Selection of AGN candidates in the GOODS-South Field through SPITZER/MIPS 24 μ\mum variability

We present a study of galaxies showing mid-infrared variability in data taken in the deepest Spitzer/MIPS 24 $\mu$m surveys in the GOODS-South field. We divide the dataset in epochs and subepochs to study the long-term (months-years) and the short-term (days) variability. We use a $\chi^2$-statistics method to select AGN candidates with a probability $\leq$ 1% that the observed variability is due to statistical errors alone. We find 39 (1.7% of the parent sample) sources that show long-term variability and 55 (2.2% of the parent sample) showing short-term variability. That is, 0.03 sources $\times$ arcmin$^{-2}$ for both, long-term and short-term variable sources. After removing the expected number of false positives inherent to the method, the estimated percentages are 1.0% and 1.4% of the parent sample for the long-term and short-term respectively. We compare our candidates with AGN selected in the X-ray and radio bands, and AGN candidates selected by their IR emission. Approximately, 50% of the MIPS 24 $\mu$m variable sources would be identified as AGN with these other methods. Therefore, MIPS 24 $\mu$m variability is a new method to identify AGN candidates, possibly dust obscured and low luminosity AGN, that might be missed by other methods. However, the contribution of the MIPS 24 $\mu$m variable identified AGN to the general AGN population is small ($\leq$ 13%) in GOODS-South.Comment: Accepted for publication in MNRA

Low-resolution spectroscopy and spectral energy distributions of selected sources towards sigma Orionis

Aims: We investigated in detail nine sources in the direction of the young sigma Orionis cluster, which is considered a unique site for studying stellar and substellar formation. The nine sources were selected because of some peculiar properties, such as extremely red infrared colours or too strong Halpha emission for their blue optical colours. Methods: We took high-quality, low-resolution spectroscopy (R ~ 500) of the nine targets with ALFOSC at the Nordic Optical Telescope. We also re-analyzed [24]-band photometry from MIPS/Spitzer and compiled the best photometry available at the ViJHKs passbands and the four IRAC/Spitzer channels for constructing accurate spectral energy distributions covering from 0.55 to 24 mum. Results: The nine targets were classified into: one Herbig Ae/Be star with a scatterer edge-on disc, two G-type stars, one X-ray flaring, early-M, young star with chromospheric Halpha emission, one very low-mass, accreting, young spectroscopic binary, two young objects at the brown dwarf boundary with the characteristics of classical T Tauri stars, and two emission-line galaxies, one undergoing star formation, and another one whose spectral energy distribution is dominated by an active galactic nucleus. Besides, we discover three infrared sources associated to overdensities in a cold cloud in the cluster centre. Conclusions: Low-resolution spectroscopy and spectral energy distributions are a vital tool for measuring the physical properties and the evolution of young stars and candidates in the sigma Orionis cluster.Comment: Accepted for publication in A&

Subspace averaging for source enumeration in large arrays

Subspace averaging is proposed and examined as a method of enumerating sources in large linear arrays, under conditions of low sample support. The key idea is to exploit shift invariance as a way of extracting many subspaces, which may then be approximated by a single extrinsic average. An automatic order determination rule for this extrinsic average is then the rule for determining the number of sources. Experimental results are presented for cases where the number of array snapshots is roughly half the number of array elements, and sources are well separated with respect to the Rayleigh limit.The work of I. Santamaría has been partially supported by the Ministerio de Economía y Competitividad (MINECO) of Spain, and AEI/FEDER funds of the E.U., under grant TEC2016-75067-C4-4-R (CARMEN). The work of D. Ramírez has been partly supported by Ministerio de Economía of Spain under projects: OTOSIS (TEC2013-41718-R) and the COMONSENS Network (TEC2015-69648-REDC), by the Ministerio de Economía of Spain jointly with the European Commission (ERDF) under projects ADVENTURE (TEC2015-69868-C2-1-R) and CAIMAN (TEC2017-86921- C2-2-R), and by the Comunidad de Madrid under project CASI-CAM-CM (S2013/ICE-2845). The work of L. L. Scharf was supported by the National Science Foundation (NSF) under grant CCF-1018472

Scale-invariant subspace detectors based on first- and second-order statistical models

