arXiv subjects

Teymoor Saifollahi

Publications and source records attributed to Teymoor Saifollahi.

11 recordsLinked to original sources

arXiv2026

Constraints on the fuzzy dark matter mass using globular clusters in dwarf galaxies from Euclid ERO data

We constrain the particle mass of the fuzzy dark matter model using globular cluster candidates in dwarf galaxies, from the Euclid Early Release Observations, within the Fornax and Perseus galaxy clusters. We model the orbital evolution of globular clusters by combining fuzzy dark matter density profiles with the corresponding dynamical friction formalism, and integrate their orbits using galpy. In the fuzzy dark matter framework, both the halo density profile and the dynamical friction force are modified by the wave-like nature of the ultra-light dark matter particle, whose mass is parameterized as $m_{22}=m_χ/10^{-22},\mathrm{eV}$ and explored over the range $0.1 \leq m_{22} \leq 100$. This framework naturally alleviates the long-standing globular cluster timing problem originally identified in the Fornax dSph. We find that globular clusters no longer experience orbital decay toward the centers of their host galaxies for $m_{22}\leq1.46^{+0.51}_{-0.32}$ in the Fornax cluster and $m_{22}\leq1.09^{+0.14}_{-0.21}$ in the Perseus cluster. Future Euclid data releases, providing substantially larger samples of globular cluster candidates, will enable significantly tighter constraints on the dark matter particle mass within the fuzzy dark matter scenario.

astro-ph.GA↗

arXiv2026

The contribution of the color space in LSST-like photometry for the selection of extragalactic globular cluster candidates

Globular clusters (GCs) are excellent tracers of their host galaxies' evolutionary histories. Traditional methods for identifying GCs in galaxies rely on cuts over photometric catalogs and can yield source lists with high levels of contamination from compact background galaxies and foreground stars. In an era when large-scale sky surveys produce photometry for millions of sources, it is essential to employ flexible and scalable tools to reliably identify GCs in external galaxies. To prepare for surveys like Rubin/LSST, we need to explore practical methodological improvements and quantify the limitations inherent in the datasets. This paper investigates the selection of point-like extragalactic GCs exclusively in the $ugrizY$ color space. We use archival data to assemble an LSST-like photometric catalog for the Fornax Cluster containing labeled confirmed GCs, galaxies, and stars. From this catalog, using principal component analysis and non-linear auto-encoders (AEs), we construct inputs to random forest and multi-layer perceptron classifiers. We show that selecting GCs using all the 15 available colors can lead to a minimum contamination rate of ~30%, whereas the use of color-color diagrams may double such rate. If only the first 4 principal components of the colors are used instead, the same minimum contamination rate is achieved without increasing incompleteness. The AEs did not improve GC identification. To further reduce contamination and extract the full potential of LSST for star cluster studies, we argue for the need to augment photometric information with ancillary data (morphology from space-based missions and near-infrared photometry) before attempting to leverage more complex models.

astro-ph.GA↗

arXiv2025

Unlocking the physics of dwarf galaxies in the 2040s: The case for a next-generation wide-field spectroscopic facility with fibres and IFUs

Dwarf galaxies ($M_{\star} \lesssim 10^{9} M_{\odot}$) are the most numerous galaxies in the Universe and critical probes of dark matter, baryonic feedback, and galaxy formation. Despite significant progress from wide-field imaging surveys, the majority of dwarf candidates beyond the Local Group will lack spectroscopic follow-up, leaving fundamental questions about their internal kinematics, stellar populations, chemical enrichment, and dark matter content unresolved. Existing and planned facilities cannot efficiently provide the necessary spectroscopy for low-surface-brightness dwarfs over wide areas. We advocate for a dedicated large-aperture ($\geq 20$ m), wide-field, highly multiplexed spectroscopic facility with deployable or monolithic IFUs, capable of high signal-to-noise observations down to $I_{\rm E} \gtrsim 22-23$ mag. Such a facility would enable transformative studies of dark matter cores, baryonic feedback, tidal interactions, environmental effects, and stellar populations, extending the spectroscopic exploration of low-mass galaxies to $z \sim 1.5$, and providing decisive tests of $Λ$CDM and alternative dark matter models. Beyond dwarfs, this capability would impact galaxy evolution, strong and weak lensing studies, and cosmology, ensuring that imaging data from the 2030s and 2040s can be fully exploited.

