- The effect of non-Gaussianity on error predictions for the Epoch of Reionization (EoR) 21-cm power spectrum
- Mondal, Rajesh; Bharadwaj, Somnath; Majumdar, Suman; Bera, Apurba; Acharyya, Ayan
The Epoch of Reionization (EoR) 21-cm signal is expected to become increasingly non-Gaussian as reionization proceeds. We have used seminumerical simulations to study how this affects the error predictions for the EoR 21-cm power spectrum. We expect …
- Selection between foreground models for global 21-cm experiments
- Harker, Geraint J. A.
The precise form of the foregrounds for sky-averaged measurements of the 21-cm line during and before the epoch of reionization is unknown. We suggest that the level of complexity in the foreground models used to fit global 21-cm data should be driv …
- Selection biases in the gamma-ray burst Eiso – Lopt, X correlation
- Coward, D. M.; Howell, E. J.; Wan, L.; Macpherson, D.
Gamma-ray burst (GRB) optical and X-ray afterglow luminosity is expected to correlate with the GRB isotropic equivalent kinetic energy of the outflow in the standard synchrotron model for GRB afterglows. Previous studies, using prompt GRB isotropic …
- Fast Bayesian inference for exoplanet discovery in radial velocity data
- Brewer, Brendon J.; Donovan, Courtney P.
Inferring the number of planets N in an exoplanetary system from radial velocity (RV) data is a challenging task. Recently, it has become clear that RV data can contain periodic signals due to stellar activity, which can be difficult to distinguish …
- Angular power spectra with finite counts
- Campbell, Sheldon S.
Angular anisotropy techniques for cosmic diffuse radiation maps are powerful probes, even for quite small data sets. A popular observable is the angular power spectrum; we present a detailed study applicable to any unbinned source skymap S(n) from w …
- Bayesian analysis of resolved stellar spectra: application to MMT/Hectochelle observations of the Draco dwarf spheroidal
- Walker, Matthew G.; Olszewski, Edward W.; Mateo, Mario
We introduce a Bayesian method for fitting faint, resolved stellar spectra in order to obtain simultaneous estimates of redshift and stellar-atmospheric parameters. We apply the method to thousands of spectra – covering 5160-5280 Å at resolution R� …
- Detection of low-level periodic signals through enhanced self-correlation method. The case of ER Vulpeculae
- Crăciun, Maria; Vamoş, Călin; Pop, Alexandru
The self-correlation (SC) method proposed three decades ago by Percy, Jakate and Matthews is a complementary time series analysis method which provides a characterization of a variability phenomenon and an estimation of the involved time-scale. It c …
- SOFIA: a flexible source finder for 3D spectral line data
- Serra, Paolo; Westmeier, Tobias; Giese, Nadine; Jurek, Russell; Flöer, Lars; Popping, Attila; Winkel, Benjamin; van der Hulst, Thijs; Meyer, Martin; Koribalski, Bärbel S.; Staveley-Smith, Lister; Courtois, Hélène
We introduce SOFIA, a flexible software application for the detection and parametrization of sources in 3D spectral line data sets. SOFIA combines for the first time in a single piece of software a set of new source-finding and parametrization algor …
- SOFIA: a flexible source finder for 3D spectral line data
- Serra, Paolo; Westmeier, Tobias; Giese, Nadine; Jurek, Russell; Flöer, Lars; Popping, Attila; Winkel, Benjamin; van der Hulst, Thijs; Meyer, Martin; Koribalski, Bärbel S.; Staveley-Smith, Lister; Courtois, Hélène
We introduce SOFIA, a flexible software application for the detection and parametrization of sources in 3D spectral line data sets. SOFIA combines for the first time in a single piece of software a set of new source-finding and parametrization algor …
- Marginal likelihoods of distances and extinctions to stars: computation and compact representation
- Sale, S. E.; Magorrian, J.
We present a method for obtaining the likelihood function of distance and extinction to a star given its photometry. The other properties of the star (its mass, age, metallicity and so on) are marginalized assuming a simple Galaxy model. We demonstr …
- Data mining for gravitationally lensed quasars
- Agnello, Adriano; Kelly, Brandon C.; Treu, Tommaso; Marshall, Philip J.
