We present an exploration of the Milky Way's structural parameters using an all-sky sample of red clump (RC) giants to map the stellar density from the Galactic disc beyond 3 kpc. These evolved giants are considered to be standard candles due to their low intrinsic variance in their absolute luminosities, and this allows us to estimate their distances with reasonable confidence. We exploited all-sky photometry from the AllWISE mid-infrared survey and the Gaia survey along with astrometry from Gaia Data Release 3 and recent 3D extinction maps to develop a probabilistic scheme in order to select with high confidence RC-like stars. Our curated catalogue contains about ten million sources, for which we estimated photometric distances based on the WISE W1 photometry. We derived the selection function for our sample, which is the combined selection function of sources with both Gaia and AllWISE photometry. Using the distances and accounting for the full selection function of our observables, we were able to fit a two-disc, multi-parameter model to constrain the scale height (h(z)), scale length (R-d), flaring, and the relative mass ratios of the two-disc components. We illustrate and verify our methodology using mock catalogues of RC stars. We find that the RC population is best described by a flared disc with scale length R-d=4.24 +/- 0.32 kpc and scale height at the Sun of h(z,circle dot)=0.18 +/- 0.01 kpc, and a shorter and thicker disc with R-d=2.66 +/- 0.11 kpc, h(z,circle dot)=0.48 +/- 0.11 kpc, with no flare. The thicker disc constitutes 66% of the RC stellar mass beyond 3 kpc, while the flared disc shows evidence of being warped beyond 9 kpc from the Galactic centre. The residuals between the predicted number density of RC stars from our axisymmetric model and the measured counts show possible evidence of a two-armed spiral perturbation in the disc of the Milky Way.

The Galactic Centre (GC) region is a highly interesting region for very high energy gamma-ray studies due to its proximity and diverse sources. It is also a unique place for Dark Matter (DM) searches, since we expect a large amount of DM in this region and it is nearby. Currently a new detector is under development to observe the GC region, called the Southern Wide field-of-view Gamma-ray Observatory (SWGO). This instrument will be the first water Cherenkov detector located in the Southern Hemisphere sensitive to >100 GeV gamma rays. In our work, we are going to present a simulation of the GC gamma-ray mission as seen by a SWGO-like observatory. To do so we are using the models published by the Cherenkov Telescope Array (CTA) Collaboration together with simulated Instrument Response Functions (IRFs) for testing purposes. This also allows us to predict the expected sensitivity of SWGO to WIMP DM annihilations using a template based method. A sensitivity study during the design phase of the observatory is important in order to choose the best detector design to get outstanding physics results.

This work aims to study the very-high-energy (VHE; 100 GeV - 100 TeV) gamma-ray emission from M 87, especially its low state emission, and probe a potential hadronic emission component in the inner Virgo Cluster. Probing a steady and extended gamma-ray signal around M 87 allows us to investigate the AGN feedback as a heating mechanism in the Virgo Cluster. We used High Energy Stereoscopic System (H.E.S.S.) observations of M 87 from 2004 to 2021 to study the source. We utilized the Bayesian block technique to identify M 87 emission states and isolate its low state. We fitted the morphology of the 120 h low state data and found no significant gamma-ray extension. We derived an upper limit on the extension that for the first time excludes the radio lobes ( ≈ 30 kpc) as the main component of the VHE gamma-ray emission from the low state of M 87. The VHE gamma-ray emission from the distinct source states of M 87 are compatible with each other and with the radio core of M 87. Based on two different models we constrained the maximum cosmic-ray to thermal pressure ratio and the total energy in CRp in the inner 20 kpc of the Virgo Cluster [1].

The Cherenkov Telescope Array (CTA) aims to have unprecedented accuracy and sensitivity, affording us the ability to understand the mysteries of the high energy universe. This unprecedented accuracy forces us to improve current calibration procedures, or indeed pioneer new techniques, to ensure the envisaged CTA performance. CTA will infer the energy of the gamma-rays it detects from the amount of Cherenkov radiation it observes. As such, the optical efficiency of the telescopes needs to be monitored, and its wavelength dependent degradation, which might be different for different telescope types, needs to be determined. Based on the results of a feasibility study, a novel cross-calibration method with an unmanned aerial vehicle (UAV) was tested on the H.E.S.S. telescope array, leading to the first cross-calibration of an Imaging Atmospheric Cherenkov Telescope (IACT) array with a single light source. In this talk, we present the cross-calibration results from a first test campaign in which we determine the relative optical efficiencies of the four HESS-I telescopes by successfully recording light from the UAV-mounted nanosecond pulsed UV light source simultaneously in all four telescopes. In addition, we show that the UAV data can be used to monitor the pointing accuracy at the level of at least tens of arcseconds and we give an outlook on other potential use cases of UAVs such as the monitoring of the atmospheric state.

