Measurement of the diffractive structure function in deep inelastic scattering at HERA

This paper presents an analysis of the inclusive properties of diffractive deep inelastic scattering events produced in $ep$ interactions at HERA. The events are characterised by a rapidity gap between the outgoing proton system and the remaining hadronic system. Inclusive distributions are presented and compared with Monte Carlo models for diffractive processes. The data are consistent with models where the pomeron structure function has a hard and a soft contribution. The diffractive structure function is measured as a function of \xpom, the momentum fraction lost by the proton, of $\beta$, the momentum fraction of the struck quark with respect to \xpom, and of $Q^2$. The \xpom dependence is consistent with the form \xpoma where $a~=~1.30~\pm~0.08~(stat)~^{+~0.08}_{-~0.14}~(sys)$ in all bins of $\beta$ and $Q^2$. In the measured $Q^2$ range, the diffractive structure function approximately scales with $Q^2$ at fixed $\beta$. In an Ingelman-Schlein type model, where commonly used pomeron flux factor normalisations are assumed, it is found that the quarks within the pomeron do not saturate the momentum sum rule.Comment: 36 pages, latex, 11 figures appended as uuencoded fil

Genetic predisposition to ductal carcinoma in situ of the breast

Background: Ductal carcinoma in situ (DCIS) is a non-invasive form of breast cancer. It is often associated with invasive ductal carcinoma (IDC), and is considered to be a non-obligate precursor of IDC. It is not clear to what extent these two forms of cancer share low-risk susceptibility loci, or whether there are differences in the strength of association for shared loci. Methods: To identify genetic polymorphisms that predispose to DCIS, we pooled data from 38 studies comprising 5,067 cases of DCIS, 24,584 cases of IDC and 37,467 controls, all genotyped using the iCOGS chip. Results: Most (67 %) of the 76 known breast cancer predisposition loci showed an association with DCIS in the same direction as previously reported for invasive breast cancer. Case-only analysis showed no evidence for differences between associations for IDC and DCIS after considering multiple testing. Analysis by estrogen receptor (ER) status confirmed that loci associated with ER positive IDC were also associated with ER positive DCIS. Analysis of DCIS by grade suggested that two independent SNPs at 11q13.3 near CCND1 were specific to low/intermediate grade DCIS (rs75915166, rs554219). These associations with grade remained after adjusting for ER status and were also found in IDC. We found no novel DCIS-specific loci at a genome wide significance level of P < 5.0x10-8. Conclusion: In conclusion, this study provides the strongest evidence to date of a shared genetic susceptibility for IDC and DCIS. Studies with larger numbers of DCIS are needed to determine if IDC or DCIS specific loci exist

Dense Stellar Populations: Initial Conditions

This chapter is based on four lectures given at the Cambridge N-body school "Cambody". The material covered includes the IMF, the 6D structure of dense clusters, residual gas expulsion and the initial binary population. It is aimed at those needing to initialise stellar populations for a variety of purposes (N-body experiments, stellar population synthesis).Comment: 85 pages. To appear in The Cambridge N-body Lectures, Sverre Aarseth, Christopher Tout, Rosemary Mardling (eds), Lecture Notes in Physics Series, Springer Verla

A probabilistic deep learning model to distinguish cusps and cores in dwarf galaxies

Numerical simulations within a cold dark matter (DM) cosmology form halos whose density profiles have a steep inner slope (`cusp'), yet observations of galaxies often point towards a flat central `core'. We develop a convolutional mixture density neural network model to derive a probability density function (PDF) of the inner density slopes of DM halos. We train the network on simulated dwarf galaxies from the NIHAO and AURIGA projects, which include both DM cusps and cores: line-of-sight velocities and 2D spatial distributions of their stars are used as inputs to obtain a PDF representing the probability of predicting a specific inner slope. The model recovers accurately the expected DM profiles: $\sim$82$\%$ of the galaxies have a derived inner slope within $\pm$0.1 of their true value, while $\sim$98$\%$ within $\pm$0.3. We apply our model to four Local Group dwarf spheroidal galaxies and find results consistent with those obtained with the Jeans modelling based code GravSphere: the Fornax dSph has a strong indication of possessing a central DM core, Carina and Sextans have cusps (although the latter with large uncertainties), while Sculptor shows a double peaked PDF indicating that a cusp is preferred, but a core can not be ruled out. Our results show that simulation-based inference with neural networks provide a innovative and complementary method for the determination of the inner matter density profiles in galaxies, which in turn can help constrain the properties of the elusive DM