The problem is to detect a multi-dimensional source transmitting an unknown sequence of complex-valued symbols to a multi-sensor array. In some cases the channel subspace is known, and in others only its dimension is known. Should the unknown transmissions be treated as unknowns in a first-order statistical model, or should they be assigned a prior distribution that is then used to marginalize a first-order model for a second-order statistical model? This question motivates the derivation of subspace detectors for cases where the subspace is known, and for cases where only the dimension of the subspace is known. For three of these four models the GLR detectors are known, and they have been reported in the literature. But the GLR detector for the case of a known subspace and a second-order model for the measurements is derived for the first time in this paper. When the subspace is known, second-order generalized likelihood ratio (GLR) tests outperform first-order GLR tests when the spread of subspace eigenvalues is large, while first-order GLR tests outperform second-order GLR tests when the spread is small. When only the dimension of the subspace is known, second-order GLR tests outperform first-order GLR tests, regardless of the spread of signal subspace eigenvalues. For a dimension-1 source, first-order and second-order statistical models lead to equivalent GLR tests. This is a new finding.The work by I. Santamaria was supported by the Ministerio de Ciencia e Innovación of Spain, and AEI/FEDER funds of the E.U., under Grants TEC2016-75067-C4-4-R (CARMEN) and PID2019-104958RB-C43 (ADELE). The work by Louis Scharf is supported by the Air Force Office of Scientific Research under contract FA9550-18-1-0087, and by the National Science Foundation (NSF) under contract CCF-1712788. The work of David Ramírez was supported by the Ministerio de Ciencia, Innovación y Universidades under grant TEC2017-92552-EXP (aMBITION), by the Ministerio de Ciencia, Innovación y Universidades, jointly with the European Commission (ERDF), under Grant TEC2017-86921-C2-2-R (CAIMAN), and by The Comunidad de Madrid under grant Y2018/TCS-4705 (PRACTICO-CM)

Subspace averaging and order determination for source enumeration

In this paper, we address the problem of subspace averaging, with special emphasis placed on the question of estimating the dimension of the average. The results suggest that the enumeration of sources in a multi-sensor array, which is a problem of estimating the dimension of the array manifold, and as a consequence the number of radiating sources, may be cast as a problem of averaging subspaces. This point of view stands in contrast to conventional approaches, which cast the problem as one of identifiying covariance models in a factor model. We present a robust formulation of the proposed order fitting rule based on majorization-minimization algorithms. A key element of the proposed method is to construct a bootstrap procedure, based on a newly proposed discrete distribution on the manifold of projection matrices, for stochastically generating subspaces from a function of experimentally determined eigenvalues. In this way, the proposed subspace averaging (SA) technique determines the order based on the eigenvalues of an average projection matrix, rather than on the likelihood of a covariance model, penalized by functions of the model order. By means of simulation examples, we show that the proposed SA criterion is especially effective in high-dimensional scenarios with low sample support.The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Yuejie Chi. The work of V. Garg and I. Santamaria was supported in part by the Ministerio de Economía y Competitividad (MINECO) of Spain, and in part by the AEI/FEDER funds of the E.U., under Grants TEC2016-75067-C4-4-R (CARMEN), TEC2015-69648-REDC, and BES-2017-080542. The work of D. Ramírez was supported in part by the Ministerio de Ciencia, Innovación y Universidades under Grant TEC2017-92552-EXP (aMBITION), in part by the Ministerio de Ciencia, Innovación y Universidades, jointly with the European Commission (ERDF), under Grants TEC2015-69868-C2-1-R (ADVENTURE) and TEC2017-86921-C2-2-R (CAIMAN), and in part by The Comunidad de Madrid under Grant Y2018/TCS-4705 (PRACTICOCM). The work of L. L. Scharf was supported in part by the U.S. NSF under Contract CISE-1712788

An alternating optimization algorithm for two-channel factor analysis with common and uncommon factors

