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We present $γ$-ray optical-depth calculations from a recently published extragalactic background light (EBL) model built from multiwavelength galaxy data from the Hubble Space Telescope Cosmic Assembly Near-Infrared Deep Extragalactic Legacy Survey (HST/CANDELS). CANDELS gathers one of the deepest and most complete observations of stellar and dust emissions in galaxies. This model resulted in a robust derivation of the evolving EBL spectral energy distribution up to $z\sim 6$, including the far-infrared peak. Therefore, the optical depths derived from this model will be useful for determining the attenuation of $γ$-ray photons coming from high-redshift sources, such as those detected by the Large Area Telescope on board the Fermi Gamma-ray Space Telescope, and for multi-TeV photons that will be detected from nearby sources by the future Cherenkov Telescope Array. From these newly calculated optical depths, we derive the cosmic $γ$-ray horizon and also measure the expansion rate and matter content of the Universe including an assessment of the impact of the EBL uncertainties. We find $H_{0}=61.9$ $^{+2.9}_{-2.4}$ km s$^{-1}$ Mpc$^{-1}$ when fixing $Ω_{m}=0.32$, and $H_{0}=65.6$ $^{+5.6}_{-5.0}$ km s$^{-1}$ Mpc$^{-1}$ and $Ω_{m}=0.19\pm 0.07$, when exploring these two parameters simultaneously., 10 pages, 8 figures, 1 tables; submitted to MNRAS. Comments welcome

Massive black holes (BHs) at the centres of massive galaxies are ubiquitous. The population of BHs within dwarf galaxies, on the other hand, is evasive. Dwarf galaxies are thought to harbour BHs with proportionally small masses, including intermediate mass BHs, with masses $10^{2} < M_{BH} < 10^{6} M_{\odot}$. Identification of these systems has historically relied upon the detection of light emitted from accreting gaseous discs close to the BHs. Without this light, they are difficult to detect. Tidal disruption events (TDEs), the luminous flares produced when a star strays close to a BH and is shredded, are a direct way to probe massive BHs. The rise times of these flares theoretically correlate with the BH mass. Here we present AT2020neh, a fast rising TDE candidate, hosted by a dwarf galaxy. AT2020neh can be described by the tidal disruption of a main sequence star by a 10$^{4.7} - 10^{5.9} M_{\odot}$ BH. We find the observable rate of fast rising nuclear transients like AT2020neh to be rare, at $\lesssim 2 \times 10^{-8}$ events Mpc$^{-3}$ yr$^{-1}$. Finding non-accreting BHs in dwarf galaxies is important to determine how prevalent BHs are within these galaxies, and constrain models of BH formation. AT2020neh-like events may provide a galaxy-independent method of measuring IMBH masses., Accepted for publication in Nature Astronomy

A new revision of the gamma flux that we expect to detect in Imaging Atmospheric Cherenkov Telescopes (IACTs) from neutralino annihilation in the Draco dSph is presented in the context of the minimal supersymmetric standard models (MSSM) compatible with the present phenomenological and cosmological constraints, and using the dark matter (DM) density profiles compatible with the latest observations. This revision takes also into account the important effect of the Point Spread Function (PSF) of the telescope, and is valid not only for Draco but also for any other DM target. We show that this effect is crucial in the way we will observe and interpret a possible signal detection. Finally, we discuss the prospects to detect a possible gamma signal from Draco for current or planned gamma-ray experiments, i.e. MAGIC, GLAST and GAW. Even with the large astrophysical and particle physics uncertainties we find that the chances to detect a neutralino annihilation signal in Draco seem to be very scarce for current experiments. However, the prospects for future IACTs with upgraded performances (especially lower threshold energies and higher sensitivities) such as those offered by the CTA project, might be substantially better., Comment: 14 pages, 10 figures and 3 tables. Accepted for publication in Physical Review D. Minor changes with respect to the previous version after referee's comments

