Microwave observations of spinning dust emission in NGC6946

We report new cm-wave measurements at five frequencies between 15 and 18GHz of the continuum emission from the reportedly anomalous "region 4" of the nearby galaxy NGC6946. We find that the emission in this frequency range is significantly in excess of that measured at 8.5GHz, but has a spectrum from 15-18GHz consistent with optically thin free-free emission from a compact HII region. In combination with previously published data we fit four emission models containing different continuum components using the Bayesian spectrum analysis package radiospec. These fits show that, in combination with data at other frequencies, a model with a spinning dust component is slightly preferred to those that possess better-established emission mechanisms.Comment: submitted MNRA

High resolution AMI Large Array imaging of spinning dust sources: spatially correlated 8 micron emission and evidence of a stellar wind in L675

We present 25 arcsecond resolution radio images of five Lynds Dark Nebulae (L675, L944, L1103, L1111 & L1246) at 16 GHz made with the Arcminute Microkelvin Imager (AMI) Large Array. These objects were previously observed with the AMI Small Array to have an excess of emission at microwave frequencies relative to lower frequency radio data. In L675 we find a flat spectrum compact radio counterpart to the 850 micron emission seen with SCUBA and suggest that it is cm-wave emission from a previously unknown deeply embedded young protostar. In the case of L1246 the cm-wave emission is spatially correlated with 8 micron emission seen with Spitzer. Since the MIR emission is present only in Spitzer band 4 we suggest that it arises from a population of PAH molecules, which also give rise to the cm-wave emission through spinning dust emission.Comment: accepted MNRA

Follow-up observations at 16 and 33 GHz of extragalactic sources from WMAP 3-year data: I - Spectral properties

We present follow-up observations of 97 point sources from the Wilkinson Microwave Anisotropy Probe (WMAP) 3-year data, contained within the New Extragalactic WMAP Point Source (NEWPS) catalogue between declinations of -4 and +60 degrees; the sources form a flux-density-limited sample complete to 1.1 Jy (approximately 5 sigma) at 33 GHz. Our observations were made at 16 GHz using the Arcminute Microkelvin Imager (AMI) and at 33 GHz with the Very Small Array (VSA). 94 of the sources have reliable, simultaneous -- typically a few minutes apart -- observations with both telescopes. The spectra between 13.9 and 33.75 GHz are very different from those of bright sources at low frequency: 44 per cent have rising spectra (alpha < 0.0), where flux density is proportional to frequency^-alpha, and 93 per cent have spectra with alpha < 0.5; the median spectral index is 0.04. For the brighter sources, the agreement between VSA and WMAP 33-GHz flux densities averaged over sources is very good. However, for the fainter sources, the VSA tends to measure lower values for the flux densities than WMAP. We suggest that the main cause of this effect is Eddington bias arising from variability.Comment: 12 pages, 13 figures, submitted to MNRA

AMI observations of unmatched Planck ERCSC LFI sources at 15.75 GHz

The Planck Early Release Compact Source Catalogue includes 26 sources with no obvious matches in other radio catalogues (of primarily extragalactic sources). Here we present observations made with the Arcminute Microkelvin Imager Small Array (AMI SA) at 15.75 GHz of the eight of the unmatched sources at declination > +10 degrees. Of the eight, four are detected and are associated with known objects. The other four are not detected with the AMI SA, and are thought to be spurious.Comment: 6 pages, 5 figures, 4 table

AMI observations of Lynds Dark Nebulae: further evidence for anomalous cm-wave emission

Observations at 14.2 to 17.9 GHz made with the AMI Small Array towards fourteen Lynds Dark Nebulae with a resolution of 2' are reported. These sources are selected from the SCUBA observations of Visser et al. (2001) as small angular diameter clouds well matched to the synthesized beam of the AMI Small Array. Comparison of the AMI observations with radio observations at lower frequencies with matched uv-plane coverage is made, in order to search for any anomalous excess emission which can be attributed to spinning dust. Possible emission from spinning dust is identified as a source within a 2' radius of the Scuba position of the Lynds dark nebula, exhibiting an excess with respect to lower frequency radio emission. We find five sources which show a possible spinning dust component in their spectra. These sources have rising spectral indices in the frequency range 14.2--17.9 GHz. Of these five one has already been reported, L1111, we report one new definite detection, L675, and three new probable detections (L944, L1103 and L1246). The relative certainty of these detections is assessed on the basis of three criteria: the extent of the emission, the coincidence of the emission with the Scuba position and the likelihood of alternative explanations for the excess. Extended microwave emission makes the likelihood of the anomalous emission arising as a consequence of a radio counterpart to a protostar or a proto-planetary disk unlikely. We use a 2' radius in order to be consistent with the IRAS identifications of dark nebulae (Parker 1988), and our third criterion is used in the case of L1103 where a high flux density at 850 microns relative to the FIR data suggests a more complicated emission spectrum.Comment: submitted MNRA