Bee parasitic mite syndrome is a disease complex of colonies simultaneously infested with Varroa destructor mites and infected with viruses and accompanied by high mortality. By using real-time PCR (TaqMan), five out of seven bee viruses were detected in mite samples (V. destructor) collected from Thailand. Moreover, the results of this study provide an evidence for the co-existence of several bee viruses in a single mite. This is also the first report of bee viruses in mites from Thailand.

A new empirical water saturation model is presented which facilitates the derivation of reservoir hydrocarbon saturation from core capillary pressure data or directly determined core saturation data. The new model outperforms existing models and is shown to be applicable to a wide range of lithotypes from tight gas sands to carbonates. The only inputs the model requires are permeability and either directly determined water saturation or drainage capillary pressure data. The model has been shown to predict core water saturation to a higher standard than actual directly extracted core saturations for populations where both types of input data exist. The model has also been used to detect and rectify errors in laboratory data determinations used to populate the model. The number of inputs required to populate the model are fewer than those required to generate water saturation from wireline logs and thus have much reduced scope for experimental error. In addition, when applied to conventional core data or probe permeability data, the model can predict reservoir saturations with far higher resolution than logging tools which inevitably suffer from bed boundary effects. Case studies have demonstrated systematic underestimation of hydrocarbon in thin bedded formations by logs when compared with saturation data from the model. Another application of the model is in prediction of the free water level. This has been implemented in situations where direct water saturation of core plugs was determined and it was desired to avoid penetration of the water zone by the well. In another example, the model prediction of free water level agreed to within a fraction of a metre of the value being carried for the field, which had been derived from pressure test data for a plurality of wells. Introduction It has long been recognised that permeability influences the primary drainage capillary pressure characteristics of reservoir rocks. One of the earlier and most enduring proponents of this was M.C.Leverett [1], whose famous “J Function” involved the square root of permeability as part of a capillary pressure normalisation term. In more recent times, a number of researchers have observed nearlinearity variously between water saturation, drainage capillary pressure and permeability in bilogarithmic space. This led the authors of this paper to the conclusion that water saturation might reasonably be treated as a planar function of permeability and capillary pressure in tri-logarithmic space. This may be expressed mathematically thus: ( ) S a Pc K w b c = 1 In the course of our investigations, the authors encountered work by Johnson [2], who arrived at a somewhat more complex functional form which may be described thus: ( ) S a K w b Pc d c = 10 2 ( ) S∅ndenå [3] developed a similar model and was able to match the quality of the Johnson analysis of his data, albeit at the cost of introducing extra parameters. S∅ndenå curiously misquotes the Johnson functional form in his paper as equivalent to the simpler form favoured by the authors of this paper (Equation (1)). The functional forms arrived at for their models by both authors seem to be a consequence of the staged approach taken. A significant drawback of these methods as published is that this staged approach is rather tedious and also requires data at a series of fixed capillary pressures, which renders some types of data (such as direct core water saturations) inadmissible. However, with the desktop computing power now available it is trivial to regress multivariate data and complex functional forms directly. The simpler conception by the current authors of a planar relationship in tri-logarithmic space quoted in Equation (1) nonetheless outperforms that of Equation (2) for the data populations studied and presented in this paper. For many years, it has been the objective of some researchers to use capillary pressure data to attempt to predict formation permeability. It is clear from the relationships shown here that a given change in water saturation is associated in general with a much higher percentage change in permeability. Permeability should therefore be a much more accurate predictor of water saturation than vice-versa. The chief purpose of this paper is to demonstrate that routine core data typically has much underused potential in formation evaluation and has the power to predict formation water saturation to unequalled accuracy in many circumstances. Treatment of Capillary Pressure Data It is clearly a prerequisite for modelling data with the functional form of Equation (1) that each capillary pressure curve be essentially linear when plotted in bilogarithmic space. It is the experience of the authors that the vast majority of curves honour this boundary condition, although rarely over the whole saturation range covered. The example in Figure 1 which shows both air/brine capillary pressure and mercury intrusion data converted to an equivalent system is typical. The air/brine data becomes linear below some critical wetting phase saturation, and agrees well with the scaled mercury data in the mid saturation range. The departure of the two curves at higher drainage pressure is expected for fundamental reasons. Since vacuum takes the place of a true wetting phase during mercury tests, there is no retention either of wetting films or trapping of occluded porosity lacking a drainage path. Non-linear behaviour seems to be a good measure of these phenomena and thus forms a reasonable exclusion criteria when filtering data to be fitted. Direct core saturation data clearly cannot be treated in this way, however this has not been deleterious to the quality of fit in the authors’ experience as the examples discussed later in this paper demonstrate. Nevertheless, with these types of data, care should be taken at high wetting phase saturations to avoid including data which might cause diminution of the quality of the model. Applications of the Model Screening of Experimental Data The model is generally capable of describing capillary pressure datasets to sufficient precision that the mean difference between model and measured saturations is of the same order or lower than random errors. In principle, therefore, the model may be used to detect excessive error in particular data points with a view to possible correction. The example shown in Figure 2 is a set of air/brine data for a small group of sandstones featuring significant quantities of microporosity. Sample 1 was initially an outlier and was excluded from the fit. Upon correction of a typographical error in pore volume amounting to some 15% which caused systematic error in all of the saturation data for the sample, the corrected data closely matched the estimate derived from the model of the remainder of the population as shown in Figure 3. Modelling of Direct Water Saturation Data The model is well suited to this type of data since each datum possesses an independent permeability and consequently provides more degrees of freedom than an equivalent population size of capillary pressure data. Example 1 The suite of data shown in Figure 4 represents a small population of directly extracted core saturations from a single core (and thus over a small depth range) from a UK Central North Sea Palaeocene play. In this instance, the range in water saturation is largely a reflection of permeability variation, since there is little change in depth across the suite of data. The mean difference between measured and model water saturation is 0.019. The permeability exponent converged on a value of -.266. This in fact demonstrates relative insensitivity of saturation to permeability change. Cursory examination of Table 1 reveals that a permeability range of over three orders of magnitude is required to cause the observed range in water saturation. The corollary of this is that permeability information is a much more powerful predictor of water saturation than saturation is of permeability, especially at low water saturations. This is the key to the predictive use of routine permeability. The dissemination of the potential for the use of routine permeability data in this way is the ultimate goal of this publication. Example 2 In this case, two sets of direct Sw from core data from two different wells from the same field in the UK West of Shetland province were used to generate the model. Height above Free Water Level was substituted for capillary pressure thus: ( ) S a H K w b c = 3 The results are presented in Figure 5, with the model parameters quoted on the plot. Well 1 (plotted solid red) is a crestal well remote from the Free Water Level and with the single exception of a low permeability siltstone sample exhibits generally low water saturations. The model nonetheless describes this particular sample (existing some 230 m above the Free Water Level) almost perfectly. Well 2 (plotted solid blue) is transitional and consequently exhibits a range in Sw arising chiefly from regression between water saturation and increasing height above Free Water Level. The mean difference between observed and model saturation is 0.012 for this dataset. It is clear that the model can precisely describe water saturation over a wide range of both permeability and height above Free Water Level. Prediction of Free Water Level The high standard of agreement between observed data and the model above would not be achieved if significant uncertainty existed in the depth of the Free Water Level because the resulting error for each sample would be strongly influenced by its depth. Data pertaining to depths more remote from the Free Water Level would suffer relatively less error than transition zone points. These data were used as an exercise to estimate the true vertical depth of the Free Water Level, already well defined by pressure data from a plurality of wells drilled through to the water zone. This was accomplished by making a trivial alteration to the model subs