We present the Young Supernova Experiment Data Release 1 (YSE DR1), comprised of processed multi-color Pan-STARRS1 (PS1) griz and Zwicky Transient Facility (ZTF) gr photometry of 1975 transients with host-galaxy associations, redshifts, spectroscopic/photometric classifications, and additional data products from 2019 November 24 to 2021 December 20. YSE DR1 spans discoveries and observations from young and fast-rising supernovae (SNe) to transients that persist for over a year, with a redshift distribution reaching z~0.5. We present relative SN rates from YSE's magnitude- and volume-limited surveys, which are consistent with previously published values within estimated uncertainties for untargeted surveys. We combine YSE and ZTF data, and create multi-survey SN simulations to train the ParSNIP and SuperRAENN photometric classification algorithms; when validating our ParSNIP classifier on 472 spectroscopically classified YSE DR1 SNe, we achieve 82% accuracy across three SN classes (SNe Ia, II, Ib/Ic) and 90% accuracy across two SN classes (SNe Ia, core-collapse SNe). Our classifier performs particularly well on SNe Ia, with high (>90%) individual completeness and purity, which will help build an anchor photometric SNe Ia sample for cosmology. We then use our photometric classifier to characterize our photometric sample of 1483 SNe, labeling 1048 (~71%) SNe Ia, 339 (~23%) SNe II, and 96 (~6%) SNe Ib/Ic. YSE DR1 provides a training ground for building discovery, anomaly detection, and classification algorithms, performing cosmological analyses, understanding the nature of red and rare transients, exploring tidal disruption events and nuclear variability, and preparing for the forthcoming Vera C. Rubin Observatory Legacy Survey of Space and Time., Accepted to ApJS; 64 pages; 35 figures; 10 tables

Supernovae (SNe) that have been multiply imaged by gravitational lensing are rare and powerful probes for cosmology. Each detection is an opportunity to develop the critical tools and methodologies needed as the sample of lensed SNe increases by orders of magnitude with the upcoming Vera C. Rubin Observatory and Nancy Grace Roman Space Telescope. The latest such discovery is of the quadruply imaged Type Ia SN 2022qmx (aka, “SN Zwicky”) at z = 0.3544. SN Zwicky was discovered by the Zwicky Transient Facility in spatially unresolved data. Here we present follow-up Hubble Space Telescope observations of SN Zwicky, the first from the multicycle “LensWatch (www.lenswatch.org)” program. We measure photometry for each of the four images of SN Zwicky, which are resolved in three WFC3/UVIS filters (F475W, F625W, and F814W) but unresolved with WFC3/IR F160W, and present an analysis of the lensing system using a variety of independent lens modeling methods. We find consistency between lens-model-predicted time delays (≲1 day), and delays estimated with the single epoch of Hubble Space Telescope colors (≲3.5 days), including the uncertainty from chromatic microlensing (∼1–1.5 days). Our lens models converge to an Einstein radius of θ E = ( 0.168 − 0.005 + 0.009 ) ″ , the smallest yet seen in a lensed SN system. The “standard candle” nature of SN Zwicky provides magnification estimates independent of the lens modeling that are brighter than predicted by ∼ 1.7 − 0.6 + 0.8 mag and ∼ 0.9 − 0.6 + 0.8 mag for two of the four images, suggesting significant microlensing and/or additional substructure beyond the flexibility of our image-position mass models.