Statistical and machine learning methods evaluated for incorporating soil and weather into corn nitrogen recommendations

Nitrogen (N) fertilizer recommendation tools could be improved for estimating corn (Zea mays L.) N needs by incorporating site-specific soil and weather information. However, an evaluation of analytical methods is needed to determine the success of incorporating this information. The objectives of this research were to evaluate statistical and machine learning (ML) algorithms for utilizing soil and weather information for improving corn N recommendation tools. Eight algorithms [stepwise, ridge regression, least absolute shrinkage and selection operator (Lasso), elastic net regression, principal component regression (PCR), partial least squares regression (PLSR), decision tree, and random forest] were evaluated using a dataset containing measured soil and weather variables from a regional database. The performance was evaluated based on how well these algorithms predicted corn economically optimal N rates (EONR) from 49 sites in the U.S. Midwest. Multiple algorithm modeling scenarios were examined with and without adjustment for multicollinearity and inclusion of two-way interaction terms to identify the soil and weather variables that could improve three dissimilar N recommendation tools. Results showed the out-of-sample root-mean-square error (RMSE) for the decision tree and some random forest modeling scenarios were better than the stepwise or ridge regression, but not significantly different than any other algorithm. The best ML algorithm for adjusting N recommendation tools was the random forest approach (r2 increased between 0.72 and 0.84 and the RMSE decreased between 41 and 94 kg N ha−1). However, the ML algorithm that best adjusted tools while using a minimal amount of variables was the decision tree. This method was simple, needing only one or two variables (regardless of modeling scenario) and provided moderate improvement as r2 values increased between 0.15 and 0.51 and RMSE decreased between 16 and 66 kg N ha−1. Using ML algorithms to adjust N recommendation tools with soil and weather information shows promising results for better N management in the U.S. Midwest

Statistical and machine learning methods evaluated for incorporating soil and weather into corn nitrogen recommendations

Nitrogen (N) fertilizer recommendation tools could be improved for estimating corn (Zea mays L.) N needs by incorporating site-specific soil and weather information. However, an evaluation of analytical methods is needed to determine the success of incorporating this information. The objectives of this research were to evaluate statistical and machine learning (ML) algorithms for utilizing soil and weather information for improving corn N recommendation tools. Eight algorithms [stepwise, ridge regression, least absolute shrinkage and selection operator (Lasso), elastic net regression, principal component regression (PCR), partial least squares regression (PLSR), decision tree, and random forest] were evaluated using a dataset containing measured soil and weather variables from a regional database. The performance was evaluated based on how well these algorithms predicted corn economically optimal N rates (EONR) from 49 sites in the U.S. Midwest. Multiple algorithm modeling scenarios were examined with and without adjustment for multicollinearity and inclusion of two-way interaction terms to identify the soil and weather variables that could improve three dissimilar N recommendation tools. Results showed the out-of-sample root-mean-square error (RMSE) for the decision tree and some random forest modeling scenarios were better than the stepwise or ridge regression, but not significantly different than any other algorithm. The best ML algorithm for adjusting N recommendation tools was the random forest approach (r2 increased between 0.72 and 0.84 and the RMSE decreased between 41 and 94 kg N ha−1). However, the ML algorithm that best adjusted tools while using a minimal amount of variables was the decision tree. This method was simple, needing only one or two variables (regardless of modeling scenario) and provided moderate improvement as r2 values increased between 0.15 and 0.51 and RMSE decreased between 16 and 66 kg N ha−1. Using ML algorithms to adjust N recommendation tools with soil and weather information shows promising results for better N management in the U.S. Midwest