Measuring Black Hole Spin using X-ray Reflection Spectroscopy

I review the current status of X-ray reflection (a.k.a. broad iron line) based black hole spin measurements. This is a powerful technique that allows us to measure robust black hole spins across the mass range, from the stellar-mass black holes in X-ray binaries to the supermassive black holes in active galactic nuclei. After describing the basic assumptions of this approach, I lay out the detailed methodology focusing on "best practices" that have been found necessary to obtain robust results. Reflecting my own biases, this review is slanted towards a discussion of supermassive black hole (SMBH) spin in active galactic nuclei (AGN). Pulling together all of the available XMM-Newton and Suzaku results from the literature that satisfy objective quality control criteria, it is clear that a large fraction of SMBHs are rapidly-spinning, although there are tentative hints of a more slowly spinning population at high (M>5*10^7Msun) and low (M<2*10^6Msun) mass. I also engage in a brief review of the spins of stellar-mass black holes in X-ray binaries. In general, reflection-based and continuum-fitting based spin measures are in agreement, although there remain two objects (GROJ1655-40 and 4U1543-475) for which that is not true. I end this review by discussing the exciting frontier of relativistic reverberation, particularly the discovery of broad iron line reverberation in XMM-Newton data for the Seyfert galaxies NGC4151, NGC7314 and MCG-5-23-16. As well as confirming the basic paradigm of relativistic disk reflection, this detection of reverberation demonstrates that future large-area X-ray observatories such as LOFT will make tremendous progress in studies of strong gravity using relativistic reverberation in AGN.Comment: 19 pages. To appear in proceedings of the ISSI-Bern workshop on "The Physics of Accretion onto Black Holes" (8-12 Oct 2012). Revised version adds a missing source to Table 1 and Fig.6 (IRAS13224-3809) and corrects the referencing of the discovery of soft lags in 1H0707-495 (which were in fact first reported in Fabian et al. 2009

A 78 Day X-Ray Period Detected from NGC 5907 ULX1 by Swift

We report the detection of a 78.1 ± 0.5 day period in the X-ray light curve of the extreme ultraluminous X-ray source NGC 5907 ULX1 (${L}_{{\rm{X,peak}}}\sim 5\times {10}^{40}$ erg s−1), discovered during an extensive monitoring program with Swift. These periodic variations are strong, with the observed flux changing by a factor of ~3–4 between the peaks and the troughs of the cycle; our simulations suggest that the observed periodicity is detected comfortably in excess of 3σ significance. We discuss possible origins for this X-ray period, but conclude that at the current time we cannot robustly distinguish between orbital and super-orbital variations

The Gaia-ESO Survey: radial metallicity gradients and age-metallicity relation of stars in the Milky Way disk

We study the relationship between age, metallicity, and alpha-enhancement of FGK stars in the Galactic disk. The results are based upon the analysis of high-resolution UVES spectra from the Gaia-ESO large stellar survey. We explore the limitations of the observed dataset, i.e. the accuracy of stellar parameters and the selection effects that are caused by the photometric target preselection. We find that the colour and magnitude cuts in the survey suppress old metal-rich stars and young metal-poor stars. This suppression may be as high as 97% in some regions of the age-metallicity relationship. The dataset consists of 144 stars with a wide range of ages from 0.5 Gyr to 13.5 Gyr, Galactocentric distances from 6 kpc to 9.5 kpc, and vertical distances from the plane 0 < |Z| < 1.5 kpc. On this basis, we find that i) the observed age-metallicity relation is nearly flat in the range of ages between 0 Gyr and 8 Gyr; ii) at ages older than 9 Gyr, we see a decrease in [Fe/H] and a clear absence of metal-rich stars; this cannot be explained by the survey selection functions; iii) there is a significant scatter of [Fe/H] at any age; and iv) [Mg/Fe] increases with age, but the dispersion of [Mg/Fe] at ages > 9 Gyr is not as small as advocated by some other studies. In agreement with earlier work, we find that radial abundance gradients change as a function of vertical distance from the plane. The [Mg/Fe] gradient steepens and becomes negative. In addition, we show that the inner disk is not only more alpha-rich compared to the outer disk, but also older, as traced independently by the ages and Mg abundances of stars.Comment: accepted for publication in A&