E. Abdalla, G. F. Abellán, A. Aboubrahim et al., The standard Λ Cold Dark Matter (ΛCDM) cosmological model provides a good description of a wide range of astrophysical and cosmological data. However, there are a few big open questions that make the standard model look like an approximation to a more realistic scenario yet to be found. In this paper, we list a few important goals that need to be addressed in the next decade, taking into account the current discordances between the different cosmological probes, such as the disagreement in the value of the Hubble constant H0, the σ8–S8 tension, and other less statistically significant anomalies. While these discordances can still be in part the result of systematic errors, their persistence after several years of accurate analysis strongly hints at cracks in the standard cosmological scenario and the necessity for new physics or generalisations beyond the standard model. In this paper, we focus on the 5.0σ tension between the Planck CMB estimate of the Hubble constant H0 and the SH0ES collaboration measurements. After showing the H0 evaluations made from different teams using different methods and geometric calibrations, we list a few interesting new physics models that could alleviate this tension and discuss how the next decade's experiments will be crucial. Moreover, we focus on the tension of the Planck CMB data with weak lensing measurements and redshift surveys, about the value of the matter energy density Ωm, and the amplitude or rate of the growth of structure (σ8, fσ8). We list a few interesting models proposed for alleviating this tension, and we discuss the importance of trying to fit a full array of data with a single model and not just one parameter at a time. Additionally, we present a wide range of other less discussed anomalies at a statistical significance level lower than the H0–S8 tensions which may also constitute hints towards new physics, and we discuss possible generic theoretical approaches that can collectively explain the non-standard nature of these signals. Finally, we give an overview of upgraded experiments and next-generation space missions and facilities on Earth that will be of crucial importance to address all these open questions., Amin Aboubrahim is supported by the BMBF under contract 05P21PMCAA and by the DFG through the Research Training Network 2149 “Strong and Weak Interactions - from Hadrons to Dark Matter”. Adriano Agnello is supported by a Villum Experiment Grant, project number 36225. Özgür Akarsu acknowledges the support by the Turkish Academy of Sciences in the scheme of the Outstanding Young Scientist Award (TÜBA-GEBİP). Yashar Akrami is supported by Richard S. Morrison Fellowship and LabEx ENS-ICFP: ANR-10-LABX-0010/ANR-10-IDEX-0001-02 PSL*. George Alestas is supported by the project “Dioni: Computing Infrastructure for Big-Data Processing and Analysis” (MIS No. 5047222) co-funded by European Union (ERDF) and Greece through Operational Program “Competitiveness, Entrepreneurship and Innovation”, NSRF 2014-2020. Luca Amendola acknowledges support from DFG project 456622116 and from the CAPES-DAAD bilateral project “Data Analysis and Model Testing in the Era of Precision Cosmology”. Luis A. Anchordoqui and Jorge F. Soriano are supported by the U.S. National Science Foundation (NSF Grant PHY-2112527). Mario Ballardini acknowledges financial support from the contract ASI/INAF for the Euclid mission n.2018-23-HH.0. Micol Benetti acknowledges the Istituto Nazionale di Fisica Nucleare (INFN), sezione di Napoli, iniziativa specifica QGSKY. David Benisty acknowledges the support the supports of the Blavatnik and the Rothschild fellowships. John Blakeslee is supported by NOIRLab, which is managed by the Association of Universities for Research in Astronomy (AURA) under a cooperative agreement with the US National Science Foundation. Thomas Buchert has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement ERC advanced grant 740021–ARTHUS, PI: Thomas Buchert). Erminia Calabrese acknowledges support from the STFC Ernest