astro-ph.IM↗

arXiv2025

Stellar tidal streams around nearby spiral galaxies with deep imaging from amateur telescopes

Tidal interactions between massive galaxies and their satellites are fundamental processes in a Universe with L-Cold Dark Matter cosmology, redistributing material into faint features that preserve records of past galactic interactions. While stellar streams in the Local Group impressively demonstrate satellite disruption, they do not constitute a statistically significant sample. Constructing a substantial catalog of stellar streams beyond the Local Group remains challenging due to the difficulties in obtaining deep, wide-field images of galaxies. Despite their potential to illuminate dark matter distribution and galaxy formation processes, stellar streams remain underutilized as cosmological probes. The Stellar Tidal Stream Survey (STSS) addresses this observational gap by leveraging amateur telescopes to obtain deep, scientific-grade images of galactic outskirts, capable of building a more statistically meaningful sample of stellar streams. Over the last decade, the STSS has acquired deep (up to surface brightness limit 28.3 mag/arcsec^2 in the r-band), wide-field images of 15 nearby Milky Way analog galaxies using a network of robotic amateur telescopes, avoiding the issues associated with "mosaicing" smaller images taken with a professional telescope. Our survey has revealed a diverse range of previously unreported faint features related to dwarf satellite accretion - including stellar streams, shells, and umbrella-like structures. We discover an ultra-diffuse galaxy (NGC150-UDG1), which shows hints of tidal tails. The STSS demonstrates the suitability of modern amateur telescopes to detect and study faint, diffuse structures in large fields around nearby spiral galaxies. Their economic and accessibility advantages enable larger samples with deep imaging, essential for testing galaxy formation models and constraining the properties of minor merger events in the local Universe.

astro-ph.GA↗

arXiv2024

Piggybacking astronomical hazard investigations on scientific Big Data missions

Current and upcoming large optical and near-infrared astronomical surveys have fundamental science as their primary drivers. To cater to those, these missions scan large fractions of the entire sky at multiple wavelengths and epochs. These aspects make these data sets also valuable for investigations into astronomical hazards for life on Earth. The Netherlands Research School for Astronomy (NOVA) is a partner in several optical / near-infrared surveys. In this paper we focus on the astronomical hazard value for two sets of those: the surveys with the OmegaCAM wide-field imager at the VST and with the Euclid Mission. For each of them we provide a brief overview of the astronomical survey hardware, the data and the information systems. We present first results related to the astronomical hazard investigations. We evaluate to what extent the existing functionality of the information systems covers the needs for the astronomical hazard investigations

astro-ph.IM↗

arXiv2023

Mining archival data from wide-field astronomical surveys in search of near-Earth objects

Increasing our knowledge of the orbits and compositions of Near-Earth Objects (NEOs) is important for a better understanding of the evolution of the Solar System and of life. The detection of serendipitous NEO appearances among the millions of archived exposures from large astronomical imaging surveys can provide a contribution which is complementary to NEO surveys. Using the AstroWISE information system, this work aims to assess the detectability rate, the achieved recovery rate and the quality of astrometry when data mining the ESO archive for the OmegaCAM wide-field imager at the VST. We developed an automatic pipeline that searches for the NEO appearances inside the AstroWISE environment. Throughout the recovery process, the pipeline uses several public web-tools to identify possible images that overlap with the position of NEOs, and acquires information on the NEOs predicted position and other properties (e.g., magnitude, rate and direction of motion) at the time of observations. We have recovered 196 appearances of NEOs from a set of 968 appearances predicted to be recoverable. It includes appearances for three NEOs which were on the impact risk list at that point. These appearances were well before their discovery. The subsequent risk assessment using the extracted astrometry removes these NEOs from the risk list. We estimate a detectability rate of 0.05 per NEO at an SNR>3 for NEOs in the OmegaCAM archive. Our automatic recovery rates are 40% and 20% for NEOs on the risk list and the full list, respectively. The achieved astrometric and photometric accuracy is on average 0.12 arcsec and 0.1 mag. These results show the high potential of the archival imaging data of the ground-based wide-field surveys as useful instruments for the search, (p)recovery and characterization of NEOs. Highly automated approaches, as possible using AstroWISE, make this undertaking feasible.