Gravitationally lensed quasars are brighter than their unlensed counterparts and produce images with distinctive morphological signatures. Past searches and target-selection algorithms, in particular the Sloan Quasar Lens Search (SQLS), have relied …
- Orbit Determination of Mixed Observations of Multiple Objects1,2
- Xin, WANG
In the conventional orbit determination with optical measurements of space objects, some observations of different objects may be marked as the same object. For this kind of data, the process of orbit determination will be failed to converge or comp …
- Is There Evidence for Dark Energy Evolution?
- Ding, Xuheng; Biesiada, Marek; Cao, Shuo; Li, Zhengxiang; Zhu, Zong-Hong
Recently, Sahni et al. combined two independent measurements of H(z) from BAO data with the value of the Hubble constant {{H}0}=H(z=0) in order to test the cosmological constant hypothesis by means of an improved version of the Om diagnostic. Their …
- Resolving the AGN and Host Emission in the Mid-infrared Using a Model-independent Spectral Decomposition
- Hernán-Caballero, Antonio; Alonso-Herrero, Almudena; Hatziminaoglou, Evanthia; Spoon, Henrik W. W.; Ramos Almeida, Cristina; Díaz Santos, Tanio; Hönig, Sebastian F.; González-Martín, Omaira; Esquej, Pilar
We present results on the spectral decomposition of 118 Spitzer Infrared Spectrograph (IRS) spectra from local active galactic nuclei (AGNs) using a large set of Spitzer/IRS spectra as templates. The templates are themselves IRS spectra from extreme …
- Luminous Red Galaxies: Selection and Classification By Combining Optical and Infrared Photometry
- Prakash, Abhishek; Licquia, Timothy C.; Newman, Jeffrey A.; Rao, Sandhya M.
We describe a new method of combining optical and infrared photometry to select luminous red galaxies (LRGs) at redshifts z\gt 0.6. We explore this technique using a combination of optical photometry from CFHTLS and Hubble Space Telescope, infrared …
- Automatic Detection Algorithm of Dynamic Pressure Pulses in the Solar Wind
- Zuo, Pingbing; Feng, Xueshang; Xie, Yanqiong; Wang, Yi; Li, Huijun; Xu, Xiaojun
Dynamic pressure pulses (DPPs) in the solar wind are a significant phenomenon closely related to the solar-terrestrial connection and physical processes of solar wind dynamics. In order to automatically identify DPPs from solar wind measurements, we …
- A Method for the Estimation of p-Mode Parameters from Averaged Solar Oscillation Power Spectra
- Reiter, J.; Rhodes, E. J., Jr.; Kosovichev, A. G.; Schou, J.; Scherrer, P. H.; Larson, T. P.
A new fitting methodology is presented that is equally well suited for the estimation of low-, medium-, and high-degree mode parameters from m-averaged solar oscillation power spectra of widely differing spectral resolution. This method, which we ca …
- A Machine Learning Approach for Dynamical Mass Measurements of Galaxy Clusters
- Ntampaka, M.; Trac, H.; Sutherland, D. J.; Battaglia, N.; Póczos, B.; Schneider, J.
We present a modern machine learning (ML) approach for cluster dynamical mass measurements that is a factor-of-two improvement over using a conventional scaling relation. Different methods are tested against a mock cluster catalog constructed using …
- Revisiting Spitzer Transit Observations with Independent Component Analysis: New Results for the GJ 436 System
- Morello, G.; Waldmann, I. P.; Tinetti, G.; Howarth, I. D.; Micela, G.; Allard, F.
We analyzed four Spitzer/IRAC observations at 3.6 and 4.5 μm of the primary transit of the exoplanet GJ 436b, by using blind source separation techniques. These observations are important for investigating the atmospheric composition of the planet …
- Autonomous Gaussian Decomposition
- Lindner, Robert R.; Vera-Ciro, Carlos; Murray, Claire E.; Stanimirović, Snežana; Babler, Brian; Heiles, Carl; Hennebelle, Patrick; Goss, W. M.; Dickey, John
We present a new algorithm, named Autonomous Gaussian Decomposition (AGD), for automatically decomposing spectra into Gaussian components. AGD uses derivative spectroscopy and machine learning to provide optimized guesses for the number of Gaussian …