A probabilistic deep learning model to distinguish cusps and cores in dwarf galaxies

Numerical simulations within a cold dark matter (DM) cosmology form halos whose density profiles have a steep inner slope (`cusp'), yet observations of galaxies often point towards a flat central `core'. We develop a convolutional mixture density neural network model to derive a probability density function (PDF) of the inner density slopes of DM halos. We train the network on simulated dwarf galaxies from the NIHAO and AURIGA projects, which include both DM cusps and cores: line-of-sight velocities and 2D spatial distributions of their stars are used as inputs to obtain a PDF representing the probability of predicting a specific inner slope. The model recovers accurately the expected DM profiles: $\sim$82$\%$ of the galaxies have a derived inner slope within $\pm$0.1 of their true value, while $\sim$98$\%$ within $\pm$0.3. We apply our model to four Local Group dwarf spheroidal galaxies and find results consistent with those obtained with the Jeans modelling based code GravSphere: the Fornax dSph has a strong indication of possessing a central DM core, Carina and Sextans have cusps (although the latter with large uncertainties), while Sculptor shows a double peaked PDF indicating that a cusp is preferred, but a core can not be ruled out. Our results show that simulation-based inference with neural networks provide a innovative and complementary method for the determination of the inner matter density profiles in galaxies, which in turn can help constrain the properties of the elusive DM

A probabilistic deep learning model to distinguish cusps and cores in dwarf galaxies

Numerical simulations within a cold dark matter (DM) cosmology form halos whose density profiles have a steep inner slope (`cusp'), yet observations of galaxies often point towards a flat central `core'. We develop a convolutional mixture density neural network model to derive a probability density function (PDF) of the inner density slopes of DM halos. We train the network on simulated dwarf galaxies from the NIHAO and AURIGA projects, which include both DM cusps and cores: line-of-sight velocities and 2D spatial distributions of their stars are used as inputs to obtain a PDF representing the probability of predicting a specific inner slope. The model recovers accurately the expected DM profiles: $\sim$82$\%$ of the galaxies have a derived inner slope within $\pm$0.1 of their true value, while $\sim$98$\%$ within $\pm$0.3. We apply our model to four Local Group dwarf spheroidal galaxies and find results consistent with those obtained with the Jeans modelling based code GravSphere: the Fornax dSph has a strong indication of possessing a central DM core, Carina and Sextans have cusps (although the latter with large uncertainties), while Sculptor shows a double peaked PDF indicating that a cusp is preferred, but a core can not be ruled out. Our results show that simulation-based inference with neural networks provide a innovative and complementary method for the determination of the inner matter density profiles in galaxies, which in turn can help constrain the properties of the elusive DM

A reduced size of the ovarian follicle pool is associated with an increased risk of a trisomic pregnancy in IVF-treated women

The increased risk of a trisomic pregnancy with a woman's age arises from an increased rate of meiotic non-disjunction in the oocytes. It has been hypothesized that the increase in meiotic errors is related to the decreasing number of oocytes with age. Our aim was to assess the relation between trisomic pregnancy and three parameters of oocyte quantity. In a Dutch nationwide database on in vitro fertilization (IVF) treatment from 1983 to 1995, we identified 28 women with a trisomic pregnancy conceived via or within 1 year from IVF treatment. We selected five age-matched controls with a healthy child for each trisomy case. We performed a case-control study to examine whether trisomy cases more often had a history of ovarian surgery and a lower response to ovarian hyperstimulation than controls. Subsequently, cases and controls were followed to compare the incidence of signs of menopause at the end of the study period as self-reported by questionnaire. Logistic regression analysis showed an association between trisomic pregnancy and a history of ovarian surgery [odds ratio (OR) 3.3; 95% confidence interval (CI): 1.0-10.5; P = 0.04] and between trisomic pregnancy and retrieval of < 4 oocytes during IVF treatment (OR 4.0; 95% CI: 1.4-11.5; P = 0.01). The adjusted OR for signs of menopause associated with trisomic pregnancy was 5.7 (95% CI: 1.1-29.9; P = 0.04). Our results suggest that IVF-treated women with a reduced ovarian follicle pool are at increased risk of a trisomic pregnancy, independent of their age. Our findings support the hypothesis that follicle pool size and not chronological age determines a woman's trisomy risk. Since a questionnaire was used, we cannot fully exclude the possibility of selection bias in this study