NuSTAR unveils a Compton-thick type 2 quasar in MrK 34

We present Nuclear Spectroscopic Telescope Array (NuSTAR) 3-40 keV observations of the optically selected Type 2 quasar (QSO2) SDSS J1034+6001 or Mrk 34. The high-quality hard X-ray spectrum and archival XMM-Newton data can be fitted self-consistently with a reflection-dominated continuum and a strong Fe K? fluorescence line with equivalent width &gt;1 keV. Prior X-ray spectral fitting below 10 keV showed the source to be consistent with being obscured by Compton-thin column densities of gas along the line of sight, despite evidence for much higher columns from multiwavelength data. NuSTAR now enables a direct measurement of this column and shows that N H lies in the Compton-thick (CT) regime. The new data also show a high intrinsic 2-10 keV luminosity of L 2-10 ~ 1044 erg s–1, in contrast to previous low-energy X-ray measurements where L 2-10 lesssim 1043 erg s–1 (i.e., X-ray selection below 10 keV does not pick up this source as an intrinsically luminous obscured quasar). Both the obscuring column and the intrinsic power are about an order of magnitude (or more) larger than inferred from pre-NuSTAR X-ray spectral fitting. Mrk 34 is thus a "gold standard" CT QSO2 and is the nearest non-merging system in this class, in contrast to the other local CT quasar NGC 6240, which is currently undergoing a major merger coupled with strong star formation. For typical X-ray bolometric correction factors, the accretion luminosity of Mrk 34 is high enough to potentially power the total infrared luminosity. X-ray spectral fitting also shows that thermal emission related to star formation is unlikely to drive the observed bright soft component below ~3 keV, favoring photoionization instead

Neonatal DNA methylation and childhood low prosocial behavior: an epigenome-wide association meta-analysis

Low prosocial behavior in childhood has been consistently linked to later psychopathology, with evidence supporting the influence of both genetic and environmental factors on its development. Although neonatal DNA methylation (DNAm) has been found to prospectively associate with a range of psychological traits in childhood, its potential role in prosocial development has yet to be investigated. This study investigated prospective associations between cord blood DNAm at birth and low prosocial behavior within and across four longitudinal birth cohorts from the Pregnancy And Childhood Epigenetics (PACE) Consortium. We examined (a) developmental trajectories of "chronic-low" versus "typical" prosocial behavior across childhood in a case-control design (N = 2,095), and (b) continuous "low prosocial" scores at comparable cross-cohort time-points (N = 2,121). Meta-analyses were performed to examine differentially methylated positions and regions. At the cohort-specific level, three CpGs were found to associate with chronic low prosocial behavior; however, none of these associations was replicated in another cohort. Meta-analysis revealed no epigenome-wide significant CpGs or regions. Overall, we found no evidence for associations between DNAm patterns at birth and low prosocial behavior across childhood. Findings highlight the importance of employing multi-cohort approaches to replicate epigenetic associations and reduce the risk of false positive discoveries

The Gaia-ESO Survey: Homogenisation of stellar parameters and elemental abundances