astro-ph.EP↗

arXiv2022

Implications for Galaxy Formation Models from Observations of Globular Clusters around Ultra-Diffuse Galaxies

We present an analysis of Hubble Space Telescope observations of globular clusters (GCs) in six ultra-diffuse galaxies (UDGs) in the Coma cluster, a sample that represents UDGs with large effective radii ($R_{\rm e}$), and use the results to evaluate competing formation models. We eliminate two significant sources of systematic uncertainty in the determination of the number of GCs, $N_{\rm GC}$ by using sufficiently deep observations that (i) reach the turnover of the GC luminosity function and (ii) provide a sufficient number of GCs with which to measure the GC number radial distribution. We find that $N_{\rm GC}$ for these galaxies is on average $\sim$20, which implies an average total mass, $M_{\rm total}$, $\sim$ $10^{11}$ $M_{\odot}$ when applying the relation between $N_{\rm GC}$ and $M_{\rm total}$. This value of $N_{\rm GC}$ lies at the upper end of the range observed for dwarf galaxies of the same stellar mass and is roughly a factor of two larger than the mean. The GC luminosity function, radial profile and average colour are more consistent with those observed for dwarf galaxies than with those observed for the more massive ($L^*$) galaxies, while both the radial and azimuthal GC distributions closely follow those of the stars in the host galaxy. Finally, we discuss why our observations, specifically the GC number and GC distribution around these six UDGs, pose challenges for several of the currently favoured UDG formation models.

astro-ph.GA↗

arXiv2022

Detection of extragalactic Ultra-Compact Dwarfs and Globular Clusters using Explainable AI techniques

Compact stellar systems such as Ultra-compact dwarfs (UCDs) and Globular Clusters (GCs) around galaxies are known to be the tracers of the merger events that have been forming these galaxies. Therefore, identifying such systems allows to study galaxies mass assembly, formation and evolution. However, in the lack of spectroscopic information detecting UCDs/GCs using imaging data is very uncertain. Here, we aim to train a machine learning model to separate these objects from the foreground stars and background galaxies using the multi-wavelength imaging data of the Fornax galaxy cluster in 6 filters, namely u, g, r, i, J and Ks. The classes of objects are highly imbalanced which is problematic for many automatic classification techniques. Hence, we employ Synthetic Minority Over-sampling to handle the imbalance of the training data. Then, we compare two classifiers, namely Localized Generalized Matrix Learning Vector Quantization (LGMLVQ) and Random Forest (RF). Both methods are able to identify UCDs/GCs with a precision and a recall of >93 percent and provide relevances that reflect the importance of each feature dimension %(colors and angular sizes) for the classification. Both methods detect angular sizes as important markers for this classification problem. While it is astronomical expectation that color indices of u-i and i-Ks are the most important colors, our analysis shows that colors such as g-r are more informative, potentially because of higher signal-to-noise ratio. Besides the excellent performance the LGMLVQ method allows further interpretability by providing the feature importance for each individual class, class-wise representative samples and the possibility for non-linear visualization of the data as demonstrated in this contribution. We conclude that employing machine learning techniques to identify UCDs/GCs can lead to promising results.

astro-ph.GA↗

arXiv2021

Ultra-compact dwarfs beyond the centre of the Fornax galaxy cluster: Hints of UCD formation in low-density environments