Rutherford Fellowship ST/M004856/2, STFC Consolidated Grant ST/S00033X/1 and from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (Grant agreement No. 849169). Salvatore Capozziello acknowledges the Istituto Nazionale di Fisica Nucleare (INFN), sezione di Napoli, iniziative specifiche QGSKY and MOONLIGHT2. Javier de Cruz Pérez is supported by a FPI fellowship associated to the project FPA2016-76005-C2-1-P. Peter Denton acknowledges support from the US Department of Energy under Grant Contract DE-SC0012704. Eleonora Di Valentino is supported by a Royal Society Dorothy Hodgkin Research Fellowship. Keith R. Dienes was supported in part by the U.S. Department of Energy under Grant DE-FG02-13ER41976 / DE-SC0009913, and also by the U.S. National Science Foundation through its employee IR/D program. Celia Escamilla-Rivera is supported by DGAPA-PAPIIT UNAM Project TA100122 and acknowledges the Royal Astronomical Society as FRAS 10147 and the Cosmostatistics National Group (CosmoNag) project. Noemi Frusciante is supported by Fundação para a Ciência e a Tecnologia (FCT) through the research grants UIDB/04434/2020, UIDP/04434/2020, PTDC/FIS-OUT/29048/2017, CERN/FIS-PAR/0037/2019 and the personal FCT grant “CosmoTests – Cosmological tests of gravity theories beyond General Relativity” with ref. number CEECIND/00017/2018 and the FCT project “BEYLA–BEYond LAmbda” with ref. number PTDC/FIS-AST/0054/2021. Adrià Gómez-Valent is funded by the Instituto Nazionale di Fisica Nucleare (INFN) through the project “Dark Energy and Modified Gravity Models in the light of Low-Redshift Observations” (n. 22425/2020). Asta Heinesen has received funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement ERC advanced grant 740021–ARTHUS, PI: Thomas Buchert). J. Colin Hill acknowledges support from NSF grant AST-2108536. The Flatiron Institute is supported by the Simons Foundation. Mustapha Ishak acknowledges that this material is based upon work supported in part by the Department of Energy, Office of Science, under Award Number DE-SC0022184. Michael Klasen is supported by the BMBF under contract 05P21PMCAA and by the DFG through the Research Training Network 2149 “Strong and Weak Interactions - from Hadrons to Dark Matter”. Suresh Kumar gratefully acknowledges support from the Science and Engineering Research Board (SERB), Govt. of India (File No. CRG/2021/004658). Ruth Lazkoz is supported by the Spanish Ministry of Science and Innovation through research projects FIS2017-85076-P (comprising FEDER funds), and also by the Basque Government and Generalitat Valenciana through research projects GIC17/116-IT956-16 and PROMETEO/2020/079 respectively. Benjamin L'Huillier would like to acknowledge the support of the National Research Foundation of Korea (NRF-2019R1I1A1A01063740) and the support of the Korea Institute for Advanced Study (KIAS) grant funded by the government of Korea. Jackson Levi Said would like to acknowledge support from Cosmology@MALTA which is supported by the University of Malta. Roy Maartens is supported by the South African Radio Astronomy Observatory and the National Research Foundation (Grant No. 75415). Valerio Marra thanks CNPq (Brazil) and FAPES (Brazil) for partial financial support. The work of Yuto Minami was supported in part by the Japan Society for the Promotion of Science (JSPS) KAKENHI, Grants No. JP20K14497. The work of Carlos Martins was financed by FEDER—Fundo Europeu de Desenvolvimento Regional funds through the COMPETE 2020—Operational Programme for Competitiveness and Internationalisation (POCI), and by Portuguese funds through FCT - Fundação para a Ciência e a Tecnologia in the framework of the project POCI-01-0145-FEDER-028987 and PTDC/FIS-AST/28987/2017. Olga Mena is supported by the Spanish grants PID2020-113644GB-I00, PROMETEO/2019/083 and by the European ITN project HIDDeN (H2020-MSCA-ITN-2019//860881-HIDDeN). Cristian Moreno-Pulido is funded by PID2019-105614GB-C21 and FPA2016-76005-C2-1-P (MINECO, Spain), 2017-SGR-929 (Generalitat de Catalunya) and CEX2019-000918-M (ICCUB) and partially