The Gaia-ESO Survey is a public spectroscopic survey that targeted ≳105 stars covering all major components of the Milky Way from the end of 2011 to 2018, delivering its final public release in May 2022. Unlike other spectroscopic surveys, Gaia-ESO is the only survey that observed stars across all spectral types with dedicated, specialised analyses: from O (Teff ~ 30 000–52 000 K) all the way to K-M (≳3500 K). The physics throughout these stellar regimes varies significantly, which has previously prohibited any detailed comparisons between stars of significantly different types. In the final data release (internal data release 6) of the Gaia-ESO Survey, we provide the final database containing a large number of products, such as radial velocities, stellar parameters and elemental abundances, rotational velocity, and also, for example, activity and accretion indicators in young stars and membership probability in star clusters for more than 114 000 stars. The spectral analysis is coordinated by a number of working groups (WGs) within the survey, each specialised in one or more of the various stellar samples. Common targets are analysed across WGs to allow for comparisons (and calibrations) amongst instrumental setups and spectral types. Here we describe the procedures employed to ensure all survey results are placed on a common scale in order to arrive at a single set of recommended results for use by all survey collaborators. We also present some general quality and consistency checks performed on the entirety of the survey results.This work was partly supported by the European Union FP7 programme through ERC grant number 320360 and by the Leverhulme Trust through grant RPG-2012-541. We acknowledge the support from INAF and Ministero dell’Istruzione, dell’Università e della Ricerca (MIUR) in the form of the grant “Premiale VLT 2012”. L. Magrini and M. Van der Swaelmen acknowledge support by the WEAVE Italian consortium, and by the INAF Grant “Checs”. A.J. Korn acknowledges support by the Swedish National Space Agency (SNSA). A. Lobel acknowledges support in part by the Belgian Federal Science Policy Office under contract no. BR/143/A2/BRASS and by the European Union Framework Programme for Research and Innovation Horizon 2020 (2014-2020) under the Marie Sklodowska-Curie grant Agreement No. 823734. D.K. Feuillet was partly supported by grant no. 2016-03412 from the Swedish Research Council. D. Montes acknowledges financial support from the Agencia Estatal de Investigacion of the Ministerio de Ciencia, Innovation through project PID2019-109522GB-C54 /AEI/10.13039/501100011033. E. Marfil acknowledges financial support from the European Regional Development Fund (ERDF) and the Gobierno de Canarias through project ProID2021010128. J.I. Gonzalez Hernandez acknowledges financial support from the Spanish Ministry of Science and Innovation (MICINN) project PID2020-117493GB-I00. M. Bergemann is supported through the Lise Meitner grant from the Max Planck Society and acknowledges support by the Collaborative Research centre SFB 881 (projects A5, A10), Heidelberg University, of the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation). This project has received funding from the European Research Council (ERC) under the European Union, Horizon 2020 research and innovation programme (Grant agreement No. 949173). P. Jofré acknowledges financial support of FONDECYT Regular 1200703 as well as Nucleo Mile-nio ERIS NCN2021_017. R. Smiljanic acknowledges support from the National Science Centre, Poland (2014/15/B/ST/03981). S.R. Berlanas acknowledges support by MCIN/AEI/10.13039/501100011033 (contract FJC 2020-045785-I) and NextGeneration EU/PRTR and MIU (UNI/551/2021) through grant Margarita Salas-ULL. T. Bensby acknowledges financial support by grant No. 2018-04857 from the Swedish Research Council. T. Merle is supported by a grant from the Foundation ULB. T. Morel are grateful to Belgian F.R.S.-FNRS for support, and are also indebted for an ESA/PRODEX Belspo contract related to the Gaia Data Processing and Analysis Consortium and for support through an ARC grant for Concerted Research Actions financed by the Federation Wallonie-Brussels. W. Santos acknowledges FAPERJ for a Ph.D. fellowship. H.M. Tabernero acknowledges financial support from the Agencia Estatal de Investigation of the Ministerio de Ciencia, Innovation through project PID2019-109522GB-C51/AEI/10.13039/501100011033