An alternating optimization algorithm is presented and analyzed for identifying low-rank signal components, known in factor analysis terminology as common factors, that are correlated across two multiple-input multiple-output (MIMO) channels. The additive noise model at each of the MIMO channels consists of white uncorrelated noises of unequal variances plus a low-rank structured interference that is not correlated across the two channels. The low-rank components at each channel represent uncommon or channel-specific factors.The work of D. Ram´ırez was supported by the Ministerio de Economía, Industria y Competitividad (MINECO) and AEI/FEDER funds of the E.U., under grants TEC2013- 41718-R (OTOSIS), TEC2015-69648-REDC (COMONSENS Network), TEC2015-69868-C2-1-R (ADVENTURE), and CAIMAN (TEC2017-86921-C2-2-R) and The Comunidad de Madrid under grant S2013/ICE-2845 (CASI-CAM-CM). The work of I. Santamaria and S. Van Vaerenbergh was supported by MINECO and AEI/FEDER funds of the E.U., under grant TEC2016-75067-C4-4-R (CARMEN). The work of L. Scharf was supported in part by the National Science Foundation under grant CCF-1712788

Multi-channel factor analysis with common and unique factors

This work presents a generalization of classical factor analysis (FA). Each of M channels carries measurements that share factors with all other channels, but also contains factors that are unique to the channel. Furthermore, each channel carries an additive noise whose covariance is diagonal, as is usual in factor analysis, but is otherwise unknown. This leads to a problem of multi-channel factor analysis with a specially structured covariance model consisting of shared low-rank components, unique low-rank components, and diagonal components. Under a multivariate normal model for the factors and the noises, a maximum likelihood (ML) method is presented for identifying the covariance model, thereby recovering the loading matrices and factors for the shared and unique components in each of the M multiple-input multipleoutput (MIMO) channels. The method consists of a three-step cyclic alternating optimization, which can be framed as a block minorization-maximization (BMM) algorithm. Interestingly, the three steps have closed-form solutions and the convergence of the algorithm to a stationary point is ensured. Numerical results demonstrate the performance of the proposed algorithm and its application to passive radar.The work of D. Ramírez was supported in part by the Ministerio de Ciencia, Innovación y Universidades under Grant TEC2017-92552-EXP (aMBITION), in part by the Ministerio de Ciencia, Innovación y Universidades, jointly with the European Commission (ERDF), under Grant TEC2017-86921-C2-2-R (CAIMAN), and in part by The Comunidad de Madrid under Grant Y2018/TCS-4705 (PRACTICO-CM). The work of I. Santamaria and S. Van Vaerenbergh was supported by Ministerio de Ciencia, Innovación y Universidades and AEI/FEDER funds of the E.U. under Grant TEC2016-75067-C4-4-R (CARMEN). The work of L. L. Scharf was supported by National Science Foundation under Grant CCF-1712788

Incidence, prevalence and persistence of bovine venereal diseases in La Pampa (Argentina): estimations for the period 2007 - 2020