supported by the fellowship 2019 FI_B 00351. Michele Moresco acknowledges support from MIUR, PRIN 2017 (grant 20179ZF5KS) and grants ASI n.I/023/12/0 and ASI n.2018-23-HH.0. Suvodip Mukherjee is supported by the Simons Foundation. Research at Perimeter Institute is supported in part by the Government of Canada through the Department of Innovation, Science and Economic Development and by the Province of Ontario through the Ministry of Colleges and Universities. Pran Nath is supported in part by the NSF Grant PHY-1913328. Savvas Nesseris acknowledges support from the Research Project No. PGC2018-094773-B-C32 and the Centro de Excelencia Severo Ochoa Program No. CEX2020-001007-S. Rafael Nunes acknowledges support from the Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP, São Paulo Research Foundation) under the project No. 2018/18036-5. Eoin Ó Colgáin was supported by the National Research Foundation of Korea grant funded by the Korea government (MSIT) (NRF-2020R1A2C1102899). Supriya Pan acknowledges the financial supports from the Science and Engineering Research Board, Govt. of India, under Mathematical Research Impact-Centric Support Scheme (File No. MTR/2018/000940) and The Department of Science and Technology (DST), Govt. of India, under the Scheme “Fund for Improvement of S&T Infrastructure (FIST)” [File No. SR/FST/MS-I/2019/41]. Santiago E. Perez Bergliaffa acknowledges partial support from Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)- Código de Financiamento 001, and Universidade do Estado do Rio de Janeiro (Brazil). Leandros Perivolaropoulos acknowledges support by the Hellenic Foundation for Research and Innovation (H.F.R.I.), under the “First call for H.F.R.I. Research Projects to support Faculty members and Researchers and the procurement of high-cost research equipment Grant” (Project Number: 789). Fabrizio Renzi is supported by the NWO and the Dutch Ministry of Education, Culture and Science (OCW), and from the D-ITP consortium, a program of the NWO that is funded by the OCW. Nils Schöneberg acknowledges the support of the following Maria de Maetzu fellowship grant: Esta publicación es parte de la ayuda CEX2019-000918-M, financiado por MCIN/AEI/10.13039/501100011033. Anjan A Sen acknowledges the funding from SERB, Govt of India under the research grants no: CRG/2020/004347 and MTR/20l9/000599. Arman Shafieloo would like to acknowledge the support by National Research Foundation of Korea NRF-2021M3F7A1082053, and the support of the Korea Institute for Advanced Study (KIAS) grant funded by the government of Korea. M.M. Sheikh-Jabbari acknowledges the support by SarAmadan grant No. ISEF/M/400122. Joan Solà Peracaula is funded by PID2019-105614GB-C21 and FPA2016-76005-C2-1-P (MINECO, Spain), 2017-SGR-929 (Generalitat de Catalunya), CEX2019-000918-M (ICCUB) and also partially supported by the COST Association Action CA18108 “Quantum Gravity Phenomenology in the Multimessenger Approach (QG-MM)”. Denitsa Staicova is supported by Bulgarian NSF grant KP-06-N 38/11. Glenn Starkman is partly supported by a Department of Energy grant DESC0009946 to the particle astrophysics theory group at CWRU. Brooks Thomas is supported in part by the National Science Foundation under Grant PHY-2014104. Luca Visinelli has received support from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement “TALeNT” No. 754496 (H2020-MSCA-COFUND-2016 FELLINI). Shao-Jiang Wang is supported by the National Key Research and Development Program of China Grant No. 2021YFC2203004, No. 2021YFA0718304, the National Natural Science Foundation of China Grant No. 12105344, and the China Manned Space Project with NO.CMS-CSST-2021-B01. Weiqiang Yang has been supported by the National Natural Science Foundation of China under Grants No. 12175096 and No. 11705079, and Liaoning Revitalization Talents Program under Grant no. XLYC1907098. Gong-Bo Zhao is supported by the National Key Basic Research and Development Program of China (No. 2018YFA0404503), NSFC Grants 11925303, 11720101004, and a grant of CAS Interdisciplinary Innovation Team.