Crop Updates 2007 - Lupins, Pulses and Oilseeds

This session covers forty eight papers from different authors: 2006 REGIONAL ROUNDUP 1. South east agricultural region, Mark Seymour1 and Jacinta Falconer2, 1Department of Agriculture and Food, 2Cooperative Bulk Handling Group 2. Central agricultural region, Ian Pritchard, Department of Agriculture and Food 3. Great Southern and Lakes region, Rodger Beermier, Department of Agriculture and Food 4. Northern agricultural region, Wayne Parker and Martin Harries, Department of Agriculture and Food LUPINS 5. Development of anthracnose resistant and early flowering albus lupins (Lupinus albus L) in Western Australia, Kedar Adhikari and Geoff Thomas, Department of Agriculture and Food 6. New lupins adapted to the south coast, Peter White, Bevan Buirchell and Mike Baker, Department of Agriculture and Food 7. Lupin species and row spacing interactions by environment, Martin Harries, Peter White, Bob French, Jo Walker, Mike Baker and Laurie Maiolo, Department of Agriculture and Food 8. The interaction of lupin species row spacing and soil type, Martin Harries, Bob French, Laurie Maiolo and Jo Walker, Department of Agriculture and Food 9. The effects of row spacing and crop density on competitiveness of lupins with wild radish, Bob French and Laurie Maiolo, Department of Agriculture and Food 10. The effect of time of sowing and radish weed density on lupin yield, Martin Harries and Jo Walker, Department of Agriculture and Food 11. Interaction of time of sowing and weed management in lupins, Martin Harries and Jo Walker, Department of Agriculture and Food 12. Delayed sowing as a strategy to manage annual ryegrass, Bob French and Laurie Maiolo, Department of Agriculture and Food 13. Is delayed sowing a good strategy for weed management in lupins? Bob French, Department of Agriculture and Food 14. Lupins aren’t lupins when it comes to simazine, Peter White and Leigh Smith, Department of Agriculture and Food 15. Seed yield and anthracnose resistance of Tanjil mutants tolerant to metribuzin, Ping Si1, Bevan Buirchell1,2 and Mark Sweetingham1,2, 1Centre for Legumes in Mediterranean Agriculture, Australia; 2Department of Agriculture and Food 16. The effect of herbicides on nodulation in lupins, Lorne Mills1, Harmohinder Dhammu2 and Beng Tan1, 1Curtin University of Technology and 2Department of Agriculture and Food 17. Effect of fertiliser placements and watering regimes on lupin growth and seed yield in the central grain belt of Western Australia, Qifu Ma1, Zed Rengel1, Bill Bowden2, Ross Brennan2, Reg Lunt2 and Tim Hilder2, 1Soil Science & Plant Nutrition UWA, 2Department of Agriculture and Food 18. Development of a forecasting model for Bean Yellow Mosaic Virus in lupins, T. Maling1,2, A. Diggle1, D. Thackray1,2, R.A.C. Jones2, and K.H.M. Siddique1, 1Centre for Legumes in Mediterranean Agriculture, The University of Western Australia; 2Department of Agriculture and Food 19. Manufacturing of lupin tempe,Vijay Jayasena1,4, Leonardus Kardono2,4, Ken Quail3,4 and Ranil Coorey1,4, 1Curtin University of Technology, Perth, Australia, 2Indonesian Institute of Sciences (LIPI), Indonesia, 3BRI Australia Ltd, Sydney, Australia, 4Grain Foods CRC, Sydney, Australia 20. The impact of lupin based ingredients in ice-cream, Hannah Williams, Lee Sheer Yap and Vijay Jayasena, Curtin University of Technology, Perth WA 21. The acceptability of muffins substituted with varying concentrations of lupin flour, Anthony James, Don Elani Jayawardena and Vijay Jayasena, Curtin University of Technology, PerthWA PULSES 22. Chickpea variety evaluation, Kerry Regan1, Rod Hunter1, Tanveer Khan1,2and Jenny Garlinge1, 1Department of Agriculture and Food, 2CLIMA, The University of Western Australia 23. Advanced breeding trials of desi chickpea, Khan, T.N.1, Siddique, K.H.M.3, Clarke, H.2, Turner, N.C.2, MacLeod, W.1, Morgan, S.1, and Harris, A.1, 1Department of Agriculture and Food, 2Centre for Legumes in Mediterranean Agriculture, 3TheUniversity of Western Australia 24. Ascochyta resistance in chickpea lines in Crop Variety Testing (CVT) of 2006, Tanveer Khan1 2, Bill MacLeod1, Alan Harris1, Stuart Morgan1and Kerry Regan1, 1Department of Agriculture and Food, 2CLIMA, The University of Western Australia 