Using classical typing antisera, previous experiments have failed to demonstrate IgG3 in partially reduced and alkylated preparations of human IgG intended for intravenous application (IGIV). To establish that IgG3 is actually present in such preparations, we designed an enzyme-linked immunosorbent assay (ELISA) using monoclonal antibodies as solid-phase reagents and protein A-purified IgG3 as antigen. Three different samples of reduced and alkylated antigen were used: (1) IgG3 isolated from a ready-for-infusion IGIV; (2) IgG3 which was purified from an intramuscular (Cohn fraction II) IgG solution before being subjected to a mild reduction and alkylation procedure, and (3) completely reduced and alkylated IgG3. The reduction and alkylation procedure did not affect the solubility of IgG3, indicating that IGIV prepared in this manner should contain normal quantities of IgG3. In the ELISA, solid-phase monoclonals which were cross-reactive with multiple IgG subclasses clearly reacted with reduced and alkylated IgG3. Furthermore, there was no substantial difference between the quantities of modified and native antigen required for 50% maximal ELISA signal. In contrast, solid-phase monoclonals with IgG3-restricted specificity did not recognize reduced and alkylated material. These results indicate that IGIV prepared by reduction and alkylation has a normal IgG3 content and confirm that some IgG3-specific determinants are altered by the modification procedure.