The primary difficulty in measuring dynamical masses of galaxy clusters from galaxy data lies in the separation between true cluster members from interloping galaxies along the line of sight. We study the impact of membership contamination and incompleteness on cluster mass estimates obtained with 25 commonly used techniques applied to nearly 1000 mock clusters. We show that all methods overestimate or underestimate cluster masses when applied to contaminated or incomplete galaxy samples respectively. This appears to be the main source of the intrinsic scatter in the mass scaling relation. Applying corrections based on a prior knowledge of contamination and incompleteness can reduce the scatter to the level of shot noise expected for poorly sampled clusters. We establish an empirical model quantifying the effect of imperfect membership on cluster mass estimation and discuss its universal and method-dependent features. We find that both imperfect membership and the response of the mass estimators depend on cluster mass, effectively causing a flattening of the estimated - true mass relation. Imperfect membership thus alters cluster counts determined from spectroscopic surveys, hence the cosmological parameters that depend on such counts., Comment: 18 pages, 11 figures, 3tables; accepted for publication in MNRAS

With the advent of wide-field cosmological surveys, we are approaching samples of hundreds of thousands of galaxy clusters. While such large numbers will help reduce statistical uncertainties, the control of systematics in cluster masses becomes ever more crucial. Here we examine the effects of an important source of systematic uncertainty in galaxy-based cluster mass estimation techniques: the presence of significant dynamical substructure. Dynamical substructure manifests as dynamically distinct subgroups in phase-space, indicating an 'unrelaxed' state. This issue affects around a quarter of clusters in a generally selected sample. We employ a set of mock clusters whose masses have been measured homogeneously with commonly-used galaxy-based mass estimation techniques (kinematic, richness, caustic, radial methods). We use these to study how the relation between observationally estimated and true cluster mass depends on the presence of substructure, as identified by various popular diagnostics. We find that the scatter for an ensemble of clusters does not increase dramatically for clusters with dynamical substructure. However, we find a systematic bias for all methods, such that clusters with significant substructure have higher measured masses than their relaxed counterparts. This bias depends on cluster mass: the most massive clusters are largely unaffected by the presence of significant substructure, but masses are significantly overestimated for lower mass clusters, by $\sim10\%$ at $10^{14}$ and $\geq20\%$ for $\leq10^{13.5}$. The use of cluster samples with different levels of substructure can, therefore, bias certain cosmological parameters up to a level comparable to the typical uncertainties in current cosmological studies., 14 pages, 8 figures, 3 tables. Submitted to MNRAS

This article is the second in a series in which we perform an extensive comparison of various galaxy-based cluster mass estimation techniques that utilise the positions, velocities and colours of galaxies. Our aim is to quantify the scatter, systematic bias and completeness of cluster masses derived from a diverse set of 25 galaxy-based methods using two contrasting mock galaxy catalogues based on a sophisticated halo occupation model and a semi-analytic model. Analysing 968 clusters, we find a wide range in the RMS errors in log M200c delivered by the different methods (0.18 to 1.08 dex, i.e., a factor of ~1.5 to 12), with abundance matching and richness methods providing the best results, irrespective of the input model assumptions. In addition, certain methods produce a significant number of catastrophic cases where the mass is under- or over-estimated by a factor greater than 10. Given the steeply falling high-mass end of the cluster mass function, we recommend that richness or abundance matching-based methods are used in conjunction with these methods as a sanity check for studies selecting high mass clusters. We see a stronger correlation of the recovered to input number of galaxies for both catalogues in comparison with the group/cluster mass, however, this does not guarantee that the correct member galaxies are being selected. We do not observe significantly higher scatter for either mock galaxy catalogues. Our results have implications for cosmological analyses that utilise the masses, richnesses, or abundances of clusters, which have different uncertainties when different methods are used., 25 pages, 19 figures, 7 tables. Accepted for publication in MNRAS

This paper is the first in a series in which we perform an extensive comparison of various galaxy-based cluster mass estimation techniques that utilise the positions, velocities and colours of galaxies. Our primary aim is to test the performance of these cluster mass estimation techniques on a diverse set of models that will increase in complexity. We begin by providing participating methods with data from a simple model that delivers idealised clusters, enabling us to quantify the underlying scatter intrinsic to these mass estimation techniques. The mock catalogue is based on a Halo Occupation Distribution (HOD) model that assumes spherical Navarro, Frenk and White (NFW) haloes truncated at R_200, with no substructure nor colour segregation, and with isotropic, isothermal Maxwellian velocities. We find that, above 10^14 M_solar, recovered cluster masses are correlated with the true underlying cluster mass with an intrinsic scatter of typically a factor of two. Below 10^14 M_solar, the scatter rises as the number of member galaxies drops and rapidly approaches an order of magnitude. We find that richness-based methods deliver the lowest scatter, but it is not clear whether such accuracy may simply be the result of using an over-simplistic model to populate the galaxies in their haloes. Even when given the true cluster membership, large scatter is observed for the majority non-richness-based approaches, suggesting that mass reconstruction with a low number of dynamical tracers is inherently problematic., 25 pages, 15 figures, 5 tables. Accepted for publication in MNRAS