25. Yield evaluation of ascochyta blight resistant Kabuli chickpeas, Kerry Regan1and Kadambot Siddique2, 1Department of Agriculture and Food, 2Institute of Agriculture, The University of Western Australia 26. Pulse WA Chickpea Industry Survey 2006, Mark Seymour1, Ian Pritchard1, Wayne Parker1and Alan Meldrum2, 1Department of Agriculture and Food, 2Pulse Australia 27. Genes from the wild as a valuable genetic resource for chickpea improvement, Heather Clarke1, Helen Bowers1and Kadambot Siddique2, 1Centre for Legumes in Mediterranean Agriculture, 2Institute of Agriculture, The University of Western Australia 28. International screening of chickpea for resistance to Botrytis grey mould, B. MacLeod1, Dr T. Khan1, Prof. K.H.M. Siddique2and Dr A. Bakr3, 1Department of Agriculture and Food, 2The University of Western Australia, 3Bangladesh Agricultural Research Institute 29. Balance® in chickpea is safest applied post sowing to a level seed bed, Wayne Parker, Department of Agriculture and Food, 30. Demonstrations of Genesis 510 chickpea, Wayne Parker, Department of Agriculture and Food 31. Field pea 2006, Ian Pritchard, Department of Agriculture and Food 32. Field pea variety evaluation, Kerry Regan1, Rod Hunter1, Tanveer Khan1,2 and Jenny Garlinge1, 1Department of Agriculture and Food, 2CLIMA, The University of Western Australia 33. Breeding highlights of the Australian Field Pea Improvement Program (AFPIP),Kerry Regan1, Tanveer Khan1,2, Phillip Chambers1, Chris Veitch1, Stuart Morgan1 , Alan Harris1and Tony Leonforte3, 1Department of Agriculture and Food, 2CLIMA, The University of Western Australia, 3Department of Primary Industries, Victoria 34. Field pea germplasm enhancement for black spot resistance, Tanveer Khan, Kerry Regan, Stuart Morgan, Alan Harris and Phillip Chambers, Department of Agriculture and Food 35. Validation of Blackspot spore release model and testing moderately resistant field pea line, Mark Seymour, Ian Pritchard, Rodger Beermier, Pam Burgess and Leanne Young, Department of Agriculture and Food 36. Yield losses from sowing field pea seed infected with Pea Seed-borne Mosaic Virus, Brenda Coutts, Donna O’Keefe, Rhonda Pearce, Monica Kehoe and Roger Jones, Department of Agriculture and Food 37. Faba bean in 2006, Mark Seymour, Department of Agriculture and Food 38. Germplasm evaluation – faba bean, Mark Seymour1, Terri Jasper1, Ian Pritchard1, Mike Baker1 and Tim Pope1,2, 1Department of Agriculture and Food, , 2CLIMA, The University of Western Australia 39. Breeding highlights of the Coordinated Improvement Program for Australian Lentils (CIPAL), Kerry Regan1, Chris Veitch1, Phillip Chambers1 and Michael Materne2, 1Department of Agriculture and Food, 2Department of Primary Industries, Victoria 40. Screening pulse lentil germplasm for tolerance to alternate herbicides, Ping Si1, Mike Walsh2 and Mark Sweetingham1,3, 1Centre for Legumes in Mediterranean Agriculture, 2West Australian Herbicide Resistance Initiative, 3Department of Agriculture and Food 41. Genomic synteny in legumes: Application to crop breeding, Phan, H.T.T.1, Ellwood, S.R.1, Hane, J.1, Williams, A.1, Ford, R.2, Thomas, S.3 and Oliver R1, 1Australian Centre of Necrotrophic Plant Pathogens, Murdoch University, 2BioMarka, University of Melbourne, 3NSW Department of Primary Industries 42. Tolerance of lupins, chickpeas and canola to Balanceâ(Isoxaflutole) and Galleryâ (Isoxaben), Leigh Smith and Peter White, Department of Agriculture and Food CANOLA AND OILSEEDS 43. The performance of TT Canola varieties in the National Variety Test (NVT),WA,2006,Katie Robinson, Research Agronomist, Agritech Crop Research 44. Evaluation of Brassica crops for biodiesel in Western Australia, Mohammad Amjad, Graham Walton, Pat Fels and Andy Sutherland, Department of Agriculture and Food 45. Production risk of canola in different rainfall zones in Western Australia, Imma Farré1, Michael Robertson2 and Senthold Asseng3, 1Department of Agriculture and Food, 2CSIRO Sustainable Ecosystems, 3CSIRO Plant Industry 46. Future directions of blackleg management – dynamics of blackleg susceptibility in canola varieties, Ravjit Khangura, Moin Salam and Bill MacLeod, Department of Agriculture and Food 47. Appendix 1: Contributors 48. Appendix 2: List of common acronym