uncharted territory of photometric supernova cosmology. We compare the performance of BEAMS to that of both mock spectroscopic surveys and photometric samples which have been cut using typical selection criteria. The latter typically are either biased due to contamination or have significantly larger contours in the cosmological parameters due to small data-sets. We then apply BEAMS to the 792 SDSS-II photometric supernovae with host spectroscopic redshifts. In this case, BEAMS reduces the area of the (\Omega_m,\Omega_\Lambda) contours by a factor of three relative to the case where only spectroscopically confirmed data are used (297 supernovae). In the case of flatness, the constraints obtained on the matter density applying BEAMS to the photometric SDSS-II data are \Omega_m(BEAMS)=0.194\pm0.07. This illustrates the potential power of BEAMS for future large photometric supernova surveys such as LSST.Comment: 25 pages, 15 figures, submitted to Ap

The Relation Between Ejecta Velocity, Intrinsic Color, and Host-Galaxy Mass for High-Redshift Type Ia Supernovae

Recently, using a large low-redshift sample of Type Ia supernovae (SNe Ia), we discovered a relation between SN Ia ejecta velocity and intrinsic color that improves the distance precision of SNe Ia and reduces potential systematic biases related to dust reddening. No SN Ia cosmological results have yet made a correction for the "velocity-color" relation. To test the existence of such a relation and constrain its properties at high redshift, we examine a sample of 75 SNe Ia discovered and observed by the Sloan Digital Sky Survey-II (SDSS-II) Supernova Survey and Supernova Legacy Survey (SNLS). From each spectrum, we measure ejecta velocities at maximum brightness for the Ca H&K and Si II 6355 features, v_Ca^0 and v_Si^0, respectively. Using SN light-curve parameters, we determine the intrinsic B_max - V_max for each SN. Similar to what was found at low-redshift, we find that SNe Ia with higher ejecta velocity tend to be intrinsically redder than SNe Ia with lower ejecta velocity. The distributions of ejecta velocities for SNe Ia at low and high redshift are similar, indicating that current cosmological results should have little bias related to the velocity-color relation. Additionally, we find a slight (2.4-sigma significant) trend between SN Ia ejecta velocity and host-galaxy mass such that SNe Ia in high-mass host galaxies tend to have lower ejecta velocities as probed by v_Ca^0. These results emphasize the importance of spectroscopy for SN Ia cosmology.Comment: 13 pages, 11 figures, accepted by Ap

Internal Robustness: systematic search for systematic bias in SN Ia data

A great deal of effort is currently being devoted to understanding, estimating and removing systematic errors in cosmological data. In the particular case of type Ia supernovae, systematics are starting to dominate the error budget. Here we propose a Bayesian tool for carrying out a systematic search for systematic contamination. This serves as an extension to the standard goodness-of-fit tests and allows not only to cross-check raw or processed data for the presence of systematics but also to pin-point the data that are most likely contaminated. We successfully test our tool with mock catalogues and conclude that the Union2.1 data do not possess a significant amount of systematics. Finally, we show that if one includes in Union2.1 the supernovae that originally failed the quality cuts, our tool signals the presence of systematics at over 3.8-sigma confidence level.Comment: 14 pages, 15 figures; matches version accepted for publication in MNRA

Statistical classification techniques for photometric supernova typing

Future photometric supernova surveys will produce vastly more candidates than can be followed up spectroscopically, highlighting the need for effective classification methods based on light curves alone. Here we introduce boosting and kernel density estimation techniques which have minimal astrophysical input, and compare their performance on 20 000 simulated Dark Energy Survey light curves. We demonstrate that these methods perform very well provided a representative sample of the full population is used for training. Interestingly, we find that they do not require the redshift of the host galaxy or candidate supernova. However, training on the types of spectroscopic subsamples currently produced by supernova surveys leads to poor performance due to the resulting bias in training, and we recommend that special attention be given to the creation of representative training samples. We show that given a typical non-representative training sample, S, one can expect to pull out a representative subsample of about 10 per cent of the size of S, which is large enough to outperform the methods trained on all of

Statistical Classification Techniques for Photometric Supernova Typing

Future photometric supernova surveys will produce vastly more candidates than can be followed up spectroscopically, highlighting the need for effective classification methods based on lightcurves alone. Here we introduce boosting and kernel density estimation techniques which have minimal astrophysical input, and compare their performance on 20,000 simulated Dark Energy Survey lightcurves. We demonstrate that these methods are comparable to the best template fitting methods currently used, and in particular do not require the redshift of the host galaxy or candidate. However both methods require a training sample that is representative of the full population, so typical spectroscopic supernova subsamples will lead to poor performance. To enable the full potential of such blind methods, we recommend that representative training samples should be used and so specific attention should be given to their creation in the design phase of future photometric surveys.Comment: 19 pages, 41 figures. No changes. Additional material and summary video available at http://cosmoaims.wordpress.com/2010/09/30/boosting-for-supernova-classification