Ultra-compact dwarf galaxies (UCDs) were serendipitously discovered by spectroscopic surveys in the Fornax cluster twenty years ago. Nowadays, it is commonly accepted that many bright UCDs are the nuclei of galaxies that have been stripped. However, this conclusion might be driven by biased samples of UCDs in high-density environments, on which most searches are based. With the deep optical images of the Fornax Deep Survey, combined with public near-infrared data, we revisit the UCD population of the Fornax cluster and search for UCD candidates, for the first time, systematically out to the virial radius of the galaxy cluster. Our search is complete down to magnitude m$_g$ = 21 mag or M$_g$ $\sim$ -10.5 mag at the distance of the Fornax cluster. The UCD candidates are identified and separated from foreground stars and background galaxies by their optical and near-infrared colours. This primarily utilizes the $u-i$/$i-Ks$ diagram and a machine learning technique is employed to incorporate other colour combinations to reduce the number of contaminants. The newly identified candidates (44) in addition to the spectroscopically confirmed UCDs (61), increases the number of known Fornax UCD considerably (105). Almost all of the new UCD candidates are located outside the Fornax cluster core (360 kpc), where all of the known UCDs were found. The distribution of UCDs within the Fornax cluster shows that a population of UCDs may form in low-density environments. This most likely challenges the current models of UCD formation.

astro-ph.GA↗

arXiv2020

The number of globular clusters around the iconic UDG DF44 is as expected for dwarf galaxies

There is a growing consensus that the vast majority of ultra-diffuse galaxies (UDGs) are dwarf galaxies. However, there remain a few UDGs that seem to be special in terms of their globular cluster (GC) systems. In particular, according to some authors, certain UDGs exhibit large GC populations when compared to expectations from their stellar (or total) mass. Among these special UDGs, DF44 in the Coma cluster is one of the better-known examples. DF44 has been claimed to have a relatively high number of GCs, $N_{GC}=74^{+18}_{-18}$, for a stellar mass of only $3\times 10^8$ $M_{ \odot }$ which would indicate a much larger dark halo mass than dwarfs of similar stellar mass. In this paper we revisit this number and, contrary to previous results, find $N_{GC}=21^{+7}_{-9}$ assuming that the distribution of the GCs follows the same geometry as the galaxy. If we assume that the GCs around DF44 are distributed in a (projected) circularly symmetric way and, if we use a less strict criterion for the selection of the GCs, we find $N_{GC}=18^{+23}_{-12}$. Making use of the $M_{\rm GC} - M_{\rm halo}$ relation, this number of GCs suggests a dark matter halo mass of $M_{halo}=1.1^{+0.4}_{-0.5} \times 10^{11} M_{\odot}$, a value which is consistent with the expected total mass for DF44 based on its velocity dispersion, $σ=33^{+3}_{-3}$ km s$^{-1}$. We conclude that the number of GCs around DF44 is as expected for regular dwarf galaxies of similar stellar mass and DF44 is not extraordinary in this respect.

astro-ph.GA↗

arXiv2020

In search of the weirdest galaxies in the Universe

Weird galaxies are outliers that have either unknown or very uncommon features making them different from the normal sample. These galaxies are very interesting as they may provide new insights into current theories, or can be used to form new theories about processes in the Universe. Interesting outliers are often found by accident, but this will become increasingly more difficult with future big surveys generating an enormous amount of data. This gives the need for machine learning detection techniques to find the interesting weird objects. In this work, we inspect the galaxy spectra of the third data release of the Galaxy And Mass Assembly survey and look for the weird outlying galaxies using two different outlier detection techniques. First, we apply distance-based Unsupervised Random Forest on the galaxy spectra using the flux values as input features. Spectra with a high outlier score are inspected and divided into different categories such as blends, quasi-stellar objects, and BPT outliers. We also experiment with a reconstruction-based outlier detection method using a variational autoencoder and compare the results of the two different methods. At last, we apply dimensionality reduction techniques on the output of the methods to inspect the clustering of similar spectra. We find that both unsupervised methods extract important features from the data and can be used to find many different types of outliers.

astro-ph.GA↗