The venereal diseases Bovine Trichomoniasis (BT) and Bovine Genital Campylobacteriosis (BGC) cause economic losses in endemic areas, such as the province of La Pampa in Argentina, where bovine production is typically extensive. This study has used data compiled from 2007 to 2013 by the Official Program for the Control and Eradication (PCE) of venereal diseases, aiming to determine the prevalence, incidence and persistence of BT and BGC and to provide projections until 2020. Fourteen univariate models were used to adjust each time series. The prevalence and incidence of both diseases have significantly decreased during the studied period, while the persistence has remained constant. The prevalence of BT has diminished from 7.48% in 2007 to 3.03% in 2013, while the prevalence of BGC has diminished from 9.36% to 3.15%. The incidences have been reduced to an annual average of 0.60 for BT and 0.67 for BGC. Although the estimation models are not able to predict accurately the future epidemiologic rates of BT and BGC in La Pampa, projections show a significant decreasing trend of the prevalence and incidence of BT and BGC. The persistence of BGC is expected to remain close to the 2007-2013 average, while the persistence of BT did not adjust to any of the 14 models used. These results indicate that PCE has been effective to reduce the infection of disease-free herds. However, in order to reduce the ratio of persistent herds, other preventive and management measures should be considered. Highlights The venereal diseases Bovine Trichomoniasis (BT) and Bovine Genital Campylobacteriosis (BGC) cause economic losses. Univariate analysis was an effective tool for modeling the historical and future prevalence, incidence and persistence of BT and BGC infections. The prevalence and incidence of BT and BGC have significantly decreased during the studied period, while the persistence has remained constant. The Official Program for the Control and Eradication (PCE) of venereal diseases, has been effective to reduce the infection of disease-free herds.The venereal diseases Bovine Trichomoniasis (BT) and Bovine Genital Campylobacteriosis (BGC) cause economic losses in endemic areas, such as the province of La Pampa in Argentina, where bovine production is typically extensive. This study has used data compiled from 2007 to 2013 by the Official Program for the Control and Eradication (PCE) of venereal diseases, aiming to determine the prevalence, incidence and persistence of BT and BGC and to provide projections until 2020. Fourteen univariate models were used to adjust each time series. The prevalence and incidence of both diseases have significantly decreased during the studied period, while the persistence has remained constant. The prevalence of BT has diminished from 7.48% in 2007 to 3.03% in 2013, while the prevalence of BGC has diminished from 9.36% to 3.15%. The incidences have been reduced to an annual average of 0.60 for BT and 0.67 for BGC. Although the estimation models are not able to predict accurately the future epidemiologic rates of BT and BGC in La Pampa, projections show a significant decreasing trend of the prevalence and incidence of BT and BGC. The persistence of BGC is expected to remain close to the 2007-2013 average, while the persistence of BT did not adjust to any of the 14 models used. These results indicate that PCE has been effective to reduce the infection of disease-free herds. However, in order to reduce the ratio of persistent herds, other preventive and management measures should be considered. Highlights The venereal diseases Bovine Trichomoniasis (BT) and Bovine Genital Campylobacteriosis (BGC) cause economic losses. Univariate analysis was an effective tool for modeling the historical and future prevalence, incidence and persistence of BT and BGC infections. The prevalence and incidence of BT and BGC have significantly decreased during the studied period, while the persistence has remained constant. The Official Program for the Control and Eradication (PCE) of venereal diseases, has been effective to reduce the infection of disease-free herds

The substellar mass function in the central region of the open cluster Praesepe from deep LBT observations

Studies of the mass function (MF) of open clusters of different ages allow us to probe the efficiency with which brown dwarfs (BDs) are evaporated from clusters to populate the field. Surveys in old clusters (age > 100 Myr) do not suffer so severely from several problems encountered in young clusters, such as intra-cluster extinction and large uncertainties in BD models. Here we present the results of a deep photometric survey to study the MF of the old open cluster Praesepe (age 590 Myr and distance 190 pc), down to a 5 sigma detection limit at i~25.6 mag (~40M_Jup). We identify 62 cluster member candidates, of which 40 are substellar, from comparison with predictions from a dusty atmosphere model. The MF rises from the substellar boundary until ~60M_Jup and then declines. This is quite different from the form inferred for other open clusters older than 50 Myr, but seems to be similar to those found in very young open cluster, whose MFs peak at ~10M_Jup. Either Praesepe really does have a different MF from other clusters or they had similar initial MFs but have differed in their dynamical evolution. We further have identified six foreground T dwarf candidates towards Praesepe, which require follow-up spectroscopy to confirm their nature.Comment: 8 pages, 5 figures, to appear in the online proceedings of the Cool Stars 16 conferenc

An asymptotic LMPI test for cyclostationarity detection with application to cognitive radio

We propose a new detector of primary users in cognitive radio networks. The main novelty of the proposed detector in comparison to most known detectors is that it is based on sound statistical principles for detecting cyclostationary signals. In particular, the proposed detector is (asymptotically) the locally most powerful invariant test, i.e. the best invariant detector for low signal-to-noise ratios. The derivation is based on two main ideas: the relationship between a scalar-valued cyclostationary signal and a vector-valued wide-sense stationary signal, and Wijsman's theorem. Moreover, using the spectral representation for the cyclostationary time series, the detector has an insightful interpretation, and implementation, as the broadband coherence between frequencies that are separated by multiples of the cycle frequency. Finally, simulations confirm that the proposed detector performs better than previous approaches.The work of P. Schreier was supported by the Alfried Krupp von Bohlen und Halbach Foundation, under its program “Return of German scientists from abroad”. The work of I. Santamaría and J. Vía was supported by the Spanish Government, Ministerio de Ciencia e Innovación (MICINN), under project RACHEL (TEC2013-47141-C4-3-R). The work of L. Scharf was supported by the Airforce Office of Scientific Research under contract FA9550-10-1-0241